MCA contents.docx - BSAbdur Rahman Crescent Institute of

1. Overview of the Department and the programme

The department offers Master of Computer Applications (MCA) since 1994, M.Phil(Computer Science) since 2009, Ph.D (Computer Science) since 2009, MCA (Lateral Entry)since 2014. The department motivates Staff and Students to promote Industry InstituteInteraction and R&D activities. In addition to regular curriculum the department organizestechnical seminars, symposia, workshops, industrial visits, in-plant training to expose thestudents to the real world environments and to enable them to gain practical knowledge.

The website and Internet services of the institute are maintained by the Department ofComputer Applications. Along with that the Department offers additional virtual lab classes,live classes, tutorial MOOC’s and vocational augmentation. Courses through IIT Professorsunder MHRD’s QEEE programme. Special placement opportunities on mobile developmentin Android/IOs platforms. The MCA Programme was accredited by NBA since 2001. Thedepartment has adequate infrastructure and well-equipped laboratories and it is empoweredwith a team of highly qualified faculty members.

2. Vision & Mission

Vision and Mission of the Institution

Vision

B.S. Abdur Rahman Crescent Institute of Science and Technology aspires to be a leader inEducation, Training and Research in multidisciplinary areas of importance and to play a vitalrole in the Socio – Economic progress of the country in a sustainable manner.

Mission

▪ To blossom into an internationally renowned Institute

▪ To empower the youth through quality and value – based education

▪ To promote professional leadership and entrepreneurship

▪ To achieve excellence in all its endeavors to face global challenges

▪ To provide excellent teaching and research ambience

▪ To network with global institutions of excellence, business, industry and researchorganizations

▪ To contribute to the knowledge base through scientific enquiry, applied research andinnovation

Vision and Mission of the

Department of Computer Applications

Vision

Aspires to provide quality education in the field of computer applications withstate-of-the-art computational facilities and undertake quality research in collaboration withindustries and universities to produce committed professionals and academicians to meetthe needs of the industries and society.

Mission

The Department of Computer Applications, endeavors

● To disseminate knowledge through education and training of graduates in the field ofcomputer applications.

● To focus on teaching - learning, research and consultancy to promote excellence incomputer applications.

● To foster graduates with opportunities required to explore, create and face challenges ofIT related industries.

● To equip the graduates with the necessary skills in communication, team work andleadership qualities to meet the needs of the IT related sector globally.

● To disseminate the outcome of projects and research work undertaken by thedepartment through appropriate measures for the benefit of society and industry.

3. Eligibility (Is there any difference between students graduated from Indian and

foreign universities. If yes give details)

Under graduate degree with science or applied science in Computer Applications

or Computer Science or Information Technology or other computer related areas with

minimum 50% of marks. The candidates should have studied core Mathematics either at +2

level or Mathematics/statistics as one of the subjects at degree level.

Duration of the programs:

● The duration of MCA program is of two years and the registration is valid for fourAcademic years.

● Learners who fail to complete the course within validity of the programs have tore-register paying the full course fees and completing all assignments, quizzes and theend semester exam.

Medium:

Medium of Instruction is English for all courses, assignments, presentations, webinars

and internal Assessment and Examinations.

4. PROGRAMMES OFFERED – REGULATIONS, CURRICULUM AND SYLLABI

4.1PRELIMINARY DEFINITIONS AND NOMENCLATUREIn these Regulations, unless the context otherwise requires "Programme" Means Post

Graduate Degree Programme(MCA)

i) "Course" means a theory /practical/laboratoryintegrated theory/ miniproject/ seminar

/internship/ Projectand any other subject that is normally studied in a semester

ii) "Institution" means B.S.Abdur Rahman Crescent Institute of Science & Technology.

iii) "Academic Council" means the Academic Council, which is the apex body on all

academic matters of B.S.Abdur Rahman Crescent Institute of Science & Technology.

iv) "Dean (Academic Affairs)" means Dean (Academic Affairs) of B.S. Abdur Rahman

Crescent Institute of Science & Technology who administers the academic matters.

v) "Dean(StudentAffairs)" means Dean(StudentAffairs) of B.S.Abdur Rahman Crescent

Institute of Science & Technology, who looks after the welfare and discipline of the

students.

vi) "Controller of Examinations" means the Controller of Examinations of B.S. Abdur

Rahman Crescent Institute of Science & Technology who is responsible for the conduct

of examinations and declaration of results.

vii) “Open and Distance Learning” means mode of providing flexible learning

opportunities by overcoming separation of teacher and learner using a variety of media,

including print, electronic, online and occasional interactive face-to-face meetings with

the learners or Learner Support Services to deliver teaching-learning experiences,

including practical or work experiences.

viii)“Online Learning” means mode of providing flexible learning opportunities by

overcoming separation of teacher and learner using internet, e-learning materials and

full-fledged programme delivery through internet using technology assisted mechanism

and resources.

ix) “Self-Learning e-Module for Online mode” means a modular unit of coursematerial ine-learning form which is inter alia self-explanatory, self- contained, self-directed at thelearner, and amenable to self-evaluation, and enables the learner to acquire theprescribed level of learning in a course of study and includes contents in the form of acombination of the following e-Learning content, namely: -

(a) e-Text Materials;

(b) Video Lectures;

(c) Audio-Visual interactive material;

(d) Virtual Classroom sessions;

(e) Audio Podcasts;

(f) Virtual Simulation; and

(g) Self-Assessment Quizzes or Tests;

x) “Self-Learning Material for Open and Distance Learning mode” means andincludes contents in the form of course material, whether print or in e-form, which isinter- alia self-explanatory, self-contained, self-directed at the learner, and amenable toself-evaluation, and enables the learner to acquire the prescribed level of learning in acourse of study, but does not include text-books or guide-books

4.2 PROGRAMMES OFFERED, MODE OF STUDY AND ADMISSION REQUIREMENTS

4.2.1 P.G. Programmes OfferedThe various P.G. Programmes and their modes of study are as follows:

Degree Mode of Study Pattern

MCA ODL & OL Semester

4.2.2. Mode of Study

a. Open and Distance LearningMode of providing flexible learning opportunities by overcoming separation of

teacher and learner using a variety of media, including print, electronic, online andoccasional interactive face-to-face meetings with the learners or Learner SupportServices to deliver teaching-learning experiences, including practical or workexperiences.

b. Online LearningMode of providing flexible learning opportunities by overcoming separation of

teacher and learner using internet, e-learning materials and full-fledged programmedelivery through internet using technology assisted mechanism and resources.

4.2.2 Admission Requirements

a. For Admission into ODL programme Indian students with Bachelor Degree in anydiscipline with Mathematics as one of the subjects (or) Mathematics at +2 level orB.Sc.ComputerScience / B.Sc.InformationTechnology /BCA and thoseappearing fortheir final examination (subject to passing) are eligible to apply.

b. For admission into OL programme other than Indian students stated in (2.3.1) above,foreign students are also eligible to enroll this programme.

c. Eligibility conditions for admission such as class obtained, number of attempts in thequalifying examination and physical fitness will be as prescribed by this Institution fromtime to time.

4.3 DURATION AND STRUCTURE OF THE P.G. PROGRAMMEa. The minimum and maximum period for completion of the P.G. Programmes are given

below:

ProgrammeMinimum

PeriodMaximum

PeriodMCA ODL 2 Years 4 Years

MCA OL 2 Years 4 Years

b. This programme consist of the following components as prescribed in the respectivecurriculum:

i. Core coursesii. Technology/Programme Elective coursesiii. Project work

The Programme may also include seminar / practicals / practical training, as specified inthe curriculum. The medium of instruction, examination and project report shall be inEnglish.

c. The curriculum and syllabi of the MCA ODL and OL programme shall be as per theguidelines of the UGC and AICTE and approved by the Academic Council of thisInstitute.

d. Each academic year shall normally be for one year. The end examinations will followsubsequently as per the Academic and Examination Schedule.

e. The curriculum of MCA ODL and OLprogramme shall follow the minimum prescribedcredits required for the award of the degree as specified in the AICTE guidelines for thisprogramme and the same is given below:

Programme Minimum prescribed creditsMCA ODL 86MCA OL 86

f. Delivery of Online Learning Materials (Online Learning Platform):

The Learning materials (Four Quadrant Approach, UGC online Regulation 2020)aredelivered through Crescent Learning Management System (LMS), called as onlinelearning platform. The unit wise continuous assessment (designed using bloomstaxonomy) is conducted online in the LMS on adaptive basis as per the requirement ofthecourse.

i) Quadrant-I i.e. e-Tutorial that shall contain - Video and Audio Contents, animation,

simulations, virtual labs.

ii) Quadrant-II i.e. e-Content that shall contain - Portable Document Format or e-Books or

Illustration, video demonstrations, documents and interactivesimulations, Web

Resources, that shall contain - Related Links, Open Contenton Internet, Case Studies,

Historical development of the subject, Articles, wherever required.

iii) Quadrant-III is the Discussion Forum for raising of doubts and clarifying the same on

real time basis by the course coordinator or team.

iv) Quadrant-IV i.e. Self-Assessment, that shall contain – MCQ, Problems, Quizzes,

Assignments and solutions, Discussion forum topics and setting up the FAQ,

Clarifications on general misconceptions.

Table 1: Norms for delivery of courses through Open and Distance Learning modeS.No.

CreditValueof theCourse

Size of SLMsRange (interms ofunits, to bedivided intoblocks)

No. ofAssignments

PracticalSessions

No. ofCounselingSessionsTheory (10Percent ofTotal StudyHours)

Study Hours ofLearner

1. 2Credits

6 – 10 Units 1 60 Hours 6 Hours 60 Hours

2. 4Credits


3. 6Credits


4. 8Credits


Table 2: Norms for Delivery of Courses in Online ModeS.

No.CreditValueof the

Course

No. ofWeeks

No. of Interactive Sessions Hours of StudyMaterial

Self –StudyHours

includingAssessment, etc.

TotalHours

ofStudy(basedon 30Hours

perCredit)

SynchronousOnline

Counseling/Webinars/Interactive

Live Lecturer(1 hour per

Week)

DiscussionForum/

asynchronous

Mentoring(2 hours

per week)

e-Tutorial in

hours

e-Content

hours

1. 2Credits

6Weeks

6 Hours 12 Hours 10 10 22 60

2. 4Credits

12Weeks

12 Hours 24 Hours 20 20 44 120

3. 6Credits

14Weeks

14 Hours 28 Hours 30 30 66 180

4. 8Credits

16Weeks

16 Hours 32 Hours 40 40 88 240

Table 3: ODL Programmes – Contact Sessions for Theory and Practical Courses; on an

Indicative BasisFour Courses, each of 4 Credits, with a total of 16 Credits per Semester

Number ofAssignments

10 – 12 Credits for theory and 6 -4Credits for Practical Courses

Counseling for theory onlyCourses: Four Courses of 4

Credits eachContact Sessions– Practical

ContactSessions –

theory

Four PerSemester

60 Hours ofGuided

Experiments withsupport of internalsupervisor per 2

credits

30 -36 Hours 12 Hours Per Course

Contact session up to the extent of twenty per cent., or as defined by the Commissionfrom time to time, could be arranged by providing Massive Open Online Courses and otheronline programme delivery systems.

Practical sessions to the extent of twenty per cent., or as defined by the Commissionfrom time to time, could be provided through virtual lab mode.4.4 REGISTRATION AND ENROLLMENTa. Except for the first semester, every student shall register for the ensuing semester during

a specified week before the semester end examination of the ongoing semester. Everystudent shall submit a completed registration form indicating the list of courses intendedto be enrolled during the ensuing semester. Late registration with the approval of theDean (Academic Affairs) along with a late fee will be permitted up to the last working dayof the current semester.

b. From the second year onwards, all students shall pay the prescribed fees for the year ona specific day at the beginning of the semester confirming the registered courses. Lateenrolment along with a late fee will be permitted up totwo weeks from the date ofcommencement of classes. If a student does not enroll, his/her name will be removedfrom rolls.

c. The students of first semester shall register and enroll at the time of admission by payingthe prescribed fees.

d. A student should have registered for all preceding semesters before registering or aparticular semester.

4.5 COURSE CHANGE / WITHDRAWALa. CHANGE OF A COURSE

A student can change an enrolled course within 10 working days from thecommencement of the course, with the approval of the Dean (Academic Affairs), on the

recommendation of the Head of the Department of the student.b. WITHDRAWAL FROM A COURSE

A student can withdraw from an enrolled course at any time before the firstassessment for genuine reasons, with the approval of the Dean (Academic Affairs), onthe recommendation of the Head of the Department of the student.

4.6 TEMPORARY BREAK OF STUDY FROM PROGRAMMEA student may be permitted by the Dean (Academic Affairs) to avail temporary

break of study from the programme up to a maximum of one semester for reasons of illhealth or other valid grounds. A student can avail the break of study before the start offirst assessment of the ongoing semester. However, the total duration for completion ofthe programme shall not exceed the prescribed maximum number of semesters. If anystudent is debarred for want of attendance or suspended due to any act of indiscipline, itwill not be considered as break of study. A student who has availed break of study has torejoin in the same semester only.

4.7 ATTENDANCEa. For Open and Distance Learning mode: the learner has minimum attendance of 75 per

cent. in the programme specific Personal Contact Programme (excluding counseling)and lab component of each of the programmes; and detailed attendance records havebeen maintained by Learner Support Centre/Regional Centre/ Higher EducationalInstitution;

b. For Online mode: the learner has minimum participation of 75 per cent. in all theactivities of Online programme prior to end semester examination or term endexamination.

4.8 ASSESSMENTS AND EXAMINATIONSThe weightage for different components of assessments for both Open and

Distance Learning mode and Online mode shall be as under:(i) continuous or formative assessment (in semester): Maximum 30 percent.(ii) summative assessment (end semester examination or term end examination):

Minimum 70 percent.

4.9WEIGHTAGESa. The Following shall be the weightage for different courses:

(i) Lecture Based CourseInternal assessment components : 30%Semester – end examination : 70%

(ii) Project Work

Review of Report : 30%Dissertation & Viva voce : 70%

Review of Report Dissertation & Viva voceComponent Marks Component Marks

First Review 15 Presentation 10Second Review 15 Analysis 20

Finding and Conclusion 20Viva Voce 20

Total 30 70

b. Appearing for semester end examination for each course (Theory and Practical) ismandatory and a student should secure a minimum of 40% marks in semester endexamination for the successful completion of the course.

c. The markings for all tests, tutorial, assignments (if any), laboratory work andexaminations will be on absolute basis. The final percentage of marks is calculated ineach course as per the weightages given in the clause 4.9(a).

d. For the first attempt of the arrear theory examination, the internal assessment marksscored for a course during first appearance will be used for grading along with the marksscored in the arrear examination. From the subsequent appearance onwards, fullweightage shall be assigned to the marks scored in the semester end examination andthe internal assessment marks secured during the course of study shall be ignored.

4.10 PASSING AND DECLARATION OF RESULTS AND GRADE SHEET

a. All assessments of a course will be made on absolute marks basis. The letter gradesand the corresponding grade points are as follows:

Letter grade Grade points

S 10

A 9

B 8

C 7

D 6

E 5

RA 0

“RA” Reappear for the course

b. A student is considered to have completed a course successfully if he / she secure fivegrade points or higher. A letter grade ‘RA’ in any course implies unsuccessfulperformance in that course and hence the student has to reappear for the same.

c. A course successfully completed cannot be repeated for any reason.

4.11 AWARD OF LETTER GRADEa. The grades are finalized by the respective course instructor as per clause 4.10(a). After

awarding of grades, the same shall be signed by the Head of the Department/Dean ofSchool and it shall be declared by the Controller of Examinations.

b. Within one week from the date of declaration of result, a student can apply forrevaluation of his / her semester-end theory examination answer scripts of one or morecourses, on payment of the prescribed fee, through proper application to the Controllerof Examination. Subsequently the Head of the Department/ Dean of School offered thecourse shall constitute a revaluation committee consisting of HoD as the Chairman, thefaculty member of the course and a senior member of faculty knowledgeable in thatcourse. The committee shall meet within a week to revalue the answer scripts andsubmit its report to the Controller of Examinations for consideration and decision.

c. After results are declared grade sheets shall be issued to each student, which willcontain the following details: The list of courses enrolled during the semester and thegrade scored, the Grade Point Average (GPA) for the semester and the CumulativeGrade Point Average (CGPA) of all the courses enrolled from first semester onwards.GPA is the ratio of the sum of the products of the number of credits of coursesregistered and the grade points corresponding to the grades scored in those courses,taken for all the courses, to the sum of the number of credits of all the courses in thesemester. If Ci, is the number of credits assigned for the ith course and GPi is the GradePoint in the ith course

Where n = number of courses

At the end of each mark statements in every semester until the IVth semester, the GPA

shall be displayed.The Cumulative Grade Point Average (CGPA) shall be calculated in

a similar manner, considering all the courses enrolled from first semester.The

Cumulative Grade Point Average CGPA. “RA” grade will be excluded for calculating

GPA.The formula for the conversion of CGPA to equivalent percentage of marks shall

be as follows: Percentage Equivalent of Marks = CGPA X 10. The CGPA obtained by

the candidate shall be displayed in the from the second semester mark statements until

the final semester mark statement.

d. After successful completion of the programme, the Degree will be awarded with thefollowing classifications based on CGPA.

Classification CGPAFirst Class withDistinction

8.50 and above and passing all the courses in firstappearance and completing the programme within theprescribed period of 4 Semesters

First Class 6.50 and above and completing the programme within amaximum of 4 semesters

Second Class Others

However, to be eligible for First Class with Distinction, a student should not haveobtained ‘U’ or ‘I’ grade in any course during his/her study and should have completedthe U.G. programme within a minimum period (except break of study). To be eligible forFirst Class, a student should have passed the examination in all the courses within thespecified minimum number of semesters reckoned from his/her commencement ofstudy. For this purpose, the authorized break of study will not be counted. The studentswho do not satisfy the above two conditions will be classified as second class. For thepurpose of classification, the CGPA will be rounded to two decimal places. For thepurpose of comparison of performance of students and ranking, CGPA will beconsidered up to three decimal places.

4.12 COURSE REPETITION AND ARREARS EXAMINATIONa. A student should register to re-do a core course wherein “I” or “W" grade is awarded. If

the student is awarded “I” or "W" grade in an elective course either the same electivecourse may be repeated or a new elective course may be taken.

b. A student who is awarded “U” or “AB” grade in a course shall write the semester-endexamination as arrear examination, at the end of the next semester, along with theregular examinations of next semester courses.

c. A student who is awarded “RA” grade in a course will have the option of either to writesemester end arrear examination at the end of the subsequent semesters, or to redo thecourse whenever the course is offered. Marks earned during the redo period in thecontinuous assessment for the course, will be used for grading along with the marksearned in the end-semester (re-do) examination.

4.13 SUPPLEMENTARY EXAMINATION

Final Year students can apply for supplementary examination for a maximum oftwo courses thus providing an opportunity to complete their degree programme.Likewise, students with fewer credits can also apply for supplementary examination fora maximum of two courses to enable them to earn minimum credits to move to highersemester. The students can apply for supplementary examination within three weeks ofthe declaration of results.

4.14 ELIGIBILITY FOR THE AWARD OF THE MASTER DEGREEa. A student shall be declared to be eligible for the award of MCA degree provided the

student has: i) successfully completed all the required courses specified in the programme

curriculum and earned the number of credits prescribed for the specialization, within amaximum period of 8 semesters from the date of admission, including break of study

ii) no dues to the Institution, Library, Hostels iii) no disciplinary action pending against him/her.

b. The award of the degree must have been approved by the Institute.c. Upon successful completion of 2 years, and after satisfying the items stated in 14.1, the

candidate shall be awarded as ‘MCA’ with subject to condition that it achieves as perthe norms laid down by AICTE.

d. However, the Institute/incubator shall issue certificate to student who wish todiscontinue the program after successful completion of 1st year only.

4.15 POWER TO MODIFYNotwithstanding all that has been stated above, the Academic Council has the

right to modify the above regulations from time to time.

REGULATIONS 2021CURRICULUM & SYLLABI FOR

MASTER OF COMPUTER APPLICATIONSOL & ODL

(FOUR SEMESTERS/FULL TIME)

(Candidates admitted from the academic year 2020-21 onwards)

SEMESTER 1

S. No. Course

Group

CourseCode

Course Title L T P C

1MS

MAD 6188 Mathematical Foundation forComputer Applications 3 1 0 4

2 PC CAD 6121 Computer Organization andOperating system

3 0 0 3

3 PC CAD 6122 Database ManagementSystems 3 0 0 34 PC CAD 6123 Computer Networks 3 0 0 35 PC CAD 6124 Data structures andAlgorithms using

C/C++3 0 0 3

6 ES CAD6125 Object Oriented SoftwareEngineering

3 0 0 3

7 PC CAD 6126 Data structures and AlgorithmsLaboratory using C/C++

0 0 2 1

8 PC CAD 6127 Programming in C and C++Laboratory

0 0 2 1

9 PC CAD 6128 DBMS Laboratory 0 0 2 122

SEMESTER 2

S. No. Course

Group

CourseCode


1 PC CAD 6221 Programming in Java 3 0 0 3

2MS

CAD 6222 Resource ManagementTechniques

3 1 0 4

3 PC CAD 6223 Cloud Computing 3 0 0 3

4 PC CAD 6224 Mobile Application Development 3 0 0 3

5 PC CAD 6225 Introduction to Data Science 3 0 0 36 PE Elective I 3 0 0 3

7 PC CAD 6226 Communication Skills Laboratory 0 0 2 1

8 PC CAD 6227 Advanced Technology Laboratory(Cloud/Mobile/Data Science)

0 0 2 1

9 PC CAD 6228 Programming in JAVA Laboratory 0 0 2 1

22

SEMESTER 3S. No. Course

GroupCourseCode


1 PC CAD 7121 Python Programming 3 0 0 3

2 PC CAD 7122 Block Chain Technologies 3 0 0 3

3 PC CAD 7123 Big Data Analytics 3 0 0 3

4 PC CAD 7124 Machine Learning Techniques 3 0 0 3

5 PC CAD 7125 Advanced Web Development andServices 3 0 1 4

6 PE Elective – II 3 0 0 3

7 MGT CAD 7126 Customer Relationship Management 3 0 0 3

8 PC CAD 7127 Python ProgrammingLaboratory 0 0 2 1

9 PI CAD 7128 Mini Project 0 0 2 1

24

SEMESTER 4

S.No.

CourseGroup

CourseCode


1 PI CAD 7221 Project 0 0 36 18

TOTAL CREDITS: 86

PROGRAMME ELECTIVES

S.No. CourseGroup

CourseCode


SEMESTER II

1 PE CADY 251 Digital Marketing 3 0 0 3

2PE

CADY 252Management InformationSystems 3 0 0 3

3PE

CADY 253Multimedia Systems andComputer Graphics 3 0 0 3

4 PE CADY 254 OrganizationalBehaviour

3 0 0 3

SEMESTER IIIMobile Applications

1 PE CADY351 Mobile Commerce 3 0 0 3

2 PE CADY352 Mobile Security 3 0 0 3

3 PE CADY353 Mobile and Digital Forensics 3 0 0 3Cloud Technology

4 PE CADY 354 Principles of Virtualization 3 0 0 3

5 PE CADY 355 Cloud Architectures 3 0 0 3

6 PE CADY 356 Cloud StorageInfrastructures 3 0 0 3

7 PE CADY 357 Cloud Security 3 0 0 3

8 PE CADY 358Information StorageandManagement 3 0 0 3

Web Applications and Development

9 PE CADY 359 Semantic Web 3 0 0 3

10 PE CADY 360 Content ManagementSystem 3 0 0 3

11 PE CADY 361 PHP Programming 3 0 0 3

12 PE CADY 362 Web Mining 3 0 0 3

IoT & Big data

13PE

CADY 363 Data Mining and DataWarehousing

3 0 0 3

14 PE CADY 364 Data Analytics andVisualization

3 0 0 3

15 PE CADY 365 Social Media Analytics 3 0 0 3

16 PE CADY 366 Health Care Analytics 3 0 0 3

17 PE CADY 367 R Programming 3 0 0 3

18 PE CADY 368 Decision Support System 3 0 0 3

19 PE CADY 369 Predictive Analysis 3 0 0 3

20 PE CADY 370 Internet of Things 3 0 0 3

Detail Syllabi in Annexure I

2. FEES STRUCTURE

Yet to be decided

3. ADMISSION BROCHUREYet to be decided

4. Admission process as text (explained stepwise) and in a diagram

format (A sample is attached herewith)

Yet to be decided

5. SALIENT FEATURES OF THE PROGRAMME

Centre for Online learning and Online Distance Learning, an initiative of BSA

Crescent Institute of Science and Technology aims to offer full academic

programs through Online Learning (OL) and Online Distance Learning (ODL)

mode. Online Learning and Distance Learning is an important field which

provides equal educational opportunities for those who do not have access to

traditional classroom based learning.

OL and ODL aims to reach out to all divisions of society in their quest for

knowledge, to empower and enrich the learners with global standards of

education. The Centre has a mission to provide a convenient platform for people

of all ages to pursue their dreams of higher education, to facilitate lifelong

learning, to learn in a free environment at their own pace, to provide clarity of

concepts, to build in-depth functional domain knowledge and practical application

using technology.

6. CONTACT DETAILS FOR ENQUIRY AND ADMISSION

Mr. A.Salman Ayaz,

Assistant Professor / CA

9789279856

7. Good quality pictures (Maximum 10 numbers) of the facilities of thedepartment/Lab/Events organized etc

8. FAQ's (Should clarify all possible doubts - Sample attached)

Yet to be decided

Annexure I

SEMESTER I

MAD6188 MATHEMATICAL FOUNDATIONFORCOMPUTER APPLICATIONS

SDG: 9COURSE OBJECTIVES:

COB1: Provide mathematical background on Number System andCombinatorics.

COB2: Give sufficient exposure to Propositions and Logical operations.

COB3: Deal and solve problems on Matrices.

COB4: Familiarize the concepts in Set Theory.

COB5: Explain the concepts in Graph Theory.

MODULEI NUMBERSYSTEMS,COMBINATORICS 12Decimal Number System - Binary Number System - Hexadecimal NumberSystem - Octal Number System - Permutations and Combinations -Mathematical Induction - Pigeonholeprinciple.MODULEII PROPOSITIONS AND LOGICALOPERATIONS 12Notation - Connections - Normal forms - Truth Tables - EquivalenceandImplications-Theoryofinferenceforstatementcalculus,Predicatecalculus- Rules of Logic Mathematical Induction and Quantifiers.MODULEIII MATRICES 12Matrices: Definition and Classification - Algebra of Matrices - Special Matrices -Elementary Operations of a Matrix. Determinants: Definitions & Properties -Minors and Cofactors -Operations on Determinants -Determinants: System ofLinear Equations-Characteristic Equation- Eigen values andEigenvectors.MODULEIV SETSANDRELATIONS 12Basic concepts of Sets - Set Operations and Venn Diagrams - Set IdentitiesCartesian products - Power sets - Representation and Properties of Relations.MODULEV GRAPHTHEORY 12Simple Graph, Multigraph, Weighted Graph, Paths and Circuits, Shortest Pathsin Weighted Graphs, Eulerian Paths and Circuits, Hamiltonian Paths andCircuits, Trees and Rooted Trees, Prefix Codes, Tree Traversals, SpanningTrees and Cut- Sets.

L – 45; T – 15; TOTAL HOURS –60

TEXT BOOKS:1. Judith L. Gersting, “Mathematical Structures for Computer Science”,

W.H. Freeman and Company, 7th Edition, New York, 2014.

2. Grimaldi R.P. and Ramana B.V., “Discrete and CombinatorialMathematics”, Pearson Education, 5th Edition, Harlow,2006.

3. Veerarajan.T., “Engineering Mathematics” Tata McGraw HillPublishingCo., 5th edition, New Delhi, 2012.

REFERENCES:1. Grimaldi R.P. and Ramana B.V., “Discrete and Combinatorial

Mathematics”, Pearson Education, 5th Edition, Harlow,2006.2. Trembley.J.P and Manohar R., “Discrete Mathematical Structures

with Applications to Computer Science”, Tata McGraw – HillPublishingCompany Limited, Reprint, New Delhi, 2008.

COURSE OUTCOMES:CO1: Explain the concepts in Mathematical Induction, Set Theory, Graph theory.

CO2: Demonstrate the use of Matrices in solving linear equations.

CO3: Apply the Combinatorics, Proposition, Logical Operators involvingcombinatorics problems.

CO4: Solve logical proofs in Mathematical Logics derived from truth tables.

CO5: Apply set operations and functions in solving in Set Problem.

Board of Studies (BoS):15th BoS of CA held on 09.07.2021

Academic Council:17th AC held on 15.07.2021

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

PSO3

CO1 H H M

CO2

H H M M

CO3 H H L M

CO4 H H L M

CO5 H H L M

Note: L-LowCorrelation M -MediumCorrelation H -HighCorrelation

SDG 4: Ensure inclusive and equitable quality education and promote lifelonglearning opportunities for all.The learner will be able to identify the problem of concern; then, build aquantitative mathematical model, analyse and solve it, apply the results, andpotentially create appropriate mathematical software that canbecommercialized.

CAD6121COMPUTERORGANIZATIONANDOPERATINGSYSTEM

L T P C

3 0 0 3

SDG: 9COURSE OBJECTIVES: The objective of this course is to

COB1: Introduce the instruction sets and operations of processor.

COB2: Explain the functions and services of Memory and I/O devices.

COB3: Provide an understanding of the major operating systemcomponents, services and functions.

COB4: Describe various features of processes and present both softwareand hardware solutions of the critical section problems.

COB5: Explore the techniques for managing both memory and files.

MODULEI INTRODUCTION TO COMPUTER 9ORGANIZATION

Functional Units of a Digital Computer: Von Neumann Architecture –Instruction Set Architecture (ISA): Memory Location, Address andOperation – Instruction and Instruction Sequencing – Addressing Modes-Instruction Execution – Building a Data Path – Designing a Control Unit –Hardwired Control, Micro programmed Control – Pipelining – Data Hazard– Control Hazards.MODULEII MEMORYAND I/O 9Memory Concepts and Hierarchy – Memory Management – CacheMemories:Mapping and Replacement Techniques – Virtual Memory – DMA – I/O –Accessing I/O: Parallel and Serial Interface – Interrupt I/O – InterconnectionStandards: USB, SATA.MODULEIII INTRODUCTION TO OPERATING SYSTEMS9Role of an Operating System – Types of Operating System – Major OSComponents – Operating System Operations – Operating System Services –System calls – System Programs – Operating System Structure – ProcessConcept – Process Scheduling – Operations on Processes – Inter processCommunication.MODULEIV PROCESS MANAGEMENT 9

Basic Concepts of Scheduling – Scheduling Criteria – SchedulingAlgorithms– FCFS – SJF – Round Robin -Critical Section Problem – Semaphores –Monitors – Dining Philosophers Solutions Using Monitors – Implementation ofMonitor Using Semaphores.MODULEV MEMORY ANDFILE MANAGEMENT 9Swapping–ContiguousMemoryAllocation–Paging–Segmentation–VirtualMemory–DemandPaging–Copy-on-Write–FilesystemInterface:The

ConceptofaFile,AccessMethods,DirectoryStructure,FileSystemMounting,FileSharing,Protection. FileSystemImplementation-CasestudyofLinuxandWindow operating systems.

L – 45; TOTAL HOURS – 45

TEXT BOOKS:1. William Stallings, “Computer Organization and Architecture –

Designing for Performance”, Tenth Edition, Pearson Education,2016.2. David A. Patterson, John L. Hennessy, “Computer Organization and

Design, The Hardware/Software Interface”, Fifth Edition, MorganKaufmann/Elsevier,2013.

3. AbrahamSilberschatz,PeterBgalvin,GregGagne,"OperatingSystemConcepts", 9th Edition, John Wiley & Sons Inc.,2013.

4. DeitelHM,"OperatingSystems",3rdEdition,PearsoneducationIndia,New Delhi, 2015.

REFERENCES:1. Carl Hamacher, Zvonko Vranesic, Safwat Zaky, Naraig Manjikian,

“Computer Organization and Embedded Systems”, Sixth Edition, TataMcGraw-Hill,2012.

2. Andrew S. Tanenbaum, “Modern Operating Systems”, AdisonWesley,2009.

COURSE OUTCOMES: On completion of this course, students will be able toCO1: Analyze the structure of a digital computer and demonstrate

programming proficiency using the various addressing modes and thedifferent control systems.

CO2: Analyze the performance of processors and cachesCO3: Describe the functioning of memory and operations of Input/output

Organization

CO4: Explain the basic structure and functions of operating systems

CO5:Identify the problems related to process managementand synchronization and apply learned methods to solvebasicproblems.



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SDG 9: Industry, Innovation and Infrastructure – Build resilient infrastructure,promote inclusive and sustainable, industrialization and foster innovation.The learner would be able to introduce the open source operating systemsand build the computerized ecosystem for the enterprise in a cost effectivemanner. The outcomes of the course are measurable and would enable thelearner to be productive in industrialization process with innovativecomputerization ideas.

CAD6122DATABASEMANAGEMENTSYSTEMS

L T P C

3 0 0 3


COB1: Introduce the basics on database and its operations.

COB2: Impart the fundamentals of relational database and process toformulate, analyze database queries.

COB3: Use of latest software to develop database projects and applynormalization techniques.

COB4: Educate the concept of database storage & file structure.

COB5: Comprehend ways of executing transactions in an effective andethical way.

MODULEI INTRODUCTION 9Database Systems vs. File Systems - View of Data - Data Models-DatabaseLanguages -Transaction Management - Database Systems Structure - History ofDatabase Systems - Database Systems Applications - Entity Relationship Model.

MODULEII RELATIONALDATABASES 9SQL-BasicStructure-SetOperations-ComplexQueries-JoinedQueries-DDL-Embedded SQL-Dynamic SQL-Other SQL Functions-Query by Example- Integrityand Security of Searching-Relational DatabaseDesign.MODULEIII NORMALIZATION &QUERYEVALUATION 9Normalization – Introduction - Non loss decomposition and functionaldependencies – First - Second and third normal forms – dependency preservation– Boyce - Codd normal form - Higher Normal Forms – Multi valueddependencies and Fourth normal form - Join dependencies and Fifth normal form- QueryProcessing-SelectionOperation-Sorting-JoinOperation–Views-EvaluationofExpressions-QueryOptimization.MODULEIV DATA STORAGE ANDINDEXING 9Storage & File Structure-RAID–File Organization–Organization of Records inFiles– IndexingandHashing–OrderedIndices–B+treeIndexFiles–BtreeIndexFiles– Static Hashing – DynamicHashing.MODULEV TRANSACTIONMANAGEMENT 9

Transaction Concept - Static Implementation-Concurrency Control – Protocols -Deadlock Handling-Recovery Systems-Recovery with Concurrent Transactions -Shadow Paging - Buffer Management-Case Studies-Oracle- Microsoft SQLServer- NOSQL – Characteristics - major types of NOSQL databases - NOSQLDatabase-as-a- Service for Web and mobile applications


TEXT BOOKS:1. Silberschatz, Korthand Sudarshan, “Data Base System

Concepts”, McGraw- Hill, 6th Edition,20112. RamezElmasri, Shamkant B. Navathe, “Fundamentals

of Database Systems”, Pearson, 7th Edition, 2016REFERENCE BOOKS:

1. Raghu Ramakrishnan & Johannes Gerhrke, “Data Base ManagementSystems”, McGraw Hill International 3rd Edition,2014.

2. An Introduction to Database systems, C.J. Date,A.Kannan, S.SwamiNadhan, Pearson, Eight Edition

COURSE OUTCOMES:CO1: Apply and deploy the importance of DBMS in comparison withtraditional

file system.CO2: Illustrate the working of a relational database.

CO3: Construct and normalize conceptual data models, analyze thenormalization technique and study the different views of the database.

CO4: Implement the concepts of data storage, query evaluations andoptimization

techniques.CO5: Handle transaction management queries in SQL in real time scenario

Board of Studies (BoS):15thBoS of CA held on 09.07.2021


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SDG 4: Quality Education – Ensure inclusive and equitable quality educationand promote lifelong learning opportunities for all

Database concepts taught in this course helps the student to learn about theback-end storage. Transactions and their working procedure in a real timescenario will help them to design a correct database through which efficientmanagement of data can be done. Acquiring knowledge in database will helpthe student to meet the requirement for the DBA position.

CAD 6123 COMPUTER NETWORKS L T P C

3 0 0 3

SDG: 8,9COURSE OBJECTIVES:

COB1: Provide students with enough knowledge in networking, varioustypes of networks and its applications.

COB2: Introduce the issues of data link protocols including encoding,framing, and error detection

COB3: Explain various switching and routing techniques

COB4: Provide essential knowledge about Transport layer issues

COB5: Explore the technologies of Software Defined Networking (SDN),Network Functions Virtualization (NFV)

MODULEI INTRODUCTION 9Building a network – Requirements – Network Architecture: – OSI Model –Internet Architecture – Direct Link Networks – Hardware building blocks –Framing – Error detection – Reliable transmission.

MODULEII NETWORK FUNDAMENTALS 9LAN Technology – LAN Architecture – BUS/Tree – Ring – Star – Ethernet –Token Rings – Wireless Technologies : Examples , Types of connections ,Media and latest technologies.

MODULEIII NETWORK LAYE 9Packet Switching – Switching and Forwarding – Bridges and LAN switches –Internetworking – Simple Internetworking – Routing: Types of Routing,Internet routing and protocols.MODULEIV TRANSPORT LAYER 9Reliable Byte Stream (TCP) – Simple Demultiplexer (UDP) – TCP CongestionControl – Congestion Avoidance Mechanisms.MODULEV PRESENTATION LAYER and APPLICATIONS 9Presentationformatting–Datacompression–CryptographicAlgorithms:RSA- DES –– Applications – Domain Name Service – Email - SMTP – MIME –HTTP – SNMP-Introduction to Software DefinedNetworking(SDN) andNetwork Functions Virtualization(NFV)- SDN Fundamentals


TEXT BOOKS:Larry L. Peterson, Bruce S. Davie, “Computer Networks: A SystemsApproach”, Morgan Kaufmann Publishers, Fifth Edition,2011

REFERENCES:

1. Erik Dahl man, Stefan Parkville, Johan Skold, “5G NR: The NextGeneration Wireless Access Technology, Academic Press, 09-Aug2018

2. James F. Kurose and Keith W. Ross, “Computer Networking - A TopDown Approach featuring the Internet”, Addison Wesley PublishingCompany, 4th Edition,2007

3. William Stallings, “Data and Computer Communications”, PHI, 7th

Edition,20114. Andrew S. Tanenbaum, “Computer Networks”, Tata Mcgraw Hill,5th

Edition, 2013.COURSE OUTCOMES:CO1: Identify and describe the layers of the OSI and TCP/IP.

CO2: List the applications of wireless network technologies

CO3: Make effective use of networking topologies.

CO4: Identify the requirements for different network architecture.

CO5: Summarize the features of an emerging paradigm software definednetworking (SDN) in computer networking



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SDG 8: Decent Work and Economic Growth – Promote sustained, inclusiveandsustainableeconomicgrowth,fullandproductiveemploymentanddecentwork forallSDG 9: Industry, Innovation and Infrastructure – Build resilient infrastructure,promote inclusive and sustainable industrialization and foster innovation

Building the next generation of ICT infrastructure will power the evolution ofsmart, sustainable cities and communities worldwide. Making modern ICTsmore widely available will foster the local innovation needed to spur domesticeconomic growth, provide decent work and reduce inequalities.

CAD6124 DATASTRUCTURESANDALGORITHMSUSING C/C++

L T P C

3 0 0 3


COB1: Study the importance of data structures in context of writing

efficient programs.COB2: Explore the different types of searching and sorting algorithms.

COB3: Explain basic data structures such as arrays, linked lists, stacksand queues.

COB4: Introduce various algorithmic techniques to solve the problems.

COB5: Demonstrate the appropriate data structure and algorithm designmethod for a specified application.

MODULEI INTRODUCTION TO DATASTRUCTURES 9Introduction to data structures, Classifications: Primitive and non-primitive,Dynamic memory allocation, Accessing the address of a variable, Declaringand initializing pointers, Memory allocation functions: malloc(), calloc(), free()and realloc().Stack- Operations on stack: Infix, Prefix and Postfix notations-Conversion from Infix to postfix. Queue- Types of queue - Operations onQueue.MODULEII LINKED LIST ANDITSOPERATIONS 9Components of linked list, Representation of linked list, Advantages andDisadvantagesoflinkedlist.Typesoflinkedlist:Singlylinkedlist,doublylinked list,Circular linked list, Operations on singly linked list: Creation, Insertion,Deletion, Search andDisplay.MODULEIII SEARCHINGANDSORTING 9Searching-LinearSearchMethods-BinarySearchMethods,Sorting-Bubble Sort,Selection Sort, Insertion Sort, Quick Sort, MergeSort.MODULEIV TREEANDGRAPH 9Tree - Binary tree, Complete binary tree, Binary search tree, Heap Treeterminology: Array representation of tree, Creation of binary tree. Traversal ofBinaryTree:Preorder,In orderandPostorder.Graphs,Definition-Breadth-first

Traversal-Shortest - path algorithms– Minimum Spanning tree– Prim's andKruskal's algorithms–Depth-firsttraversal.MODULEV ALGORITHM ANALYSISANDDESIGN 9Algorithm design techniques: Greedy algorithms, Divide and conquer,Dynamic programming, Backtracking, Branch and bound, Introduction toalgorithm analysis: Asymptotic notations, Asymptotic Notations and its

properties – Mathematical analysis for Recursive algorithm and Non-recursivealgorithms. Time and space complexity of an algorithm.


TEXT BOOKS:1. TanenbaumA.S,LangramY,AugesteinM.J,“DataStructuresusingC”

Pearson Education,2004.2. Lipschutz: Schaum’s outline series Data structures TataMcGraw-Hill,

1st edition, July 2017REFERENCES:

1. Robert Kruse, Data Structures and program designing using ‘C’, 3rd

edition,2. Hanumanthappa M., Practical approach to Data Structures,Laxmi

Publications, Fire Wall media 2006.COURSE OUTCOMES:CO1: Describe how arrays, records, stack, queues are represented in

memory.CO2: Compare and contrast various sorting and searching techniques.

CO3: Evaluate algorithms and data structures in terms of time and memorycomplexity of basic operations.

CO4: Demonstrate different methods for traversing trees.

CO5 : Apply suitable shortest path algorithm in appropriate applications



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SDG 9: Industry, Innovation and Infrastructure – Build resilient Infrastructure,promote inclusive and sustainable industrialization and foster innovation

Design and development skills taught in this course for the learners withrespect to the course outcomes are measurable. The learner can able toemphasize the importance of data structures in developing and implementingefficient algorithms.

CAD6125 OBJECTORIENTEDSOFTWAREENGINEERING

L T P C

3 0 0 3

SDG: 9

COURSE OBJECTIVES:

COB1: Provide basic concepts of software engineering and software lifecycle models.

COB2: Explore the techniques for requirement gathering design andspecification.

COB3: Give an insight into the concepts of modeling and notations ofthe different UML diagrams.

COB4: Explain the strategies behind designing a project and trackprogress.

COB5: Provide knowledge on software configuration management.

MODULEI INTRODUCTION TOSOFTWAREENGINEERING 9Software engineering concepts - Software engineering development activities- Software life cycle models- Standards for developing life cyclemodels- Modeling withUML.MODULEII REQUIREMENT, PLANNING&SCHEDULING 9Introduction - Overview of requirements elicitation - Requirement elicitationconcepts-Requirementelicitationactivities-Managingrequirementelicitation- Software Requirements Specification - Software project planning –Scope - Resources - Software Estimation - Empirical Estimation Models –Planning – Risk Management - Software Project Scheduling - ObjectOriented Estimation &Scheduling.MODULEIII ANALYSIS 9UML:AnalysisModelling-DataModelling-FunctionalModelling&InformationFlow - Behavioural Modelling-Structured Analysis - Object Oriented Analysis -Domain Analysis-Object Oriented Analysis process - Object RelationshipModel - Object Behaviour Model. Design modelling withUML.MODULEIV OBJECT ORIENTED DESIGNANDINTERFACE 9Overview of Object Oriented Design-Design Concepts& Principles-Design

Process Modular Design - Design Effective Modularity - Reuse concepts-Reuse Activities Managing Reuse-Overview of interfaceSpecification-Interface specification concepts- Interface specificationactivities- Managing object design.MODULEV MPLEMENTATION AND TESTING 9

SOFTWARECONFIGURATIONMANAGEMENTOverview of mapping- Mapping models to Code- Mapping Object Model toDatabase Schema- Overview of testing- Testing concepts- Testing activities -

Managing testing. Managing and controlling Changes- Managing andcontrolling versions- Types of maintenance- Maintenance log and defectreports- Reverse and re-engineering.


TEXT BOOKS:1. Roger. S. Pressman and Bruce R. Maxim, “Software Engineering – A

Practitioner’s Approach”, McGraw Hill, seventh Edition,20152. Ian Sommerville, “Software Engineering”, Pearson Education, eighth

edition, New Delhi, 2011.REFERENCES:

1. Timothy C. Lethbridge, Robert Laganiere, "Object-Oriented SoftwareEngineering - A practical software development using UML and Java",Tata McGraw-Hill, 3rd Edition,2006.

2. S.K.Kataria, Rajiv Chopra, "Object Oriented Software Engineering",3rd Edition,2013.

3. Stephan R. Schach, "Object oriented and classical softwareengineering", Tata McGraw Hill, 8th Edition,2010.

4. Bernd Bruegge, "Object oriented software engineering", 3rd Edition,Pearson Education,2009

COURSE OUTCOMES:CO1: Compare the different software life cycle models and select the

appropriate model for a real time projectCO2: Identify the software requirement specification and formulate project

planning in real time scenario.CO3: Analyze different UML concepts and illustrate the UML design for real-

time project.CO4: Execute the object-oriented and software reusability concepts.CO5: Implement and test software configuration management techniques in

software engineering environment.Board of Studies (BoS):15th BoS of CA held on 09.07.2021


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SDG 9: Industry, Innovation and Infrastructure – Build resilient infrastructure,promote inclusive and sustainable industrialization and foster innovationObject Oriented Software Engineering concepts taught in this course for thelearners with respect to the course outcomes are measurable Helps thelearner to apply standard Software Engineering practices and strategies inreal-time software project development. As a future industrial managementpersonnel, the learner would be able to work in teams to build softwaresystems, comprehend and write effective reports and design documentation.

CAD6126 DATASTRUCTURESALGORITHMS

USING C/C++LABORATORY

L T P C

0 0 2 1


COB1: Explain various sorting and searching algorithms.

COB2: Explore linear and nonlinear data structures

COB3: Design and implement algorithms for searching and sorting

COB4: Design and implement operations on stacks, queues, and linkedlists

COB5: Introduce the Binary Search Tree implementation using.

PRACTICALSList of Experiments:

1. Write a C program to create a Stack and do the following operationsusing arrays and linked lists (i) Push(ii)Pop

2. Create a Queue and do the following operations using arrays andlinkedlistsi) Add (ii)Remove

3. Write a C program to implement doubly linkedlist4. WriteaCprogramtosortalistofNelementsofintegertypeusingquick

sortAlgorithm5. Write a C program to sort a list of N elements using Bubble Sort

Technique6. Write a C program to search for an element in an array using Binary

search7. WriteaC++programtoimplementinsertionsortmethodtosortagiven list of

integers in descendingorder.8. WriteaC++programtoimplementselectionsortmethodtosortagiven list of

integers in descendingorder.9. Write a C++ program to Create a binary search tree and do the

followingtraversalsi) In-order (ii) Pre order (iii) Postorder.

10. Perform the following operations in a given graph (i)Depth firstsearch(ii) Breadth first search

11. Find the shortest path in a given graph using Dijkstraalgorithm.12. Apply the divide and Conquer technique to arrange a set ofnumbers

13. Construct optimal binary search trees using dynamic programmingmethod of problem solving.

14. Implement knapsack problem usingbacktracking

15. Find the solution of traveling salesperson problem using branch andbound Technique.

P – 30; TOTAL HOURS – 30TEXT BOOKS:

1. TanenbaumA.S,LangramY,AugesteinM.J,“DataStructuresusingC”Pearson Education,2004.

2. Lipschutz: Schaum’s outline series Data structures TataMcGraw-Hill,1st edition,July 2017

REFERENCES:1. Robert Kruse, Data Structures and program designing using ‘C’, 3rd

edition,2. Hanumanthappa M., Practical approach to Data Structures,Laxmi

Publications, Fire Wall media 2006.COURSE OUTCOMES:CO1: Apply various data structure such as stacks, queues, trees, linked listand graphs to solve various computing problems.CO2: Choose and implement efficient data structures and apply them to solveproblems.CO3: Implement and analyze various searching techniques and sortingtechniques.CO4: Write programs that use arrays, records, linked structures, stacks,queues, trees, and graphs.CO5: Develop program that implements kruskal’s algorithm, prims, binarysearch, all types of sorting, greedy algorithm and backtracking technique.Board of Studies (BoS):15th BoS of CA held on 09.07.2021


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SDG 9: Build resilient Infrastructure, promote inclusive and sustainableindustrialization and foster innovation

Design and development skills taught in this course for the learners withrespect to the course outcomes are measurable. The learner can able toemphasize the importance of data structures in developing andimplementing efficient algorithms.

CAD6127PROGRAMMINGIN CANDC++LABORATORY

L T P C

0 0 2 1


COB1: Describe the basic concepts of C structure.

COB2: Provide knowledge on functions, pointers, structures, and strings

COB3: Explain the necessity of Object-Oriented Programming overTraditional programming.

COB4: Illustrate concepts of Object-Oriented programming language

COB5: Demonstrate the use of file programming.


C programs1. Programs using, I/O statements andexpressions.2. Programs using decision-making constructs.3. Programsusingloopingstatements(alsodemonstratetheuseofbreak and

continuestatements).4. Programs using single dimensional and multi-dimensionalarrays.5. Programs using string handling functions.6. Programs using functions and recursivefunctions.7. Demonstrate the use of structure andUnion.8. Demonstrate the use ofPointers.9. Demonstrate the use of filehandling.

C++ Programs

1. Simple Programs using Data Types, Input/output statements andArithmetic Operators, Conditional statements and differentloops.

2. Programs using structures andfunctions.3. Programs using classes, objects and scope resolutionoperator.4. Programs using Constructors anddestructors.5. Demonstration of array ofobject.6. Demonstration usingthis->pointer.7. Application Programs using Simple, Multiple, Multilevel, Hierarchical

and HybridInheritance.8. Demonstration of Virtual function, Friend function and Staticfunction.9. Programs to implement functionoverloading.

10. Programs using operator overloading for Binary, Unary and relationaloperators.

11. Demonstration of pointers to base class and derived class memberfunctions.

12. Programs using Function and Classtemplate.13. Program to access a record using filehandling.

P – 30; TOTAL HOURS – 30

TEXT BOOKS:1. Yashavant P. Kanetkar, “Let Us C”, BPB Publications; Seventeenth

edition, September 2020.2. Kunal Pimparkhede, "Computer Programming withC++", Cambridge

Institution Press; First edition, January 2017.REFERENCES:

1. G Balagurusamy, “Object-Oriented Programming with C++ | 8thEdition”,McGrawHill;Eighthedition(24September2020);McGrawHillEducation (India) Private Limited, September2020.

2. Yashavant Kanetkar, "Let Us C++", BPB Publications,16 September2020.

3. Herbert Schildt, "C++: The Complete Reference, 4th Edition",McGrawHill Education; 4th edition, July2017.

4. StanleyLippman,"C++Primer”,Addison-Wesley;5thedition,August2012.

COURSE OUTCOMES:CO1: Develop C programs for simple applications making use of basicconstructs, arrays, strings.CO2: Develop C programs involving functions, recursion, pointers, andstructures.CO3: Develop C++ programs using Class, Objects, array of object, functionoverloading, operator overloading.CO4: Develop C++ programs using the concepts of Object-OrientedProgramming features.CO5: Design applications using sequential and random-access file processing.



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SDG 9: Develop durable Infrastructure, promote inclusive and balancedindustrialization and adoptive innovationStrategy and development skills taught in this course for the beginners withrespecttothecourseeffectsaremeasurableandusefulinimprovingtheability ofthe learner. As the future industrial management staffs, the learner wouldmakechoiceswiththehelpofcomputationalintelligence-basedassessmentsupport systems.

CAD 6128 DBMS LABORATORY L T P C

0 0 2 1


COB1: Learn how to create tables which are fundamental storageblocks of data.

COB2: Learn how to place constraints on data that is entered on tablesto ensure data integrity.

COB3: Learn how to add, change and remove data from tables.

COB4: Learn how to select a subset of the data you want to seefromthe collection of tables and data.

COB5: Learn how to combine table and group multiple rows of data intable.


1. Execute a single line and group functions foractable.● UPPER function converts a string to upper case.● LOWER function converts a string to lower case.● MONTHS_BETWEEN function returns the count of months

between the two dates.● NEXT_DAY function returns the next day of the date specified.● LAST_DAY function returns last day of the month of the inputdate.

2. Execute DCL and TCLCommands.● GRANT-REVOKE-● COMMIT–ROLLBACK–SAVEPOINT-SETTRANSACTION.

3. Create and manipulate various DB objects for atable.● Table – This object is used to create a table indatabase.● This object is used to create a view indatabase.

4. Create views, partitions and locks for a particularDB.5. Write PL/SQL procedure for an application using exceptionhandling.6. Write PL/SQL procedure for an application usingcursors.7. Write a DBMS program to prepare reports for an applicationusing functions.8. Write a PL/SQL block for transaction operations of a typicalapplication usingtriggers.

9. Write a PL/SQL block for transaction operations of a typicalapplication

using package.10. Design and develop an application using any front end and back endtool (make use of ER diagram and DFD)


TEXT BOOKS:1. Silberschatz, Korth and Sudarshan, “Data Base SystemConcepts”,

McGraw- Hill, 6th Edition, 2011.2. Ramez Elmasri, Shamkant B. Navathe, “Fundamentals ofDatabase

Systems”, Pearson,7th Edition,2016.REFERENCES:

1. Raghu Ramakrishnan & Johannes Gerhrke, “Data Base ManagementSystems”, McGraw Hill International 3rd Edition,2014.

2. An Introduction to Database systems, C.J. Date, A. Kannan,S.SwamiNadhan, Pearson, Eight Edition,2016.

COURSE OUTCOMES:CO1: Apply iterative programming at database level

CO2: Write programming blocks with conditional structure, assignmentstructure, loop structure et

CO3: Use exception Handling, Transaction oriented programs, Storedprocedures, functions, packages, etc.

CO4: Implement cursors which would allow row wise access of data

CO5: Use triggers which would allow you define pre and post actions whensomething change in the database tables



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SDG 9: Industry, Innovation and Infrastructure – Build resilient infrastructure,promote inclusive and sustainable industrialization and foster innovation.Database related programming plans, concepts, & features are taught in thiscourse for the learners with respect to the course outcomes are measurableand useful in improving the query based programming and logical skill of thelearner. As the software industries growing rapidly, this course will enablethelearnertoexplorevarioustechnologiessuchasOracleMySQl,MicrosoftSQLServer, DB2, MongoDB, and NoSQL.

SEMESTER II

CAD 6221 PROGRAMMING IN JAVA L T P C

3 0 0 3


COB1: Provide basic understanding of Java fundamentals

COB2: Explore inheritance, interfaces and packages.

COB3: Explain Java programs to perform multi-threading and exceptionHandling

COB4: Familiarize the programming skills to use the object-orientedprogramming methodology to produce quality computer basedsolutions to real time problems.

COB5: Introduce collection of AWT packages and develop programs.

MODULEI JAVA FUNDAMENTALS 9Java features – Java Platform – Java Fundamentals – Expressions, Operators,and Control Structures – Arrays –Constructor.MODULEII INHERITANCE AND INTERFACES 9The Java Class- Inheritance, Derived Classes, Method Over-riding, MethodOverloading, Access Modifiers, Abstract Class and Method, Interfaces,Packages, Imports and Class Path.

MODULEIII THREADING AND EXCEPTION HANDLING 9Threads: Introduction, Creating Threads in Applications-Thread Priority-ExceptionHandling-Try-CatchStatement,catchingmorethanoneException,Generating Exceptions.MODULEIV APPLETS AND AWT PACKAGES 9Create an Applet, Applets Life Cycle, and AWT package – Layouts–Containers – Event Package – Event Model – Painting– LanguagePackages.MODULEV STREAM CLASSES AND I/OPACKAGES 9Input Stream Classes, Output Stream Classes, File Class. Utility Packages –Input Output Packages – Inner Classes – Java Database Connectivity -Servlets - RMI –Java Beans.


TEXT BOOKS:1. HerbertSchildt,“JavaTheCompleteReference“,Tata McGrawHill,

th11 Edition, 2020.

2. Hortsmann & Cornell, “Core Java Advance Features VOL II”,Pearson

Education, 9thEdition, 2013.

REFERENCE BOOKS:1. Keyur shah, “Gateway to Java Programmer Sun Certification”,

TataMcGraw Hill,2005.th

2. Deitel &Deitel, Java How to Program, Prentice Hall 11 Edition2018.COURSE OUTCOMES:CO1: Write java programs using control structures, arrays and constructors.

CO2: Identify classes, objects, members of a class and the relationshipsamong them needed for a specific problem.

CO3: Compare and contrast the interfaces and abstract classes

CO4: Handle the exceptions effectively and illustrate the life cycle of thread.

CO5: Create solutions for real time problems using AWT packages, servletsand java beans



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SDG 9: Industry, Innovation and Infrastructure – Build resilient Infrastructure,promote inclusive and sustainable Industrialization and foster innovationByunderstandingtheobject-orientedfeaturesofJava,thestudentswillbeableto applythe knowledge to derive solutions to computing problems. Apply object-orientedprinciplesinsoftwaredesignprocess,thestudentswillbeabletoanalyzecomplex problems in the domain of software development with bettereffectiveness. Java programming helps in finding solutions to the realtimeapplications.

CAD6222RESOURCEMANAGEMENTTECHNIQUES

L T P C

3 1 0 4


COB1: Explain mathematical model of linear programming problems.

COB2: Illustrate mathematical model of Transportation problems.

COB3: Familiarize the mathematical model of Assignment problems.

COB4: Explore network modeling for planning and scheduling the projectactivities.

COB5: Demonstrate Queuing Models to minimize waiting time in thequeue.

MODULEI LINEAR PROGRAMMINGMODELS 12Mathematical Formulation - Graphical Solution of linear programming models –Simplex method – Artificial variable Techniques-Variants.MODULEII TRANSPORTATION ANDASSIGNMENTMODELS 12Mathematical formulation of transportation problem- Methods for finding initialbasic feasible solution – Optimum solution - Degeneracy – Mathematicalformulation of assignment models – Hungarian AlgorithmMODULEIII INTEGERPROGRAMMINGMODELS 12Formulation – Gomory’s IPP method – Gomory’s mixed integer method –Branch and bound technique.MODULEIV PROJECT SCHEDULING BY PERTAND CPM 12Network Construction – Critical Path Method – Project Evaluation and ReviewTechnique – Resource Analysis in Network Scheduling.MODULEV QUEUINGMODELS 12Characteristics of Queuing Models–Poisson Queues-(M/M/1) :(FIFO/∞/∞), (M /M / 1): (FIFO / N / ∞), (M / M / C): (FIFO / ∞ / ∞), (M / M / C): (FIFO/N / 8)models.

L – 45; T – 15; TOTAL HOURS – 60

TEXT BOOKS:1. Taha H.A., Institution of Arkansas “Operations Research:

An Introduction, global edition, Pearson Education, 10th Edition,2017.REFERENCES:

1. A.M. Natarajan, P. Balasubramani, A. Tamilarasi, “OperationsResearch”, Pearson Education, Asia,2014.

2. Gross, D. and Harris, C.M., “Fundamentals of Queueing Theory”, WileyStudent, New Jersy, 3rd Edition2012.

3. N. D Vohra, Quantitative Techniques in Management, Tata Mcgraw Hill,2010

COURSE OUTCOMES:CO1: Formulate and apply linear, integer programming to solve operationalproblems taking into accounts of social and economic constraints with ethicalvalues.

CO2: Solve transportation and assignment models to find optimal solution inwarehousing, travelling problems in industries like automobile.CO3: Prepare project scheduling using PERT and CPM.

CO4:Identifyandanalyzeappropriatequeuingmodeltoreducethewaitingtimeinqueue.

CO5: Solve optimization concepts in real world problems.



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SDG 9: Build resilient Infrastructure, promote inclusive and sustainableindustrialization and foster innovationThe learner will be able to identify the problem of concern; then, build aquantitative mathematical model, analyse and solve it, apply the results, andpotentially create appropriate mathematical software that can becommercialized.

CAD 6223 CLOUD COMPUTING L T P C

3 0 0 3


COB1: Explain the basic concept of cloud computing.

COB2: Explore about various cloud services provided by differentservice providers.

COB3: Illustrate the virtualization concepts in cloud environment.

COB4: Expose various ways to deploy the cloud services in online.

COB5: Learn about the different online tools available in the cloudenvironment.

MODULE I INTRODUCTION 9Introduction to Cloud Computing: Cloud Computing in a Nutshell – Roots ofCloud Computing – Layers and Types of Cloud Computing – CloudInfrastructure Management – Migration to Cloud Environment: Approaches –The Seven Step Model for MigrationMODULE II CLOUD EVOLUTION AND SERVICES 9Evolution of Cloud Computing: Hardware Evolution – Internet SoftwareEvolution – Server Virtualization – Web Services Delivered from the Cloud:Communication as a Service (CaaS) – Infrastructure as a Service (IaaS) –Platform as a Service (PaaS) – Software as a Service (SaaS) - CloudDeployment Models: private – public – hybrid – Discovering Cloud ServicesDevelopment Services and Tools–AmazonEc2–Google App Engine–IBMClouds.MODULE III VIRTUALIZATION 9Level of Virtualization – Virtualization Structure / Tools and Mechanism –VirtualizationofCPU–Memory–I/ODevices–VirtualClustersandResourceManagement – Virtualization for Data Centre AutomationMODULE IV APPLICATIONS USING CLOUDSERVICES 9Application - Calendars, Schedules and Task Management – ExploringOnline Scheduling - Applications – Exploring Online Planning and TaskManagement – Event Management – Contact Management– ProjectManagement –Databases – Storing and Sharing Files.MODULE V COLLABORATION 9Collaborating via Web-Based Communication Tools – Evaluating Web MailServices – Evaluating Web Conference Tools – Collaborating via SocialNetworks and Groupware – Collaborating via Blogs and Wikis – Case Study


TEXT BOOKS:1. Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Cloud

Computing Principles Books and Paradigms, Wiley,20102. Rittinghouse, John W.,and James F. Ransome, “CloudComputing:

Implementation, Management and Securityǁ,”CRC Press, 2017.REFERENCES:

1. Kumar Saurabh, “Cloud Computing – Insights into New EraInfrastructure”, Wiley Indian Edition,2011.

2. HaleyBeard, Cloud Computing Best Practices forManaging and Measuring Processes for On- demand Computing,Applications and Data Centres in the Cloud with SLAs, Emereo PtyLimited, July2008.

3. KaiHwang,GeoffreyC.Fox,JackG.Dongarra,"DistributedandCloudComputing, From Parallel Processing to the Internet of Things",MorganKaufmann Publishers,2012.

4. Michael Miller, Cloud Computing: Web-Based ApplicationsThatChange the Way You Work and Collaborate Online, Que Publishing,August 2008.

COURSE OUTCOMES:CO1: Acquire knowledge on the fundamentals of cloud computing.

CO2: Identify and implement the architecture and various services offered bycloud computing.

CO3: Analyze and implement on different types software’s used in virtualizationenvironment.CO4: Apply the knowledge acquired to integrate the cloud based technologiesin real time scenario.CO5: Explore and implement the insights of online cloud based tools indeveloping software project applications.Board of Studies (BoS):15th BoS of CA held on 09.07.2021


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SDG 4: Quality Education – Ensure inclusive and equitable quality educationand promote lifelong learning opportunities for allThe basics of cloud computing and its techniques were taught in this course.Understandingtheinsightsofcloudcomputingandvirtualizationwillmotivate thestudent to deploy cloud technology in needed real-time scenarios. Theapplicationsofcloudcomputingwillimprovetheskillsetofthestudenttomeetthe IT sector demand.

CAD6224MOBILEAPPLICATIONDEVELOPMENT

L T P C

3 0 0 3


COB1:Giveanoverviewondifferentmobiledevelopmentenvironment.

COB2: Gain basic understanding of Android application development.

COB3: Impart knowledge on how to build an Android application

COB4: Understand the IOS developmentenvironment

COB5: Provide knowledge on how to build an iOS application

MODULEI INTRODUCTION TOMOBILEAPPLICATION 9DEVELOPMENT

Introduction to mobile Applications-Differences between mobileapplications and desktop Applications-App store, Google Play, WindowsStore-Hybrid Mobile App Development-Phone GAP-Ionic Framework.MODULEII ANDROIDFRAMEWORK 9IntroductiontoAndroid-BriefHistory-FeaturesofAndroid-TheAndroidPlatform- Android SDK - Anatomy of an Android Application-Creating Android VirtualDevices-Manifest file - Activity - Services-Content Provider-BroadcastReceiver-Intents - SQLite DatabasesMODULEIII USER INTERFACEDESIGN 9Android User Interface Design Elements-Views: Button, Text Field, RadioButton, Toggle Button, Checkbox, Spinner -View Groups-Android LayoutManagers-- List View- Grid View-Table View- Web View- Adapters-Menus,ActionBars,Notifications:Status,ToastsandDialogs,StylesThemes-Drawingand Working with Animation Android Media API: Playing audio/video, Media

recording. Sensors - Maps &LocationMODULEIV IOSDEVELOPMENTFUNDAMENTALS 9iOSBasics-iOSArchitecture-IntegratedDevelopmentTools–IntroductiontoXCode, Swift - Frame work and Libraries - Project templates - Resource &ApplicationSettings-Views&Controls-Debugging&Running-BuildingBlockApproach - Application Life cycle - MVC – Pattern –ViewMODULEV ADVANCED CONCEPTSINIOS 9Data Management - Core Data - Application Storage - External Storage -Memory Management - Leaks and Allocations - UI Design - Design Tools -InterfaceBuilders-Storyboard-ViewControllers-Drawingmodel–Windows-EventHandling-ViewDataSourceanddelegates-MultimediaandNetworks- Library - Location Services - Google Maps - Apple Push and LocalNotifications - Accelerometer.


TEXT BOOKS:1. Dawn Griffiths &David Griffiehts, Head First Android Development,

O’Reilly Publication, SecondEdition,2017.2. AhmadSahar,ProgrammingforBeginners,IOS13, PacktPublishing,

Fourth Edition, 2020.REFERENCES:

1. Reto Meier,Professional Android 4 Application Development, WroxPublications, ThirdEdition,2012.

2. David Mark, Jack Nutting, Jeff Lamarche andFrederic OlssonBeginning iOS 6 Development: Exploring the iOS SDK, Apress, 2013.

COURSE OUTCOMES:CO1: Describe the requirements for mobile applications

CO2: Explain the architecture and building blocks of Android

CO3: Develop and design mobile applications using Android for specificrequirements

CO4: Explain the architecture and building blocks of iOS

CO5 :Develop and design mobile applications using iOS for specificrequirements



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SDG 9: Industry, Innovation and Infrastructure – Build resilient Infrastructure,promote inclusive and sustainable industrialization and foster innovationThe skills taught in this course for the learners with respect to the courseoutcomes are measurable and useful in improving the programming skill ofthelearner.Bylearningthissubject,thelearnercandevelopinnovativemobileapplications which can solve several problems of the user.

CAD6225INTRODUCTIONTODATASCIENCE

L T P C

3 0 0 3


COB1: Explain fundamentals of data science and statistical modeling

techniques.COB2: Describe proficiency with statistical analysis of data.

COB3: Demonstrate on mathematical tools for data science.

COB4: Familiarize on machine learning algorithms for predictivemodeling.

COB5: Expose to different data visualization tools and techniques.

MODULEI INTRODUCTION 9Introduction: Data Science - Big Data and Data Science hype –Datafication -Current landscape of perspectives - Skill sets needed. Statistical Inference -Populationsandsamples-Statisticalmodelling,probabilitydistributions,fitting a

model.MODULEII EXPLORATOTYDATAANALYSIS 9Exploratory Data Analysis - Getting and Cleaning Data Statistical Inferences -Summarizing and Visualizing the DataMODULEIII MATHS FORDATASCIENCE 9Mathematical Tools for Data Science - Statistics Inferences and Probability -Linear AlgebraMODULEIV MACHINE LEARNING 9Machine Learning in Data Science Supervised, unsupervised, reinforcementand deep learning, Naive Bayesian Algorithm, K means, K nearestNeighborhood algorithms.MODULEV DATAVISUALIZATION 9Data Visualization - Basic principles, ideas and tools for data visualization.Examples of inspiring (industry) projects. Creation of own visualization of acomplex dataset. Data Science and Ethical Issues - Discussions on privacy,security, ethics.


TEXT BOOKS:1. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk

from The Frontline. O’Reilly.2014.2. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of

Massive Datasets. v2.1, Cambridge Institution Press. 2014. (freeonline)

REFERENCES:1. Kevin P. Murphy. Machine Learning: A Probabilistic Perspective.ISBN

0262018020. 2013.2. Foster Provost and Tom Fawcett. Data Science for Business: What

You Need to Know about Data Mining and Data-analytic Thinking.ISBN 1449361323.2013.

3. Trevor Hastie, Robert Tibshirani and Jerome Friedman. Elements ofStatistical Learning, Second Edition. ISBN 0387952845. 2009.(freeonline)

COURSE OUTCOMES:CO1: Describe the Data Science Process and how its components interact.

CO2: Explain the significance of exploratory data analysis (EDA) in datascience.

CO3: Apply basic tools (plots, graphs, summary statistics) to carry out EDA.

CO4: Analyze the different basic machine learning algorithms (LinearRegression, k-Nearest Neighbors (k-NN), k-means, Naive Bayes) forpredictive modeling.

CO5: Create effective visualization of given data (to communicate orpersuade).Board of Studies (BoS):15th BoS of CA held on 09.07.2021


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SDG 4: Quality Education – Ensure inclusive and equitable quality educationand promote lifelong learning opportunities for allComplete understanding of the Data Science process applying the variousmachine learning algorithms and deriving comparative study with exposure toEDA tools.

CAD6226COMMUNICATIONSKILLSLABORATORY

L T P C

0 0 2 1


COB1: To enhance the ability of students in Learning, Speaking,Reading, Writing (LSRW) skills.

COB2: To develop their speaking skills to interact efficiently in real lifesituations and in workplace

COB3: To impart listening and reading techniques for bettercommunication

COB4: To improve the writing skills of students through reports,letterset.

MODULEI FUNDAMENTALSOF LANGUAGE 3Tenses, Subject – Verb Agreement, Correction of Errors.MODULEII ORALCOMMUNICATION 15Introducing oneself, Conversations, Role-play - Activities based on real lifesituations and professional situations such as marketing, advertising, etc.Debating on a topic, Group Discussion, Oral Presentation, Non-verbalcommunication, Mock Interviews, Phonetics- Correct Pronunciation.MODULEIII WRITTEN COMMUNICATION 6Writing a letter of application with résumé - calling for quotations – placing anorder – letter of complaint, Memoranda, Writing an email, Report Writing -Project report.MODULEIV LISTENINGAND READING 6

Language fundamental practices - Listening Comprehension, ReadingComprehension,listeningtocorrectpronunciation,Accent,ViewingmodelsofPresentations,Interviews.


REFERENCES:1. “A Textbook of English and communication skills” by

RichaMishra, Ratna Rao1 January2019.2. Andrea J. Rutherford, “Basic Communication Skills for Technology”,

second edition, Pearson Education,2007.3. P.K.Dutt, G. Rajeevan and C.L.N. Prakash, “A Course in

Communication Skills”, Cambridge Institution Press, India2007.4. Krishna Mohan and Meera Banerjee, “Developing Communication

Skills”, Macmillan India Ltd. (reprinted2007).

https://www.amazon.in/s/ref%3Ddp_byline_sr_book_1?ie=UTF8&field-author=Richa%2BMishra&search-alias=stripbooks

https://www.amazon.in/s/ref%3Ddp_byline_sr_book_2?ie=UTF8&field-author=Ratna%2BRao&search-alias=stripbooks

5. Riordan, Pauley, “Report Writing Today”, AIT B.S. Publisher, New Delhi(2000).

COURSE OUTCOMES:CO1: Demonstrate the efficacy of their reading and listening skills.

CO2: Speak fluently on various topics and participate effectively in debatesand discussions.

CO3: Write professional documents like reports, letters and proposalsefficiently.CO4: Communicate clearly using appropriate vocabulary andgrammatically

correct expressions.Board of Studies (BoS):15th BoS of CA held on 09.07.2021


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SDG 4: Quality Education: - Building a Communication Skills for Discussionon various sectors as an Industrialist, Corporate works, and Academician.Students will develop knowledge, skills, and judgment around humancommunication that facilitate their ability to work collaboratively with others.Such skills could include communication competencies such as managingconflict, understanding small group processes, activelistening,appropriate self-disclosure.

CAD6227ADVANCEDTECHNOLOGYLABORATORY

(Cloud/Mobile/DataScience)

L T P C

0 0 2 1


COB1: Explain the fundamental concepts of cloud computing.

COB2:Exploretheservicesandsecurityconceptsincloudenvironment

COB3:Describe the components and structure of a

mobile

development frameworks (Android SDK and Eclipse AndroidDevelopment Tools (ADT)) and learn how and when to applythe different components to develop a working system.

COB4: Demonstrate the basic concepts of Reprogramming

COB5: Illustrate Data Science applications using Reprogramming

PRACTICALSLIST OF EXPERIMENTS:

CLOUD COMPUTING:

1. Create NFS & VMFS Data store in the v-Sphere WebClient.2. Implementation of Load Balancing in AWS.3. Manage Hosts on a v-Sphere Distributed Switch in the v-Sphere Web

Client.4. Study and implementation of Infrastructure aseservice.5. Study and implementation of Storage as aService.

6. Study and implementation of Cloud Securitymanagement.

MOBILE APPLICATION DEVELOPMENT:1. Develop an application that uses GUI components, Fonts andcolors.2. Develop an application that uses layout managers and eventlisteners.3. Develop a native calculatorapplication.4. Develop an application that draws basic graphical primitives on the

screen.5. Develop an application that creates an alarmclock.

DATA SCIENCE USING R PROGRAMMING:1. Programs using basic datatypes2. Programs using Arrays.3. Programs usingMatrix.4. Programs usingVector

5. Programs using Functions.6. Programs using Dataframe.7. Programs using List andFactors.8. Programs using loops.9. Programs using Plots andtabulation.


TEXT BOOKS:1. Ritting house, John W., and James F. Ransome, ―Cloud Computing:

Implementation, Management and Securityǁ, CRC Press,2017.2. IOS 13 Programming for Beginners-Fourth Edition, Ahmad Sahar,

Packt Publisheing-20203. CathyO’NeilandRachelSchutt.DoingDataScience,StraightTalk

fromthe Frontline. O’Reilly. 2014.REFERENCES:

1. KaiHwang,GeoffreyC.Fox,JackG.Dongarra,"DistributedandCloudComputing, From Parallel Processing to the Internet of Things",Morgan Kaufmann Publishers,2012.

2. Dawn Griffiths &David Griffiehts, Head First Android Development –Second Edition,2017-O’ReillyPublication

3. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman, Mining ofMassive Datasets. v2.1, Cambridge Institution Press. 2014.(freeonline)

COURSE OUTCOMES:CO1: Implement the cloud services in real-time scenario.CO2: Deploy cloud-computing technologies to analyze the security

management in real time projects.CO3: Develop and deploy mobile applications for the Android operating

system using basic and advanced phone features.CO4: Implement basic R programming concepts.CO5: Analyze and plot graph for various data science applications.Board of Studies (BoS):15th BoS of CA held on 09.07.2021


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SDG 9: Build resilient Infrastructure, promote inclusive and sustainableindustrialization and foster innovation.The clear understanding of cloud, data science and mobile appdevelopment,leads to new innovations and build better technology leading to sustainableindustrialization

CAD6228PROGRAMMINGINJAVALABORATORY

L T P C

0 0 2 1


COB1: Explain object-oriented programming techniques.

COB2: Provide quality-based software solutions to real problems.

COB3: Familiarize the advance features of java technology.

COB4: Demonstrate the use of application programming interface (api)and develop programs.

COB5: Illustrate multithreaded programs with exception handlingmechanism.


1. Program to implement various looping structures andarrays.2. Program to illustrate the use of overloading andoverriding.3. Program to implement the concept ofinheritance.4. Program to illustrate the use ofmulti-threading.5. Program to implement the concept of Interfaces andpackages.6. Generate the program using exceptions handlingmechanism.7. Implement the fileoperations.8. Implement i/o streamclasses9. Program using Applets.10. ProgramtohandleMouseEvents,KeyboardEventsandworkwithGUI

Components.11. Program usingJDBC.


TEXT BOOKS:1. Herbert Schildt, The Complete Reference – Java 2, 7thEdition, Tata

McGraw Hill, 2017.REFERENCES:

1. Deitel & Deitel, Java How to Program, Prentice Hall 9TH Edition 2011.

COURSE OUTCOMES:CO1: Apply basic control structures, arrays, looping statement and variousclass libraries in developing program.CO2: Write java programs using object-oriented programming techniquesinheritance, polymorphism, interface, constructors and abstract class.CO3: Create package for real time applications like bank transaction,employee processing etc.

CO4: Construct multithreaded programs and handle exceptions.

CO5: Develop programs using Applets.



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SDG 9: Build resilient Infrastructure, promote inclusive and sustainableindustrialization and foster innovationHelps in Learning the basic programming concepts. It helps in increasing thestudent’s skill and helps them to get placed.

SEMSTERIII

CAD7121 PYTHONPROGRAMMING L T P C

3 0 0 3SDG:4

COURSEOBJECTIVES:

COB1: Applyvariousdata typesandcontrolstructures.COB2:Facilitatecodereusabilityandexploreobject-orientedfeatures.

COB3:Learnhowtouseindexingandslicingtoaccessdata.

COB4:ManipulateandpreprocessthedatausingPandas.

COB5:Visualizethedatain agraph,chartorother visualformat.

MODULEI FUNDAMENTALSOFPYTHON 9FeaturesofPython–Datatypes:Numbers,Strings&itsoperations,Boolean–Operators–List&itsoperations,Tuples&itsoperations,Dictionaries&itsoperations–Arrays–InputandOutput–Conditionsstatements:if,if-else,if-elif-else– Looping statements:while,for

MODULEII MODULARIZATIONANDOOPsCONCEPT 9Functions:Withandwithoutargument,withandwithoutreturn,recursivefunction,Datefunction,Mathfunction,Lambda–Errorhandling–ClassesandObjects–Inheritance–Polymorphism–ExceptionHandling.

MODULEIII INTRODUCTIONTONUMPY 9NumPyarrayattributes–Arrayindexing–Arrayslicing–ComputationonNumpyArrays –Aggregations–Sortingarrays.MODULEIV FILEHANDLING&DATAMANIPULATIONUSING

PANDAS9

File Handling: Files I/O - Printing to the Screen - Reading Keyboard Input-Opening and Closing Files- Reading and WritingFiles - RenamingandDeletingFiles-Directoriesin Python –Exceptions -ExceptClause.DataManipulationusingPandas:IntroductiontoJupyter–PandasBasics(DataFrame), Pandas Series and Index Objects – Position / Label baseddataindexingandselection.

MODULEV DATAPREPROCESSING&VISUALIZATION 9Data Work Flow & Importing Data – Data Cleaning: Handling of inconsistentdata – Detection of missing values – Removing & Replacingmissing values –Duplicate Data Handling – Detection of Outliers. GeneralMatplotlib Tips –CustomizationofPlots–LinePlots–Histogram–Barcharts andPieCharts–ScatterPlots.

L– 45;TOTALHOURS–45

TEXTBOOKS:

1. KennethA.Lambert,TheFundamentalsofPython:FirstPrograms,CengageLearning, 2nd2018.(ISBN:9781337560092)

2. DustyPhillips,PythonObjectOrientedProgramming,PACKTPress,4thEdition,2021.(ISBN: 9781801077262)

3. JakeVanderPlas,PythonDataScienceHandbook:Essentialtoolsforworkingwithdata,O’ReillyMedia,CA,2016.

REFERENCES:1. Mark Lutz,ProgrammingPython,O'ReillyMedia,5thEdition, 2013.2. TonyGaddis,StartingOutwithPython,Pearson,5thEdition,2021.(ISBN:97

80136679110)3. Downey,AllenB,ThinkPython:HowtoThinkLikeaComputerScientist,O’R

eilly,2nd Edition,2016.4. DavidM.Baezly,PythonCookbook,O’ReillyMedia, 3rdEdition,2013.

COURSEOUTCOMES:CO1:Demonstratetheuseof built-indatastructureslist,tupleanddictionary.

CO2:Implement objectorientedconcepts.

CO3:Processandanalyzethedatausing NumPy.

CO4:HandlemissingdataandworkwithcombiningdatasetsusingPandas.

CO5:Understandtheinsightof thedatasetusingvisualization.

Board ofStudies(BoS):15thBoSofCAheldon22.06.2021

AcademicCouncil:17thACheldon14.07.2021

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Note:L-LowCorrelation M-MediumCorrelation H-HighCorrelation

SDG4:QualityEducation–Ensureinclusiveandequitablequalityeducationandpromotelifelonglearningopportunitiesfor allThe language constructs as well as its object-oriented approach aim tohelpstudentstowriteclear,logicalcodeforsmallandlarge-scaleprojects.Studentshave a much higher chance of finding a solution to any problem.Python, isthe go-to technology for scientific computing. MultipleStudiesunequivocallyhailPythonasthemostpopularlanguageformachinelearningand datascience.

CAD7122 BLOCKCHAINTECHNOLOGIES

L T P C

SDG:9

3 0 0 3

COURSEOBJECTIVES:

COB1: LearnthebasicconceptsofBlockchainTechnologies.

COB2:ImpartknowledgeaboutBlockchainGeneral Architecture.

COB3:Learntheinventorymanagementconceptsforoptimizingsupplychainperformance.

COB4:Integrateblockchaintechnologieswithsupplychains.

COB5: ApplytheBlockchainconceptsindifferentusecases.

MODULEI INTRODUCTION 9Basicsofblockchain,History,UsesofBlockchain,Structureofablock,Transactions,PublicLedger,blockchainworking,accumulationofblocks,prosandconsofblockchain,tiersofblockchaintechnology,featuresofblockchain.Typesofblockchain:DistributedLedger,PublicBlockchains,PrivateBlockchains, Semiprivate Blockchains,Side chains, PermissionedLedger,SharedLedger,FullyPrivateandProprietaryBlockchains,TokenizedBlockchains,TokenlessBlockchains.MODULEII BLOCKCHAINARCHITECTURE 9Designmethodologyforblockchainapplications,blockchainapplicationtemplates, blockchain application development, Ethereum, Solidity,Businessproblems.Decentralizedapplications-Dapps,implementingDapps,EthereumDapps, casestudies relatedtoDapps.MODULEIII MANAGINGINVENTORYINSUPPLYCHAIN 9Definition,Concept,SignificanceandFunctionsofOperationsandSCM.ValueinSupply Chain- quality, delivery, flexibility, Source management inSupplyChain- in sourcing, outsourcing, Make Vs Buy, Managing Inventory inSupplychain-definitionofinventories,RoleofInventory,Inventorycontroltechniques(ABCAnalysis, VEDAnalysis).MODULEIV BLOCKCHAININTEGRATIONWITHSUPPLY

CHAINS9

Supply Chain Management & Blockchain Integration Overview, SupplyChainManagement Traditional Architecture, Supply Chain ManagementBlockchainArchitecture,BlockchainDeploymentStages,Usecase-FoodIndustryArchitecture,IdentitiesandPolicies,MembershipandAccessControl,Channels,TransactionValidation,writingsmartcontractusingHyperledgerFabric.

MODULEV CASESTUDIES 9Manufacturingandproduction,supplychainmanagement,logisticsandtransportation,Internetofthings,e-voting,healthcare,productlifecycle,knowledgeandinnovationmanagement,newbusinessmodelsandapplications,Casestudies:Decentralizedfleettrackingsystem,supplychainandlogistics,RealWorldCase Study(IBM/Wal-Mart and VeChain).

L–45;TOTAL HOURS –45

TEXTBOOKS:1. BahgaA.,MadisettiV.,Blockchainapplications:ahands-onapproach,VPT,

2017.2. MelanieSwan,“Blockchain:BlueprintforaNewEconomy”,O’Reilly,

2015.REFERENCES:

1. VikramDhillon,DavidMetcalfandMaxHooper,“BlockchainenabledApplications”,Apress,2017,

2. B.Mahadevan,OperationsManagementTheory&Practice,Pearson,3rdedition, 2015.

COURSEOUTCOMES:CO1:Identifythestakeholdersoftheselectsupplychainforblockchainintegration.CO2:Designtherequirementengineeringmetricsforthesystemtointegrateblockchaintechnologies.CO3:Selecttheappropriatecommodityorspecificproductsupplychainwithstartnodeand endnodefortheeffective inventorymovement.CO4:WritetheSMARTcontractusingHyperLedger.

CO5:Evaluateandmanagethesupplychainwithblockchainintegration.

Board ofStudies(BoS) :15thBoSofCAheldon22.06.2021


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SDG9:Industry, InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThe cognitive learning objectives, socio-emotional learning objectivesandbehavioral learning objectives are achieved in the course outcomes asthelearner would be able to select the commodity/product supply chain,identifythe stakeholders and the explanatory variables of the system,evaluate thesupplychainperformanceandoptimizeitinnovativelywithblockchainintegration.

CAD7123 L T P CBIGDATAANALYTICS

SDG:9 3 0 0 3COURSEOBJECTIVES:

COB1:Understandthefundamentalconceptsofbigdataand analytics.

COB2:Gaintheknowledgeaboutbigdatastorage,processing,visualization,andapplicationproblemsinrealtimeworldscenario.

COB3:Tosetupsingleand multi-nodeHadoopClusters.

COB4:Tosolvea bigdataproblemusingMapReducetechnique.

COB5:Learnhowtohandlethelargevolumeofdataincloudenvironment.

MODULEI INTRODUCTION 9OverviewofBigDataandItsImportance-SourceofBigData-FourV’sofBigData -Types of the Data and Its Applications - Role of Distributed SysteminBigData-ComplexityofData&DataAnalysis-BigDataUseCases-DataModel- Structures,OperationsandConstraints-DataDiscovery.MODULEII BIGDATAARCHITECTURE 9IntroductiontoBigDataIntegrationandProcessing-TraditionalDataIntegration -Transforming Data for Processing- Data Fusion - Big DataAnalyticalTools-In-MemoryComputingTechnologyforBigData –PredictiveAnalytics- DataIntelligence-DataSerialization- DataMonitoring&Indexing.MODULEIII HADOOPECOSYSTEM 9Overview of Big Data Frameworks - Apache Hadoop - History andMilestoneof Hadoop - Core Components of Hadoop - Hadoop Architecture -HadoopEcosystem - Distinguishing Hadoop Daemons and Its Features -Overview ofHDFS-HDFS Architecture -MapReduceinHadoop-HadoopSingle& Multi-NodeCluster–OverviewofApacheSpark.MODULEIV CLOUD SERVICES FOR BIG DATA

STORAGE9

Overview ofBigDataStorage-DataStoragesinCloudEnvironment–CloudBasedStorageServices–AWS&MicrosoftAzure-AzureDataLakeAnalytics-AzureDataFactory–AWSBigDataStorage&CollectionServices-ETLTechniques-TraditionalETL –Benefits of ETLinBigDataAnalytics.MODULEV CASESTUDY 9

OverviewofRealTimeBigDataAnalytics-Real-TimeArchitecture-CharacteristicsofReal-TimeSystem-ChallengesofReal-TimeSystem-DataStreamAnalyticsPlatforms-BigDataAnalyticApplications-Social-Media,Health-Care,Agriculture,EducationSectors&E-Commerce.

L–45;TOTAL HOURS –45

TEXTBOOKS:1. MichaelMinelli,MicheheChambers,“BigData,BigAnalytics:EmergingBu

sinessIntelligenceandAnalyticTrendsforToday’sBusiness”,1stEdition,AmbigaDhiraj,WielyCIOSeries,2013.

2. Dietrich,D.Datascienceandbigdataanalytics:discovering,analyzing,visualizingand presentingdata.JohnWiley&Sons,2015.

3. BuyyaRajkumar,RodrigoN.Calheiros,andAmirVahidDastjerdi.“Bigdata:principlesandparadigms”.MorganKaufmann,2016.

REFERENCES:1. ArvindSathi,“BigDataAnalytics:DisruptiveTechnologiesforChangingthe

Game”,1stEdition,IBMCorporation,2012.2. TomWhite,“Hadoop:TheDefinitiveGuide”,3rdEdition,O’reilly,2012.3. M.Bernard.Bigdatainpractice:how45successfulcompaniesusedbigdata

analyticstodeliverextraordinaryresults.John Wiley&Sons,2016.

COURSEOUTCOMES:CO1:In-depthunderstandingoftheconceptsandintricaciesofbigdataanalyticsCO2:CategorizeandSummarizeBig Dataanditsimportance

CO3:LearnNoSQLdatabasesandmanagementsystem

CO4:UnderstandthedatastorageincloudenvironmentlikeMicrosoft Azure&AWS

CO5:Gainknowledgeaboutrealworldapplicationsof bigdataanalytics.

Board ofStudies(BoS) :15thBoSofCAheldon22.06.2021


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SDG9:Industry,Innovation&Infrastructure-Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovation.Designinganddevelopingskillstaughtinthiscoursewithrespecttothecourseoutcomes improve the analytical knowledge and innovation of the learner.Itwouldcreateavarietyofwaysforthelearnertoprogressandcanhelpsignificantlyimprovethequalityof the learner.

CAD7124 MACHINELEARNINGTECHNIQUES L T P C

SDG:4 3 0 0 3

COURSEOBJECTIVES:

COB1:Understandthebasicconceptsof Machinelearning

COB2:ApplySupervisedMachineLearningTechniquesfordatahandling.

COB3:UnderstandthefeaturesofNeuralnetworkanditsapplications

COB4:CreateUnsupervisedLearningmodelsforhandlingunknownpatterns

COB5: Learntheconcepts of AdvancedandReinforcementLearning

MODULEI INTRODUCTIONTOMACHINELEARNING 9Introduction to Machine learning - Machine Learning types- Types of data-Exploringstructureofdata-DataqualityandRemediation-DataPre-processing–ModelSelection-TrainingandtestingtheModel–Modelrepresentation.IntroductiontoFeatureEngineering:FeatureTransformation&SubsetSelection.OverviewofProbability:Discrete–Continuous–Probabilitydistribution.MODULEII SUPERVISEDLEARNING 9Classification:ClassificationandRegressionTrees(CART)-K-NearestNeighbors-Supportvectormachines.Bayestheorem-NaïveBayes-Bayesianbeliefnetwork.Regression:LinearRegression,MultipleLinearRegression,LogisticRegression.MODULEIII NEURALNETWORKLEARNING 9Multilayerperceptron:Introduction-Perceptron-Training-Backpropagationalgorithm-Trainingprocedures-Tuningthenetworksize;Competitive learning:Adaptive resonance theory - Self-Organizingmaps,RadialBasisFunctions-LearningVectorQuantization-HebbianLearning,ApplicationofNeuralnetwork–Facerecognition.MODULEIV UNSUPERVISEDLEARNING 9IntroductiontoClustering-Partitioningmethod:K-means-K-medoids;HierarchicalClustering-Spectral Clustering, Association RuleLearning-Apriorialgorithm-ExpectationMaximization-Dimensionalityreduction-Principalcomponentsanalysis(PCA).

MODULEV ADVANCEDLEARNING 9

ReinforcementLearning–RepresentationLearning-ActiveLearning-EnsembleLearning-RandomForest-BootstrapAggregation-Boosting-GradientBoostingMachines –Deeplearning.

L–45;TOTALHOURS –45

TEXTBOOKS:

1. EthemAlpaydin,"IntroductiontoMachineLearning”,MITPress,PrenticeHall ofIndia,Third Edition 2014.

2. SaikatDutt,SubramanianChandramouli,AmitKumarDas,“MachineLearning”,Pearson, 4thimpression, 2019,PearsonPublications.

3. Kevin P. Murphy "Machine Learning: A Probabilistic Perspective",TheMITPress,2012.

4. TomMitchell,“MachineLearning”,McGrawHill,3rdEdition,1997.

REFERENCES:1. Charu C. Aggarwal, “Data Classification Algorithms and

Applications”,CRCPress,2014.2. Jiawei Han and Micheline Kambers and Jian Pei, “Data

Mining–ConceptsandTechniques”,3rdedition,MorganKaufmanPublications,2012.

3. TrevorHastie,RobertTibshirani,JeromeFriedman,“TheElementsofStatisticalLearning”,SecondEdition,Springer,2008.

COURSEOUTCOMES:CO1:IdentifyandapplytheappropriatetechniquestoprocessthedataandsolvetheapplicationsusingmachinelearningtechniquesCO2:Gainin-depthfamiliaritywithvarioussupervisedlearningalgorithms

CO3:ImplementmachinelearningthroughNeuralnetworks.

CO4:ApplytheUnsupervisedlearningtechniquesinreallifeproblems.

CO5:Developskillsbyusingadvancedmachinelearningtechniquesforsolvingpracticalproblems.

BoardofStudies(BoS):15thBoSofCAheldon22.06.2021


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SDG4:QualityEducation-Ensureinclusiveandequitablequalityeducationandpromotelifelonglearning opportunitiesforall.Course Outcomes have achieved the Curricula need and Learnerwouldacquirecomplexproblemsolvingskillswithcriticalthinkingability.Thelearnerwillabletouseallopportunitiesandtoapplytheacquiredknowledgeineverydaysituations topromotesustainabledevelopment

CAD7125 ADVANCEDWEBDEVELOPMENTAND

SDG:9

COURSEOBJECTIVES:

SERVICES

COB1:AppraisetheopportunitiesandchallengesofInternetrelatedenvironment.

COB2:DesignStaticClientwebsiteusingmarkuplanguagesandstylesheets.COB3: Analyze theadvantagesanduseofAjax.

COB4: FamiliarizeAPI ConnectiontoThirdpartyvendors.

COB5:ApplythePHPFrameworkandconnectwithMySQLdatabase.

MODULEI INTRODUCTION TOWWW 9Introduction to Network, Internet and Intranet, Internet Addressing – IP,DNS,URL.ElementsofWeb–WebPage,WebSite,WebClient&Server.Introduction to Web Languages and Framework – HTML/DHTML, JavaScript,PHP,XML.MODULEII BUILDINGWEBSITESUSINGHTML5 AND 9

CSSHTML5Tags–HTMLNewElements-EventAttributes–HTML5:Googlemaps,GEOLocation-HTMLCanvasTag-Audio,Video.IntroductiontoStylesheet,TypesofStylesheet,conceptofclass&ID,DifferentCSSProperty-Background Property- Fontproperty- Text –Dimensions -Borders-Margins-Padding-BoxModel.CSS3-BoxModelBackground-TextEffects.MODULEIII ADVANCED CLIENT SIDE SCRIPTING 9

LANGUAGEConcept and types of Scripting language, Introduction to Web Applications-Pre and Post Ajax, Ajax in the Real World, Alternatives to Ajax, XML InANutshell, Syntax, Rules, JavaScript In A Nutshell, Primitive Data TypesandReference Types, Variables Loops, Function Definition and FunctionCall,Objects, Expressions, Operators and Escape Sequences, DocumentObjectModel(DOM),WindowObject.MODULEIV SERVERSIDESCRIPTINGLANGUAGE 9Introduction to PHP, Basic PHP Syntax: PHP tags, PHP statementsandwhitespace,comments,Operators,ConditionalStructure,UserDefineFunctions,Arrays.GETandPOSTMethods.Cookies,Session.IntroductiontoGithub,APIconnectionwith thirdpartyvendors.MODULEV DATABASE AND ADVANCED PHP 9

FRAMEWORK

PHP MyAdmin - Performing basic database operation (DML) (Insert,Delete,Update,Select)-Settingqueryparameter-Join(Crossjoins,Innerjoins,OuterJoins,Selfjoins.).Introductiontocodeigniter-UnderstandingtheMVCPatternModels-ConfigurationCodeIgnitertoworkwithdatabase-Realtimecasestudy-Wordpress,DomainRegistrationandhosting.


TEXTBOOKS:1. DevelopingWebApplication,WileyIndiaPublication,RalphMoseley,Wiley

India,2013.2. BeginningPHP5,Apache,MysqlWebDevelopment,Wrox,Elizabeth

Naramore, MichaelK.Glass,2005.REFERENCES:

1. BeginningJavaScript2ndEdition,Wrox, NicholasC.Zakas,2004.2. WebEnabledCommercialApplicationDevelopmentUsingHTML,DHTML

,PERL,JavaScript, BPBPublications,IvanBayross, 2005.3. BeginningAjax,Wrox,ChrisUllman,LucindaDykes, 2007.4. BeginningJavaScript 2ndEdition,Wrox,Nicholas C.Zakas,2004.5. https://codeigniter.com/6. ForfreehostingandCpanelvisit :https://in.000webhost.com/

COURSEOUTCOMES:CO1:Demonstratetheknowledgeoffundamentalelementwebandwebsiteandsummarizetheimportanceofweblanguagesinthedevelopmentofwebsite.

CO2:ApplyAjax,JavaScript,HTMLandCSS3effectivelytocreateinteractiveanddynamicwebsites.CO3:BuildwebapplicationsusingPHPandsubmittheformusingGETorPOSTmethod.CO4:DeterminenumerousopportunitiesexistforAPIpractitionersseekingconnectionwithThirdpartyvendors.CO5:DevelopWebApplicationusingCodeignitorandabletoconnectandmanipulatetheMySQLdatabase.Board ofStudies(BoS):15thBoSofCAheldon 22.06.2021


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Note: L-LowCorrelation M-MediumCorrelation H-HighCorrelation

SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfoster innovationTo analyze, design and develop Advance Web development skills taughtinthiscourseforthelearnerswithrespecttothecourseoutcomesaremeasurableandimplementable.LearnerswillDesigntheresponsivewebsites,WebappsandtobecomeaWeb-AppDeveloperthroughinnovativeapproach.

CAD7126 CUSTOMERRELATIONSHIPMANAGEMENT

L T P C

SDG:9 3 0 0 3

COURSEOBJECTIVES:

COB1:IntroducethebasicconceptsofCustomerRelationshipManagement

COB2:LearnthedifferentCRMModelsandtheartofmakingCRMStrategy.

COB3:Understandthepresentandchangingpatternsofe-CRMSolutions.

COB4:SelecttheappropriateCRMsoftwaretoolandcustomizetheoperations

COB5:ApplyCRMinvariousbusinessverticalstoprovidebusinessintelligence

MODULEI INTRODUCTION 9Evolution of Customer Relationship: CRM-Definition - Emergence ofCRMPractice - Factors responsible for CRM growth - CRM process -frameworkofCRM-BenefitsofCRM,TypesofCRM,ScopeofCRM,CustomerProfitability,FeaturesTrendsinCRM,CRMandCostBenefitAnalysis.MODULEII CRMROADMAPFORBUSINESS

APPLICATIONS9

Elements of CRM – CRM Process – Strategies for Customer acquisition–Retention and Prevention of defection – Models of CRM – CRM road mapforbusinessapplications-StrategicCRMplanningprocess–Implementationissues.MODULEIII e-CRMSOLUTIONS 9CRM-IssuesandStrategies-WinningMarketsthroughEffectiveCRM-CRMasabusinessstrategy-EffectiveCRMthroughCustomerKnowledgeManagement -Customer Interaction Management - Call CentremanagementinCRM.CustomerCentricityinCRM-CustomerlifecycleManagement.Componentsof e-CRM-Changing Patterns of e-CRMSolutions.MODULEIV SOFTWARETOOLSFORCRM 9SalesForceAutomation:Salesprocess–ActivityContact-LeadandKnowledgemanagement -SalesforeCRMtool&ZohoCRMtool-CRMLinksinE-Business-E-Commerce.MODULEV CASESTUDIES 9ImplementingCRMatBankingsectors–MicrosoftCRMsolutions-CRMinB2CMarket:Telecom–Airlines –Banking –Hospitality–CRMinInsurance


TEXTBOOKS:

1. JagdishNSheth,ParvatiyarAtul,GShainesh,CustomerRelationshipManagement:EmergingConcepts,ToolsandApplications,1stEdition,TataMcGrawHill,June 2017.

2. G.Shainesh andJagdishN.Sheth,“CustomerRelationshipManagement:AStrategicperspective”,LaxmiPublications;FirsteditionJanuary2016.

3. V. Kumar, Werner Reinartz, “Customer RelationshipManagementConcept,StrategyandTools”,3rdEdition,SpringerTextsinBusinessandEconomics,2018.

REFERENCES:1. Makkar,U.andMakkar,H.K.,CustomerRelationshipManagement,TataMc

Graw-Hill Education,2012.2. AlokKumar,ChhabiSinha,RakeshSharma,“CustomerRelationship

Management:Conceptsandapplications”,DreamtechPress,2007.COURSEOUTCOMES:CO1:IdentifytherightCRMframework forthebusinessvertical.

CO2:SelecttherightCRMstrategyandmodelfortheproposedsystem.

CO3:Integratecustomerknowledgeandinteractionmanagement.

CO4:SelecttheappropriatesoftwaretoolandcustomizeitsoperationstoimplementtheproposedCRMmodel.

CO5:Derivebusinessintelligenceandinsightsfromtheverticalcasestudies

Board ofStudies(BoS) :15thBoSofCA heldon22-06-2021


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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThecourseoutcomesaremeasurableandhelpthelearnertoimplementCRMsolutionmethodologies toachievetheSustainabledevelopmentgoalonIndustry,Innovation and infrastructure. The proposed CRM solution by thelearnerwould improve the customer retention capacity of the system.Theinnovativeapplicationofe-CRMssalesforceautomationandcostbenefitanalysisbythelearnerwouldalsoimprovethebusinessprofitability.

CAD7127PYTHONPROGRAMMINGLABORATORY

L T P C

0 0 2 1

SDG: 4

COURSEOBJECTIVES:

Studentwillbeableto

COB1: Understandpythonbasicoperationusingvarious functionsofPython

COB2:IdentifytheconceptsofStringsandFilesinPython.

COB3:ComprehendtheLists,DictionariesandTupleConceptsinPython.COB4:Acquiretheskillstobuildgaminglogicsinpythonenvironment.

COB5: Explore the features of PANDAS library used inpythonprogramming

PRACTICALSListofExperiments:

1. Writeapythonprogramtoimplementthearithmeticoperationsforthefollowing:

● Addition● Subtraction● Multiplication● Modulus● FloorDivision

2. Write a python program to implement the conditional statement forthefollowing:

● Fibonaccinumberseries.

● IncorporateFIZZforanynumberdivisibleby3andBuzzforanynumb

er divisible for 5 and FIZZBUZZ for any number divisibleby3and 5aswell.

3. Write a python program to implement the crowd computing usingthearrayconceptforthefollowingscenario.

● To collect approximate cost for a material or object andstorethe same in the array. Remove first and last 10 % of thelistedcost from the array and compute the mean value of thearrayitems.

4. Write a python program to implement loopingconcept,conditionalstatementandfunctiontobuild agamecalledjumbledword.

5. Write a python program to implement random module torandomlygenerate 50 birth dates and find how many of them havesame day oftheyear.

6. ListPrograms (PythonLists&itsFunctionality)● DisplayofListwithelements.● FindingtherangeoftheLists.● IndexingintheLists(IncludingNegativeIndexing).● UseofLoopintheLists.● Adding,removing andJoining twoLists

7. TuplePrograms (PythonTuple&itsFunctionality)● CreationofTuplewithvalues.● FindingtherangeoftheTuple.● IndexingintheTuple(Including NegativeIndexing).● UseofLoopintheTuple.● Adding,removingandJoining TwoTuple

8. DictionaryPrograms (PythonDictionary&itsFunctionality)● Displayof unorderedelements.● Accessingtheelements inthedictionary.● Useof LoopintheDictionary.● Adding,removing andJoiningtwoDictionary

9. Writeapythonprogramtoconvertspeechtotext.10. Writeapythonprogramtocreateagame“MONTEHALL_3-DOORSAND A

TWIST”. This comprises of three doors. In which two doorscontainGOAT and one door contain BMW. User has to pick his/herchoice ofdoor. If the choice of door contains BMW, then userWINSotherwiseLOST.

11. Write a python program to implement visualization concept to plotthevalues ina chartwith x-axisandy-axis.

12. Write a python program using pandas library to perform thefollowingoperation.

● CreateDataFrame● Manipulatethevalues inDataFrame● Barcharts● Pie Charts● Scatter Plots

P–30;TOTALHOURS–30

TEXTBOOKS:1.Python:The Complete ReferencebyMartin CBrown-20March

2018.

REFERENCES:1.PythonProgrammingforBeginners:AnIntroductiontothePython

Computer Language and Computer Programming (Python, Python3,PythonTutorial)byJasson Cannon,2014.

COURSEOUTCOMES:CO1:Learnerscanapplytheacquiredskilltocomputefundamentalsand

mathematicalconceptsinpythonenvironment.CO2:CanIdentify,formulate,analyzeandimplementtechnicalskillstosolve

realtimeproblems.CO3:Toimplementandanalyzethestatisticsofthedatausingvisualization

conceptsofpython.CO4:Implementandinterprettheapplicationstoexplorethedatainsights

usingPANDAS.CO5:Abletodesignanddevelopthesoftwaretomeetthecustomerand

industryrequirements.Board ofStudies(BoS):15thBoSofCAheldon22.06.2021


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SDG4:QualityEducation–Ensureinclusiveandequitablequalityeducationandpromotelifelonglearningopportunitiesfor allProgramming concepts, plan & features are taught in this course forthelearners with respect to the course outcomes are measurable and usefulinimproving the programming and logical skill of the learner. As thesoftwareindustries growingrapidly,thiscoursewillenablethelearner toexplorevarioustechnologiessuchaswebdevelopment,ArtificialIntelligence,DataScienceandIoTbyusingpythonprogramming.

CAD7128 MINIPROJECT L T P C

0 0 2 1SDG: 9

COURSEOBJECTIVES:

COB1:UnderstandtheProcessofSoftwareEngineeringfundamentals

COB2:Addresstheproblem andplantocollect data.

COB3: Describethedatastructuretoimplement.

COB4: Fabricateandimplementtheprojectusingwebdevelopmenttools

COB5:UnderstandtheimportanceofdocumentdesignbycompilingTechnicalReportontheMiniProjectworkcarriedout

GUIDELINES1. StudentstoknowaboutSoftware Engineering Processfundamentals.2. TohandlehugevolumeofStructuredandUnstructuredDatausingBigdata.3. Develop algorithms using Data Structures, Machine

LearningTechniquesandtools.4. ImplementanalgorithminPython,RProgramming,J2EE,ASP,PHPanditc

anbestored incloud environment.5. Thestudentsundertakeindividualapplicationprojectbasedontheir

interestlevel.Theprojectcoordinatorsmustapprovetheprojects.

REPORTANDDOCUMENTATION

1. Studentsmustmaintainalabrecordandupdatetheprojectprogressonaweeklybasis.

2. Mustdemonstrateduringlabhoursandupdatetheprojectprogressonaweeklybasis.

3. MustsubmitadetailedprojectreportasperthecommontemplateforaProjectViva-voce examination.

4. Monthlyreviewwillbeconductedandevaluatedbythecoordinators.PROJECTEVALUATIONCRITERIA

TheProjectcoordinatorsverifyandvalidatetheinformationpresentedintheprojectrep

ort.Thesplit-up ofmarksisasfollows:1. InternalAssessment2. ExternalExamination3. VivaVoceCOURSEOUTCOMES:CO1:ApplytheknowledgeofsoftwareengineeringProcessfundamentals

CO2:Identify,Collectandanalysedatausingtoolstosolvetherealworldproblems.CO3: Developanalgorithmusing DataStructureandMachineLearningToolsandTechniquesCO4: DesignandDevelopthesoftwareaccordingtotherealworldproblemusingUserfriendlylanguage.CO5: Demonstrate and build the project successfully by hardwarerequirements,Codingandtesting.Board ofStudies(BoS):15thBoSofCAheldon26.06.2021


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SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationLearnershavecapacity–buildingtoinvestininnovationandinthedevelopmentofcleanandsoundtechnologiesinsupportofthesustainabledevelopmentgoals.

CAD7221 PROJECTWORK L T P C

0 0 36 18SDG:9

COURSEOBJECTIVES:

COB1:Definetheproblem.COB2:Analyzeprojectrequirementsanddrawrespectivedesigndiagrams.

COB3:WriteeffectiveCodeforUserInterfacedesign,DatabaseConnectivity,ProcessinglogicandReportgeneration.

COB4:Testandimplementthe codeforallthemodules.

COB5:TraintheenduserwithsystemandusermanualandcompletetheprojectLifecycle withreportgeneration.

PROJECTGUIDELINES1. Identifyandanalyzetheobjective,scope,conceptandfeasibilityoftheprojectthr

oughliteraturereview2. Formulationofdesignanddevelopinnovativesolutionsafteridentifyingandanal

yzingproblems3. Developsoftwareprototypestoprovethedesignaspartofdevelopinginnovative

products4. Testthemodules basedon therisk and integrateall theModules.5. Discusstheresults obtainedtoderiveconclusions6. Defendtheworkbypreparing areportasperthe University format7. Compiletheexperimentalresultstopublishinjournals orconference8. Performmulti-disciplinarytaskasanindividualtomanagetheproject.9. Comprehendtheprojectdevelopmentwitheffectivepresentationandreport10.Interpretthefindingswith appropriatetechnologicalcitation

COURSEOUTCOMES:CO1:Definetherealtimeproblem/researchprojectscopes,objectivesanddeliverableswithprojectschedule.CO2:Designwithasystemmodelinglanguagetoolanddrawdiagrams,covering allmodulesof theproject.CO3:Writeeffectiveprogramstodevelopuserinterfacedesign,databasedesign,processinglogicandgeneratereports.CO4:Applyvarioussoftwaretestingtoolsforthetestcasesandimplementtheprojectmoduleswithaconsolidatedprojectreport.

CO5:Demonstratetheworkingprojecttotheenduserwithsystemandusermanual.Board ofStudies(BoS):15thBoSofCA heldon22.06.21


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SDG9:Industry,InnovationandInfrastructure-Buildinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThestudentscandemonstratecreativityandinnovationbyemployingvalidandreliableresearchstrategies.Utilizecriticalthinkingtomakesenseofproblemsandpersevere insolvingthem.

SEMESTER II

ELECTIVES

CADY251 DIGITALMARKETING L T P C

3 0 0 3SDG: 9COURSEOBJECTIVES:

COB1:ExplainasystematicapproachtodevelopaDigitalMarketingstrategy

COB2:FamiliarizeonlinemarketingstrategyintegratedwithoverallmarketingObjectives

COB3: Exploreemailmarketingasaneffectivemarketingchange

COB4: Exposealltheessentialsof mobilemarketing

COB5:Explorevariousstrategicbuildingprocessindigitalmarketing

MODULEI DIGITALMARKETINGBASICS 9Introductiontomarketing-digitalmarketinganditsprinciples-digitalmarketingwins over traditional marketing- CPR, CPM, PPC, CPC,SEO,SEM-UNDERSTANDINGvariousSocialchannels-DigitalMarketingProcess-IncreasingVisibility-VisitorsEngagement--BringingTargetedTraffic-ConvertingTrafficintoLeads-Retention-PerformanceEvaluation.MODULEII BUILDINGWEBSITEANDSEARCHENGINE

OPTIMIZATION9

Internet- web – websites-domain names-web server- web hosting-Planningand conceptualizing a website- Building website using CMS inclass-SEO-SERPGoogleKeywordPlannertool-GoogleOperator-Contentoptimization&planningOnPageOptimization-OffPageOptimization-LocalSEO-GoogleWebmasterToolsMODULEIII ONLINE DISPLAY ADVERTISING AND

ECOMMERCEMARKETING9

Online advertising-display advertising- Banner ads- Rich Media ads- PopupsandPopunderads-Contextualadvertising-PaymentModules-Onlineadvertising platforms- Ecommerce- Top Ecommerce websites-EcommercescenarioinIndiamarketingstrategy-MobileMarketingandSocialMedia-Usingtoolstocreatemobilewebsites-ContentMarketingonmobile-SMSmarketing-UploadingmobileappinAndroidandIOSMODULEIV CONTENTMARKETING 9

ContentMarketing-stepsinstrategybuildingprocess-Optimizingcontentforsearchengines-authorityblog-monetizingauthorityblog-uniquewaystowritemagneticheadlines-Case studyoncontentmarketing.

MODULEV ONLINEREPUTATIONMANAGEMENT 9Online reputation management- ORM scenario- Onlinereputationmanagement Commandments- positivebrandimageonline-toolsformonitoringonlinereputation-overcomenegativeonlinereputation-CaseStudy

L–45; TOTALHOURS –45

TEXTBOOKS:1. AlanCharlesworth,” Digital

Marketing:APracticalApproach”,RoutledgePublication, 2nd Edition,20142. “DigitalMarketersSoundOff:Tips,Tactics,Tools,andPredictions”,Matt

Chiera,2018REFERENCES:

1.DaveChaffeyEtAlEMarketingExcellence:Planning andOptimizingyourdigitalmarketing,ThirdEdition,2008

COURSEOUTCOMES:CO1:Listtheadvantagesof digitalmarketingovertraditionalmarketing.

CO2:Summarizehowtheycanusedigitalmarketingisusedtoincreasesalesandgrowtheirbusiness.

CO3:Analyzedigitalmarketingtoolkit

CO4:Familiarizeelementsofthedigitalmarketingplan

CO5:Developonlinetargetmarket andbasicdigitalmarketingobjectives



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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainable industrializationandfoster innovation

Digital technologies place people at the center of products andservices,allowing for attractive offerings due to reduced costs, improvedsustainability,anduser-friendlinessTechnologiesatissueenablenewbusinessmodelsthatenhanceinnovationandgrowthinawiderangeofsectors.

CADY252 MANAGEMENTINFORMATIONSYSTEM

L T P C


COB1:Introducetheconcepts of MachineSystem.

COB2:UnderstandtheStructureofOrganization.

COB3: Learntomakedecisions.

COB4: Knowaboutthestoragedevicesandfileorganizations.

COB5:Becomefamiliarwithdevelopmentandmanagementofproject.

MODULEI SYSTEMCONCEPTS 9Definition–Computerbasedusermachinesystem–Integratedsystem–Needforadatabase–Utilizationofmodels–Evolution–Subsystems–Organizationalsubsystems–Activities subsystems.

MODULEII ORGANIZATIONALSTRUCTURE 9Basic model – Hierarchical – Specialization – Formalization – Centralization–Modificationsofbasicorganizationalstructure–Projectorganization–Lateralrelations–Matrixorganization–Organizationalcultureandpowerorganizationalchange.MODULEIII STRUCTUREOFMIS 9Operatingelements–Physicalcomponents–Processingfunctions–outputs–MISsupport for decision making– Structured programmable decisions–Unstructurednon-programmabledecisions–MISstructurebasedonmanagementactivityandorganizationalfunctions–SynthesisofMISstructure.MODULEIV SYSTEMSUPPORT 9Datarepresentation–Communicationnetwork–Distributedsystems–Logicaldataconcepts–Physicalstoragedevices–Fileorganizations –Database.MODULEV DEVELOPMENTANDMANAGEMENT 9Acontingencyapproachtochoosinganapplication–Developingstrategy–Lifecycledefinitionstage–Lifecycledevelopmentstage–Lifecycleinstallationandoperation stage–Projectmanagement.


TEXTBOOKS:1. JamesA,O’Brien,GeorgeM.Marakas,RameshBehl,"ManagementInfor

mationSystems",10thEdition,McgrawHill,2017.2. HaroldKoontz,HeinzWeihrich,“EssentialsofManagement”,5th

Edition,TataMcGrawHill 1998..REFERENCES:

1.E.WainrightMartin,CarolV.Brown,DanialW.DeHayes,JeffreyA.Hoffer,WilliamC.Perkins,“ManagingInformationTechnology”3rdEdition,PrenticeHallInternationaledition1999.

COURSEOUTCOMES:CO1:Learnthebasics ofMachineSystem.

CO2:UnderstandtheOrganizationstructure.

CO3:Identifythedecisionsfor MIS.

CO4:FamiliarwithStoragedevicesanditsorganization.

CO5:AbletodevelopaProjectandmaintain.



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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationDevelopthemanagingskillswithrespecttothecourseoutcomesandimprovetheorganizationdevelopmentskillofthe learner.Thiswouldhelpthelearnerindevelopingspacesforcommunityget-togethers.

CADY253 MULTIMEDIASYSTEMS ANDCOMPUTERGRAPHICS

L T P C


COB1:Explainthebasicconceptofmultimediaanditshardware/software.

COB2:Explore thevariousmultimediatoolsanditsusage

COB3: Familiarize the importance of internet in multimediaapplications.

COB4:Introducebasicgraphics anddesignalgorithms.

COB5:Illustratetheconceptof 2Dand3Dtransformation.

MODULEI INTRODUCTION 9Definition-CD-ROMandMultimedia-Multimediaapplications:business–schools-Homes-publicplacesandvirtualreality.Introductiontomakingofmultimedia:hardware- software-creativity- andorganization.MODULEII MULTIMEDIATOOLS 9Macintosh and windows production platforms - 3-d modelling and animation-image-editingtools-soundeditingtools-animation-video-anddigitalmovietools -linking multimedia objects - office suites - word processors - spreadsheets -databases - presentation tools. Authoring tools - Card andPage-basedauthoringtools-IconBasedauthoringtools-timebasedauthoringtools- objectorientedauthoringtools-crossplatform-authoringtools.MODULEIII MULTIMEDIAANDTHEINTERNET 9Internet fundamentals: Internetworking – Connections – Internet services–The World Wide Web – Tools for the World Wide Web: Web serves –Webbrowsers–WebpagemakersandSitebuilders–Plug-insandDeliveryvehicles–BeyondHTML.MODULEIV GRAPHICSPRIMITIVES 9IntroductionOverviewofGraphicsSystem–Bresenhamtechnique–LineDrawingandCircleDrawingAlgorithms–DDA–LineClipping–TextClipping.MODULEV 2DAND3DTRANSFORMATIONS 9Twodimensionaltransformations–ScalingandRotations–InteractiveInputmethods–Polygons–Splines–BezierCurvesWindowviewportmappingtransformation– 3DConcepts–Projections.


TEXTBOOKS:1.TayVaughan,”Multimedia:MakingItWork”,8thEdition,2011,

McGraw-Hill(Unit 1,Unit 2andUnit 3)

2.HearnDandBakerM.P,“Computergraphics–CVersion”,2ndEdition,

PearsonEducation,2004(Unit4and5)REFERENCES:

1. K.AndleighandK.Thakkrar,“MultimediaSystemDesign”,1996,PrenticeHall PTR

2. SteveRimmer,“Advancedmultimediaprogramming”,Windcrest/ McGrawHill,1995

COURSEOUTCOMES:CO1:AnalyzethetechnicalaspectofMultimediaSystems.

CO2:DevelopvariousMultimediaSystemsapplicationforrealtimescenario.

CO3:Applyvariousnetworkingprotocolsformultimediaapplications.

CO4:Evaluatemultimediaapplicationforitsoptimumperformance.

CO5:Createamultimediacomponentusingvarioustoolsandtechniques.



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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationDesigninganddevelopingskillstaughtinthiscoursewithrespecttothecourseoutcomes improve the analytical knowledge and innovation of the learner.Itwouldcreateavarietyofwaysforthelearnertoprogressandcanhelpsignificantlyimprovethequalityof thelearner.

CADY254 ORGANIZATIONALBEHAVIOUR

L T P C

SDG: 93 0 0 3

COURSEOBJECTIVES:COB1:Toenablethestudentstounderstandtheleadershipanditsgoalstowar

dstheorganizational behaviour.

COB2:Tounderstandtheconcepts,principlesandtechniquesrelatingtodifferentfunctionalareasof organizationalbehaviour.

COB3:Toidentifythemajorframesofreferenceon conflicts.COB4:Toenablethestudentstocreateanawarenessonethicsand

humanvalues,toknowmoralandsocialvaluesandloyalty.COB5:Toenablethestudentstobemoreinnovativeandgetthe

rewardsforhardwork.MODULEI LEADERSHIP 9Characteristicsofleadership-TechnicalLeadership-Leader'sGoal,Conviction,Vision – Leadership Styles: Transformational andTransactionalLeadership-Leader'sVision-Professionalism:Importance,Elements-Managing Awareness -Performance-Manager's RoleinProfessionalism.MODULEII TALENTMANAGEMENT 9TalentedProfessionals–Importance-Characterization-Identification–Assessment and Recognizing Talent- - Purpose of Talent Management-Talentmanagementprocess-Development-DevelopmentNeeds–CounselingandMentoring.MODULEIII CONFLICTMANAGEMENT 9Reasons for conflict- Conflict frames of reference - Conflict levels and cause-Conflictmanagement:resolutionapproaches,stimulationapproaches-Organizationaljustice:Components,Consequences-workbehaviours:citizenshipbehaviour,Counter-productivebehaviour.MODULEIV ETHICSINORGANIZATION 9SensesofEthics–Varietyofmoralissues–Typesofinquiry–Moraldilemmas–MoralAutonomy–Kohlberg‘stheory–Gilligan‘stheory–ConsensusandControversy–Modelsofprofessionalroles-Theoriesaboutrightaction–Self-interest– Customs andReligion– UsesofEthicalTheories.

MODULEV INNOVATIONANDRECOGNITION 9TheImportanceofInnovation-RiskofFailure-NatureofCreativity-Imagination-Managing Innovative Teams - Needs of Creative Teams -TeamDynamics-InnovativeTeamEnvironment-AwardPrograms-RecognitionPrograms-IndustryAwardPlans-AwardGuidelines –IncentivePlans.


TEXTBOOKS:1. DavidA.Buchanan,AndrzejA.Huczynski,OrganizationalBehaviour,

PearsonEducationLimited, UnitedKingdom,10thedition2019.2. MelihaNurdanTaskiranandFatihPinarbaşiIstanbulMedipolUniversity,T

urkeyMultidisciplinaryApproachestoEthicsintheDigitalEra,IGIGlobalbookseriesAdvancesinInformationSecurity,Privacy,andEthics(AISPE),2021.

3. WattsS.Humphrey,“ManagingTechnicalPeople:Innovation,Teamwork, andtheSoftwareProcess”,Addison-Wesley,1996.

REFERENCES:1. CarolinaMachado,

J.PauloDavim,OrganizationalBehaviourandHumanResourceManagement,SpringerInternationalPublishing,2018.

2. LauraP.HartmanandJoeDesjardins,BusinessEthics:DecisionMakingforPersonalIntegrityandSocialResponsibility,McGrawHilleducation,IndiaPvt.Ltd.NewDelhi,2013.

3. WorldCommunityServiceCentre,ValueEducation,Vethathiripublications,Erode, 2011.

4. Saiyadain,M.S.OrganizationalBehaviour,TataMcGrawHill,2009.

5. MikeW.MartinandRolandSchinzinger,EthicsinEngineering,TataMcGrawHill,NewDelhi,2003.

COURSEOUTCOMES:Studentswouldbeencouraged:CO1:Toworkinteam,toleadateamandcomeupwithmoreinnovativeideas.

CO2:Toperformasateam leaderandteammemberwithtechnicalskills.

CO3:Tomanagetheorganizationalconflicts.

CO4:Toknowthehumanvalues,moralvalues,socialvaluesandloyaltyinanorganization.

CO5:Tocomeupwithmore innovativeideas.



https://1lib.in/g/Carolina%20Machado

https://1lib.in/g/J.%20Paulo%20Davim

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SDG9:Industry,InnovationandInfrastructure–Buildastronginfrastructure,encouragethecompleteandsustainableindustrialdevelopmentsandinnovationsThe purpose of the organizational behaviour is to develop an ongoingandconstructiveinterchangeamongorganizationalbehaviourscholarsandpractitioners to conduct research that is relevant for management theoryandpractice in the contemporary world. The organizational behaviour aimsatpromoting research and interests in individual behaviour as well asgroupbehaviour in the organizational context by providing a wide-ranging,engagedandinternationally-focusedforumtodiscussanddevelopresearchandpracticeinthefield.

SEMESTERIIIELECTIVES

CADY351 MOBILECOMMERCE L T P C

3 0 0 3SDG:9

COURSEOBJECTIVES:COB1:TointroducetheE-Commercestrategiesandvalue chains.

COB2:TolearntheM-Commerceservices.

COB3: To understand mobile-commerce infrastructure andapplications.

COB4:Toknowtheavailabilityoflatesttechnology,applicationsandsecurityofM-Commerceinvariousdomains.

COB5:Toanalysethecasestudies.

MODULEI ELECTRONICCOMMERCE 9Introduction -The e-commerce environment - The e-commerce marketplace-Focusonportals,Locationoftradinginthemarketplace-Commercialarrangementfor transactions - Focus on auctions - Business models fore-commerce–e-CRM–e-SCM-Revenuemodels-Focusoninternetstart-upcompanies-E-commerceversus E-business.MODULEII MOBILECOMMERCE 9Introduction – Infrastructure of M–Commerce – Types of MobileCommerceServices – Technologies of Wireless Business – Benefits andLimitations,Support,MobileMarketing&Advertisement,non–InternetApplicationsinM–Commerce–Wireless/WiredCommerceComparisons.MODULEIII FRAMEWORKANDARCHITECTURE 9A Framework for The Study of Mobile Commerce – NTT Docomo’s I– Mode–WirelessDevicesforMobileCommerce–TowardsAClassificationFrameworkforMobile Location Based Services – Wireless Personal and LocalAreaNetworks–TheImpactofTechnologyAdvancesonStrategyFormulationinMobileCommunicationsNetworks.MODULEIV PAYMENTGATEWAYS 9PaymentGatewayArchitecture–SoftwareComponentsof PaymentGateway–RequirementsofPaymentGateway–TypesofPaymentGateway–StepsinBuilding

a PaymentGateway–SecurityinPaymentGateways.

MODULEV MOBILECOMMERCE–CASESTUDIES 9MobileDataTechnologiesandSmallBusinessAdoptionandDiffusion–M–CommerceinTheAutomotiveIndustry–Location–BasedServices:Criteria

forAdoptionandSolutionDeployment–TheRoleofMobileAdvertisinginBuilding aBrand–M–CommerceBusiness.

L–45; TOTALHOURS–45

TEXTBOOKS:1. E-CommerceandMobileCommerceTechnologies,byPandeyU.S.&Shukl

aSaurabh |9July2018.2. MobileCommerce, byKarabiBandyopadhyay|1 December2013.3. E-PaymentGatewayACompleteGuide-2019Edition,byGerardusBlokdy

k.

REFERENCES:1. E-CommerceandMobileCommerceTechnologies,KristianBass,2018.2. MobileCommerce:Concepts,Methodologies,Tools,andApplicationsbyM

anagementAssociation,InformationResources, 2017.3. “E-BusinessandE-CommerceManagement”,DaveChaffey,Third

Edition, 2009,PearsonEducation.

COURSEOUTCOMES:CO1:ToapplyE-Commerce principlesinmarketplace.

CO2:ToimplementM-Commerceprinciplestovariousbusinessdomains.

CO3:Todesigntheapplicationsof M-Commerceinbusiness domain.

CO4:Todeploysecuritymeasuresinmobile commerceapplication.

CO5:To implementthecase studies.



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SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationUnderstandingofprinciples,designanddevelopmentofthemobilecommerceframeworkandtechnologyleadstoimprovethemobiletechnologyskillsforindustrialization.

CADY352 MOBILESECURITY L T P C

3 0 0 3SDG:9

COURSEOBJECTIVES:COB1:Understandfundamentalmobilecomputingprinciples,andmodelsandmobile computingsecurityprinciples.

COB2:Understandthefundamentalelementsandroleofencryptioninmobileapplicationanddevicesecurity,anddescribecommonscenarioswhere encryption processesare applied

COB3:Gainin-depthknowledgeonwirelessandmobilenetworksecurityand itsrelationto thenewsecurity-basedprotocols.COB4:Applyproactiveanddefensivemeasurestocounterpotentialthreats,attacksandintrusions.COB5:Designsecuredwirelessandmobilenetworksthatoptimizeaccessibilitywhilstminimizingvulnerabilitytosecurityrisks.

MODULEI INTRODUCTION 9Introduction:Confidentiality,IntegrityandAvailabilityThreatsinMobilePhones,Perceptions,andAwarenessRegardingMobilePhoneSecurity,Voice,SMS,andIdentificationDataInterceptioninGSM,SMSSecurityIssues.

MODULEII NETWORKAUTHENTICATION 9Authentication,Encryption/DecryptioninGSM, Securing the WLAN,WEPIntroduction,RC4Encryption,DataAnalysis,IVCollision,KeyExtraction,WEPCracking, WPA/ WPA2, AES, Access Point-Based Security Measures,Third-PartySecurityMethods,Funk'sSteel-BeltedRadius,WLANProtectionEnhancements.MODULEIII SECURITYISSUES 9BasicSecurityandCryptographicTechniques-SecurityofGSMNetworks-SecurityofUMTSNetworks-LTESecurity-Blue-toothSecurityImplementation,SecurityinWi-MAX, UWBsecurity,SatelliteNetworkSecurity.MODULEIV SECURITYTYPES 9Introduction to Mobile Security-SIM/UICC Security. Mobile Malware andAppSecurity Android Security Model. IOS Security Model. Security Model oftheWindowsPhone.SMS/MMS,MobileGeolocationandMobileWebSecurity.-SecurityofMobileVoIPCommunications-EmergingTrendsinMobileSecurity.MODULEV SECURITYTHREATS 9SecurityThreatsandVulnerabilities-Virus-Trojan-Rootkits-Backdoors-Botnets-Man inthe middleattack-DosandDDos -Replayattack-Spoofing-Spam-Phishing-privilegeescalation-DNSpoisoning-Bruteforce-Dictionaryattack-Cross-sitescripting-SQLinjection-Zero-dayattack-

Sessionhijacking-VulnerabilityscanningvsPortScanning-Honeypots-Bannergrabbing-SocialEngineering.


TEXTBOOKS:1. Andreoulakis,IosifI,MobilePhoneSecurityandForensics,APracticalAppr

oach,2012.2. ChrisClarkandDavidThiel,MobileApplicationSecurity,Himanshu

Dviwedi,1stEdition.REFERENCES:

1. Hidekilmai,MohammadGhulamRahmanandKazukuniKobari"WirelessCommunicationsSecurity",UniversalPersonalCommunicationsofArtechHouse,2006.

2. Stallings William, "Wireless Communications and Networks”SecondEdition,PearsonEducationLtd,2009.

3. NoureddineBoudriga,Securityof MobileCommunications,2009.COURSEOUTCOMES:CO1:Identifyandinvestigatein-depthbothearlyandcontemporarythreatsto

mobileandwirelessnetworkssecurity.CO2:Analyzing thefundamentalelementsandroleofencryptioninmobile

applicationanddevicesecurity.CO3:Applyproactiveanddefensivemeasurestodeter andrepelpotential

threats,attacksandintrusions.CO4:Developaclearviewofintegratedsecurityenvironmentsconsistingof

bothsimilaranddiversewirelessaccesstechnologiesandsecurityarchitectures.

CO5: Understand common threats and vulnerabilities related to

mobilecomputingnetworks,andexplaintheconceptsofdefendingagainstandmanagingnetworkattacks.

BoardofStudies(BoS):15thBoSofCAheld on22.06.2021


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Note: L-LowCorrelation M-Medium Correlation H-HighCorrelation

SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationBylearningthefundamentalsofmobilecomputingandmodelsandsecurityprinciples, which helps to develop an integrated security system consistingofbothsimilaranddiversewirelessaccesstechnologiesinmobileapplications.

CADY353 MOBILEANDDIGITALFORENSICS

L T P C

SDG: 9 3 0 0 3COURSEOBJECTIVES:COB1:ThecoursewillincorporatethefoundationalunderstandingofDigitalmobileforensics.

COB2:Understandthefundamentalsandadvancedissuesofvariousthreatsfacedbytoday’sMobilecyberoperationinfrastructure.COB3:Studentswillgettoknowthefunctionalityofmobilenetworkforensicsandexploretheirtechnicaloperations.

COB4:Learnthetechnicalfunctionsthatresideonmobiledeviceswithvarious

operatingsystems.COB5:Illustratetheimportanceofreportgenerationswithreal-timecasestudies.MODULEI INTRODUCTION TO DIGITAL MOBILE

FORENSICS9

IntroductiontoDigitalMobileForensics:Mobileforensicchallenges-Mobilephoneevidenceextractionprocess.ChainofCustody:Identificationphase-PreparationPhase-IsolationPhase-ProcessingPhase-Verificationphase-Documentandreportingphase-Presentationphase.MODULEII MOBILEFORENSICS 9MobileDeviceForensics:UnderstandingMobileDeviceForensics-MobilePhoneBasics-InsideMobileDevices-UnderstandingacquisitionproceduresforMobileDevices-MobileForensicsEquipment-UsingMobileForensicsTools.

MODULEIII NETWORKMOBILEFORENSICS 9CellularNetworks:TypesofCellularNetworks-MobileOperatingSystems-CellPhone Evidence- Call Detail Records- Collecting and Handling CellPhoneEvidence- Subscriber Identity Modules(SIM)- Cell Phone Acquisition:PhysicalandLogicaltechniques-CellPhoneForensicTools-GlobalPositioningSystems(GPS)-Casestudy.MODULEIV OPERATING SYSTEMS IN MOBILE

FORENSICS9

Mobileoperatingsystemsoverview:ProceduresforhandlinganAndroiddevice-HowtoCircumventthePassCode-Recoverymode.DataAcquisitionfromiOSDevices- Operating modes of iOS devices- Recovery mode. WindowsPhoneForensics-WindowsPhoneOS-Security model-WindowsPhonefilesystem-ExtractingSMSandapplicationdata.MODULEV REPORTWRITINGFORHIGH-TECH

INVESTIGATIONS9

Understanding the Importance of Reports: Types of Reports- GuidelinesforWriting Reports- Preliminary Reports- Report Structure- Designing theLayoutandPresentationofReports-ExaminationandDataCollectionMethods-Realtimecasestudy-Incidentspecific procedures.

L–45;TOTALHOURS–45

TEXTBOOKS:1. Practical Mobile Forensics: Forensically investigate and analyze

iOS,Android, and Windows 10 devices, 4th Edition Paperback –Import, 9April2020.

2. Mobileforensics:PracticalMobileForensics:ForensicallyinvestigateandanalyzeiOS,Android, andWindows 10 devices, 4th EditionbySatishBommisetty,RohitTamma,HeatherMahalik,21 July2014.

3. Nelson B, Phillips, Enfinger F, Stuart C., “Guide toComputerForensicsandInvestigations,2nded.,ThomsonCourseTechnology,ISBN:0-619-21706-5,2006.

4. The Basics of Digital Forensics the Primer for Getting StartedinDigital Forensics, John Sammons Technical EditorJonathanRajewski.SYNGRESS,Elsevier,ISBN978-1-59749-661-2,2012.

REFERENCES:1. AndroidForensicsInvestigation,Analysis,andMobileSecurityforGoogle

Android, Andrew Hoog John McCash, SYNGRESS, Elsevier,2011.2. MobilePhoneSecurityandForensicsAPracticalApproachAuthors:Androu

lidakis,IosifI.ISBN978-3-319-29742-2,Springer,2016.

COURSEOUTCOMES:CO1:Conductdigitalinvestigationsthatconformtoacceptedprofessionalstandardsandarebasedontheinvestigativeprocess:identification,preservation,examination,analysis,andreporting.

CO2:Identifyandapplycurrentpracticesfordatadiscovery,recoveryandacquisition.CO3:Applyasolidfoundationalgroundingincomputernetworks,operatingsystems,fi

lesystems,hardware,andmobiledevicestodigitalinvestigations

andtoprotectcomputernetworkresourcesfromunauthorizedactivity.CO4:Learnandexploreonthedetailsofwhatresidesonmobiledevicesintechnicalaspects.

CO5:Acquireknowledgeonreport writingandopenupwaystocommunicateeffectivelytobothtechnicaland non-technicalaudiences.



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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainable,industrializationandfosterinnovationThe student would be able to do the chain of investigation steps that needtobe ensured for Confidentiality, Integrity, Authenticity, and legal acquisitionofanyformofdigitalevidencefrommobiledevices.Theoutcomesofthecoursearemeasurable and able to meet real-time cases. Also, would enablethelearnertohavefunctionedinforensicsectorswithphenomenaltechnicalideas.

CADY354 PRINCIPLESOFVIRTUALIZATION

L T P C

SDG:9 3 0 0 3COURSEOBJECTIVES:COB1:ToUnderstandVirtualizationConcepts,Technologies,ArchitectureandApplications

COB2:ToUnderstand the Principle of Virtualization, Storage, DataManagementandDataVisualizationCOB3:ToUnderstandandApplyVariousTypesofVirtualization

COB4: ToUnderstandDifferentCloudProgrammingPlatformsandDeployApplicationsonCloudMODULEI INTRODUCTION 9Virtualization definition – Virtual machine basics – Need andApplicationsof Virtualization –Virtualization Technologies – Benefits andLimitations−Traditionalvs.ContemporaryVirtualizationprocess–SimulationsandEmulations–Pitfallsof virtualization–Taxonomy–Challenges.MODULEII TYPESOFVIRTUALIZATION 9Typesofhardwarevirtualization:Fullvirtualization-Paravirtualization–Desktopvirtualization- Server virtualization – Data virtualization – OSlevelvirtualization-Applicationlevelvirtualization–MemoryandI/Ovirtualization–ComparingVirtualizationapproaches–ManagingstorageforvirtualmachinesMODULEIII VIRTUALMACHINE 9Understanding virtual machines − Taxonomy of virtual machines – Life cycle−Process and system level virtual machines – Emulation – Binarytranslationtechniques–Virtualisingstorage–Managingstorageforvirtualmachines–Backupandrecoveryvirtualmachine–Applicationsofvirtualmachines.MODULEIV HYPERVISORS 9IntroductiontoHypervisors–TypesofHypervisors–Hypervisorarchitecture– Comparing hypervisors – Virtualization considerations for cloud providers–Building and managing Virtual machines − Algorithms for implementationofVirtualization − Virtualization performance and Security Performance issues−VMWareVSphere−VirtualBox−VMattacks,VMmigrationattacks,SecuritysolutionsMODULEV AUTOMATION&CLOUD 9AutomatingtheDataCenter–Benefitsofdatacenterautomation–Softwaredefineddatacenter-Backup-Disasterrecovery–VirtualizationandCloud,AnatomyofCloud,BenefitsofCloud,CloudDeliveryandDeploymentmodels.


TEXTBOOKS:

1. MatthewPortney,“VirtualizationEssentials”,JohnWiley&Sons, 20122. Nadeau,TimCerng,JeBuller,ChuckEnstall,RichardRuiz,“MasteringMicr

osoftVirtualization”,WileyPublication, 2010.3. NelsonRuest,DanielleRuest,“Virtualization,Abeginnersguide”,2009,MG

H4. VenkataJosyula,MalcolmOrr,GregPage,“CloudComputing:Automatingthe VirtualizedDataCenter”,Ciscoress/Pearson,2012

REFERENCES:1. VenkataJosyula,MalcolmOrr,GregPage,“CloudComputing:Automatingt

heVirtualizedDataCenter”,CiscoPress/Pearson,2012.2. DaveShackleford,Virtualizationsecurity,protectingvirtualizedenvironme

nt,JohnWiley,2012.3. Edward Haletky, “VMware ESX and ESXi in the Enterprise –

PlanningDeploymentofVirtualizationServers”[ISBN:978-0137058976].,PrenticeHall;2edition February18,2011

4. ChrisWolfandErickM.Halter,“Virtualization”Apress;1stedition2005.COURSEOUTCOMES:CO1:UnderstandingandImplementationof virtualmachines.

CO2:Createand ConfiguretheHypervisorsinCloud

CO3:ApplytheVirtualizationConceptsinServerandManagetheStorageCapacity.

CO4:Analyze,IdentifyandSelectSuitableTypeofVirtualization.

CO5:UsetheManagementToolsforManagingtheVirtualizedCloudInfrastructure



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SDG9:Industry,InnovationandInfrastructure –Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThelearnerwouldbeabletointroducetheconceptofVirtualizationatOperatingSystems, Application, Memory, and at I/O levels to build aVirtualEnvironment for an enterprise in cost effective manner. The outcomesof thecoursearemeasurableandwouldenablethelearnertobeproductiveinindustrializationprocesswithnovelideasofvirtualization.

CADY355 CLOUDARCHITECTURES L T P C


COB1:Understandthebroad perceptiveofcloudarchitectureandmodel.

COB2:Providethecoreconceptsusedincloudcomputing.

COB3: Exploretheleadplayers incloudanddesignofcloudServices.

COB4:Gaintheknowledgeofvirtualizationtechniques.

COB5:Learnthesecurityandtrustedcloudcomputingsystem.

MODULEI INTRODUCTION 9Cloud Computing and Service Models: Public – Private - Hybrid Clouds-CloudEcosystemandEnablingTechnologies-Infrastructure-as-a-Service(IaaS) - Platform- and Software-as-a-Service (Paas, SaaS).ArchitecturalDesignofComputeandStorage Clouds:AGenericCloudArchitectureDesign-LayeredCloudArchitecturaldevelopment-VirtualizationSupportandDisasterRecovery-ArchitecturalDesignChallenges.MODULEII CLOUDCOMPUTINGSTANDARDS 9Best Practices and Standards - Practical Issues- Interoperability-Portability-Integration- Security - Standards Organizations and Groups- CloudSecurityAlliance- Distributed Management Task Force (DMTF)- NationalInstituteofStandardsandTechnology(NIST)-OpenCloudConsortium(OCC)-OpenGridForum(OGF)-ObjectManagementGroup(OMG)-StorageNetworkingIndustryAssociation (SNIA)-Cloud Computing InteroperabilityForum(CCIF)-VerticalGroups.MODULEIII CLOUDVENDORSANDSERVICE

MANAGEMENT9

Amazon cloud - AWS Overview - Installation of AWS - Google app engine-azure cloud - sales force. Service Management in Cloud Computing:ServiceLevelAgreements(SLAs)-Billing&Accounting-ComparingScalingHardware:Traditionalvs.Cloud-Economicsofscaling:Benefittingenormously,ManagingData:LookingatData-Scalability&CloudServices-Database&DataStoresinCloud -LargeScaleData Processing.MODULEIV VIRTUALIZATION 9

Basics of Virtualization - Types of Virtualization - Implementation LevelsofVirtualization-VirtualizationStructures-ToolsandMechanisms-Virtualization ofCPU - Memory - I/O Devices - Virtual Clusters andResourcemanagement–VirtualizationforData-centerAutomation-IntroductiontoMapReduce-GFS-HDFS-HadoopFramework.MODULEV SECURITYCONCEPTS&CASESTUDY 9

Cloudsecuritychallenges-Cloudsecurityapproaches:encryption-tokenization/obfuscation - cloud security alliance standards - cloudsecuritymodelsandrelatedpatterns-Cloudsecurityinmainstreamvendorsolutions-Mainstream Cloud security offerings: security assessment - secureCloudarchitecturedesign-Authenticationincloudcomputing-Clientaccessincloud-CloudcontractingModel- Commercialandbusinessconsiderations.Case Study on Open Source & Commercial Clouds: Eucalyptus -MicrosoftAzure -Amazon EC2.


TEXTBOOKS:1. Cloud Computing: A Practical Approach by Anthony T. Velte Toby

J.Velte, RobertElsenpeter,TheMcGraw-Hill.2. Cloud Computing: SaaS, PaaS, IaaS, Virtualization and more by

Dr.Kris Jamsa.3. CloudComputing:Principles,SystemsandApplications,Editors:Nik

os Antonopoulos,LeeGillam,Springer,20124. Cloud Computing: Principles and Paradigms, Editors:

RajkumarBuyya, JamesBroberg,AndrzejM.Goscinski,Wile,2011.5. CloudSecurity: AComprehensiveGuidetoSecureCloudComputing,

RonaldL.Krutz,RussellDeanVines,Wiley-India.REFERENCES:

1. KaiHwang,GeoffreyCFox,JackGDongarra,“DistributedandCloudComputing, From Parallel Processing to the Internet ofThings”,MorganKaufmann Publishers,2012.

2. Gautam Shroff, “Enterprise Cloud Computing TechnologyArchitectureApplications”, Cambridge University Press; 1 edition,[ISBN: 978-0521137355],2010.

3. DimitrisN.Chorafas,“CloudComputingStrategies”CRCPress;1edition[ISBN:1439834539],2010.

COURSEOUTCOMES:CO1:UnderstandcommonreasonswhySaaSsolutionsareselectedovertraditionalsoftwarepurchases.CO2:Learnhowglobalinfrastructurefacilitatescloudcomputing.

CO3:DesignCloudServicesandSetaprivate cloud

CO4:Applysuitablevirtualizationconcept.

CO5: Address the core issues of cloud computing such as security,privacy,interoperabilityand implementthem.



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SDG9:Industry,InnovationandInfrastructure-Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovation.Design and development skills taught in this course for the learnerswithrespect to the course outcomes are measurable and useful in improvingtheunderstanding capacity of the learner. The learner understands thelocal,nationalandglobalchallengesandconflictsinachievingsustainabilityininfrastructureandindustrialization.

CADY356CLOUDSTORAGEINFRASTRUCTURES

L T P C

3 0 0 3

SDG:9

COURSEOBJECTIVES:

COB1:Appraisetheopportunitiesandchallengesofinformationmanagement

inbusinessenvironment.COB2:Comparethemodernsecurityconceptsandassessthesecurityofvirtu

alsystems.COB3:EvaluateInformationstoragemanagementinacloudenvironmentand

howitrelatestothebusiness objectivesofanorganization.COB4:Familiarizeintypesof storagesystemandsolutions

COB5:ImplementationofstorageinGooglecloud.

MODULEI INFORMATION STORAGE AND DATA9CENTERENVIRONMENT

InformationStorage:EvolutionofStorageArchitecture,DataCenterInfrastructure,VirtualizationandCloudComputing.DataCenterEnvironment: Application, Database Management System, Host, Connectivity,Storage,Disk Drive Components, Disk Drive Performance, Host Access toData,Direct AttachedStorage,StorageDesign basedon Application.MODULEII SECURINGSTORAGEINFORMATION 9Information security framework, Risk Triad, Storage Security Domains–Security Implementation in Storage Networking, Securing ImplementationinStorageNetworking–SecuringStorageInfrastructureinVirtualizedandCloudEnvironments–ConceptsinPracticeRSAandVMware SecurityProducts.MODULEIII MANAGINGSTORAGEINFORMATION 9Monitoring the Storage Infrastructure – Storage InfrastructureManagementActivities – Storage Infrastructure Management Challenges –DevelopinganIdealSolution-InformationLifecycleManagement–StorageTiering–Conceptsin Practice:EMC InfrastructureManagementTools.MODULEIV STORAGESYSTEMANDSOLUTION 9

Types of Storage System – Storage System in Characteristics –StorageSolution Packaging Approaches – Converged Infrastructures andServices –Gateways, Appliances, Adapters and Accessories – StorageManagementSoftware -ResiliencyInside andOutsideStorage Solutions.MODULEV GOOGLECLOUD STORAGE 9Object Storage: Concepts, storing data in Cloud Storage, AccessControl,Object Version, Change notifications, Common use Cases,UnderstandingPricing,Cloud Storageusage.


TEXTBOOKS:1. EMC, “Information Storage and Management” Wiley; II Edition

(ISBN:978-0470294215),2012.2. GregScholz,“Software–definedDataInfrastructuresEssentials”,CRCPre

ss, (ISBN:978-14987-3815-6);2017.3. JJGeewax,“GoogleCloudPlatforminaction”,(ISBN:978-161-

7293528),2018.REFERENCES:

1. VolkerHeminghausAlbertScriba,“StorageManagementinDataCenters”Springer; (ISBN :978-3540850229).2009.

2. MartyPonaiatowski,“FoundationsofGreenITPrenticeHall;(ISBN:978-0137043750),2012.

3. KlausSchmidt,“HighAvailabilityandDisasterRecovery”Springer;(ISBN:978-3540244608),2006.

COURSEOUTCOMES:CO1:Understandthekeydimensions ofthechallengesaboutDatacenterin

businessenvironment.CO2:DesignInformationSecureframeworkandimplementvariouscore

securitycontrolsforCloudComputing.CO3:Developanidealsolutionininformationmanagementrelatestothe

businessenvironment.CO4:Analyzethetypes ofstoragesystemanditsstrategiestoimplement in

CloudStorage.CO5:Deployment ofdata inGooglecloudstorage.

Board ofStudies(BoS):15th BoSofCAheld on22.06.2021


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SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationTo analyse, design and develop skills taught in this course for thelearnerswith respect to the course outcomes are measurable. Learners willpursueresearchandtobecomeasoftwareProfessionalsthroughinnovativeapproach.

CADY357 CLOUDSECURITY L T P C

3 0 0 3SDG:9

COURSEOBJECTIVES:

COB1:IntroduceandAssessthekeysecurityandcompliancechallenges.

COB2:AnalyzetheSecurityArchitectureandEvaluateRisk issues.

COB3:DepictsSecurityManagementframeworkandthestandards.

COB4: Appraisethemanagementof abusinessphysicalcloudEnvironment.

COB5: ReviewtheusabilityandIntegrityofanetworkanditsdata.

MODULEI SECURITYFUNDAMENTALSANDCHALLENGES

9

CloudComputingSoftwareSecurityFundamentals: CloudInformationSecurityObjectives – Cloud Security Services – Relevant Cloud SecurityDesignPrinciples – Secure Cloud Software Requirements –ApproachestoCloudSoftwareRequirementEngineering–CloudSecurityPolicyImplementation–SecureCloudSoftwareTesting–CloudPenetrationTesting–Regression.CloudComputingSecurityChallenges:SecurityPolicyImplementation – Policy Types –Computer Security Incident ResponseTeam(CSIRT)–Virtualization SecurityManagement.

MODULEII SECURITY ARCHITECTURE AND RISKISSUES

9

CloudComputingSecurityArchitecture:ArchitecturalConsiderations–IdentityManagementandAccessControl–AutonomicControl.CloudComputingRiskIssues:TheCIATriad–PrivacyandComplianceRisks–ThreattoInfrastructure,DataandAccessControl–CloudServiceProvider Risks.MODULEIII SECURITYMANAGEMENT 9SecurityManagementintheCloud:SecurityManagementStandards-AvailabilityManagement – SaaS Availability Management – PaaSAvailabilityManagement–IaaSAvailabilityManagement–AccessControl–SecurityVulnerability,patch,andConfigurationManagement.MODULEIV DATAANDCLOUDASSETMANAGEMENT 9DataAssetManagementandProtection:DataIdentificationandClassification-Data Asset Management in the Cloud - Protecting Data in the Cloud.CloudAssetManagementandProtection: DifferencesfromTraditionalIT–TypesofCloudAssets–AssetManagementPipeline–TaggingCloudAssets.MODULEV NETWORKSECURITY 9

NetworkSecurity:DifferencesfromTraditionalIT–ConceptsandDefinitions–SampleApplication.Detecting,Respondingto,andRecoveringfromSecurityIncidents: Differences fromTraditional IT – What to Watch – How to Watch–PreparingforanIncident–Recovery–ExampleMetrics–ExampleToolsforDetection, Response,andRecovery.

TOTALHOURS –45

TEXTBOOKS:1. Chris Dotson, “Practical Cloud Security” O’Reiliy Media; (ISBN:

978-1-492-03751),2019.2. RonaldL.KrutzandRusellDeanVines“CloudSecurity–AComprehensive

Guide to Secure Cloud Computing” WileyPublication(ISBN:978-0-470-58987-8),2ndEdition 2021.

3. Tim Mather, Subra Kumarasway, ShahedLatif, “Cloud SecurityandPrivacy: An Enterprise Perspective on Risk and Compliance “O’ReiliyMedia; (ISBN:0596802765),2009.

REFERENCES:1. JohnR.Vacca“CloudComputingSecurity:FoundationandChallenges”CR

CPress;ISBN[918-0-429-05512-6],2016.2. TimothyGrance,WayneJansen;NIST“GuidelinesonsecurityandPrivacyi

n public Cloud Computing”,2011.3. J.R.(“Vic”) Winkler,“Securingthecloud”Syngress(ISBN:

1597495921],2011.COURSEOUTCOMES:CO1: UnderstandthekeydimensionsofthechallengesandbenefitsofCloudComputing.CO2: DesignSecureCloudArchitecturesandimplementvariouscoresecuritycontrolsforCloudComputing.CO3: Createasecure–mindedworkforceandprotecttheOrganizationReputation.CO4: Totrackandusetoolsofeveryaspectsofcloudestate,Managingthemaintenance, Complianceanddisposalof Cloud.CO5:Determinenumerousopportunitiesthatexistforpractitionersseekingtocreatesolutionsforcloudcomputing.Board ofStudies(BoS):15thBoSofCAheld on22.06.2021


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SDG 9: Industry, Innovation and Infrastructure – Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationLearnerswillbeabletocreate,designdevelop,maintain,upgradeandcontinuouslyimprovesecureInfrastructure.Learnerswillhavethecapacitytobuild securedinfrastructure and contribute innovatively in the developmentofcleanandsoundtechnologieswiththesupportofthesustainabledevelopmentgoals.

CADY358 INFORMATIONSTORAGE

ANDMANAGEMENT

L T P C

SDG:9 3 0 0 3

COURSEOBJECTIVES:

COB1:Introduce the concepts of the Storage architecture andInformationLifecycle

COB2.UnderstandthebasiccomponentsofDataCenterEnvironmentandapplyDatabasemanagementsystemCOB3:DistinguishbetweendifferenttypesofIntelligentStorageSystemsCOB4:Learntodeploytheproposedsystemin cloud

COB5ProvidesecurityforStorageInfrastructureandCloudEnvironmentsMODULEI INTRODUCTIONTOSTORAGESYSTEMS 9.Overviewofinformationstorage,EvolutionofstorageArchitecture,InformationLifecycleManagementconcept,DataCenterInfrastructure,VirtualizationandCloudComputingMODULEII DATACENTERENVIRONMENT 9Application, Database Management System, Host (Compute),Connectivity,Storage,DiskDriveComponents,HostAccesstoData,Direct-AttachedStorage,StorageDesignBasedonApplication,DiskNativeCommandQueuing.MODULEIII INTELLIGENTSTORAGESYSTEMS 9Componentsof anIntelligent StorageSystem,StorageProvisioning,TypesofIntelligentStorageSystems,IntelligentStorageArrayMODULEIV CLOUDCOMPUTING 9CloudEnabling Technologies, Characteristics and Benefits of CloudComputing,CloudServiceModels,CloudDeploymentModels,CloudComputingInfrastructure,Cloud ChallengesMODULEV SECURINGTHESTORAGE

INFRASTRUCTURE9

InformationSecurityFramework,RiskTriad,StorageSecurityDomains,SecurityImplementations in Storage Networking, Securing Storage

InfrastructureinVirtualizedandCloudEnvironments.L–45;TOTALHOURS –45

TEXTBOOKS:1. PrachiS.Deshpande , SubhashC.Sharma ,

SateeshK.Peddoju,Security and Data Storage Aspect in CloudComputing (Studies in BigData,52)1sted.2019.

2. G.Somasundaram,AlokShrivastava,EMCEducationServices,InformationStorageandManagement:Storing,Managing,and

https://www.amazon.com/s/ref%3Ddp_byline_sr_book_1?ie=UTF8&field-author=Prachi%2BS.%2BDeshpande&text=Prachi%2BS.%2BDeshpande&sort=relevancerank&search-alias=books

https://www.amazon.com/s/ref%3Ddp_byline_sr_book_2?ie=UTF8&field-author=Subhash%2BC.%2BSharma&text=Subhash%2BC.%2BSharma&sort=relevancerank&search-alias=books

https://www.amazon.com/s/ref%3Ddp_byline_sr_book_3?ie=UTF8&field-author=Sateesh%2BK.%2BPeddoju&text=Sateesh%2BK.%2BPeddoju&sort=relevancerank&search-alias=books

ProtectingDigitalInformationinClassic,Virtualized,andCloudEnvironments2012, 2ndEdition,Wileypublications.

REFERENCES:1.RobertSpalding,StorageNetworks:TheCompleteReference,2017,

McGrawHill Education.COURSEOUTCOMES:CO1:Designthestoragearchitecturefortheinformation.

CO2:.Retrievedatafromthestorageandanalyzewithdatabasemanagementsystem.

CO3:Applytheconceptsof intelligentstoragetechniques

CO4:StoreandManagedata in acloud.

CO5:ProvidestoragesecuritytotheInformationStorageSystem

Board ofStudies(BoS) :15thBoSofCA heldon22-06-2021


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SDG9: Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThecourseoutcomesachievetheSustainableDevelopmentGoalofprovidingbasic infrastructure in Information, Communication Technologies-ICT. Thelearnerof this course would be proficientenough to provideinformationstoragesolutionwithinnovativeapplicationofconceptslearnedintheabovecourse.

CADY359 SEMANTICWEB L T P C

3 0 0 3

SDG: 9COURSEOBJECTIVES:

COB1:Introducethefundamentalconceptsofsemanticweb.

COB2:Learnandappreciatethemeritsofsemanticwebovertraditionalweb.Toknowthemethodstodiscover,classifyandbuildontologyformorereasonable resultsinsearching.

COB3:Tolearn,buildandimplementasmallontologythatissemanticallydescriptiveofchosenproblemdomain.

COB4:Tolearndifferentwebontologylanguagesalongwithdatatypesandassertions.

COB5:Implementapplicationsthatcanaccess,useandmanipulatetheontology.

MODULEI INTRODUCTION 9

Introductiontothe Syntactic weband SemanticWeb – EvolutionoftheWeb– The visual and syntactic web – Levels of Semantics – Metadata forwebinformation - The semantic web architecture and technologies–ContrastingSemanticwithConventionalTechnologies–SemanticModeling-Potentialofsemanticwebsolutionsandchallengesofadoption.

MODULEII ONTOLOGICALENGINEERING 9Ontologies–Taxonomies–TopicMaps–ClassifyingOntologies–Terminologicalaspects:concepts, terms,relationsbetweenthem–ComplexObjects–Subclasses and Sub-properties definitions–Upper Ontologies –Quality –Uses - Types of terminological resources for ontology building–Methodsandmethodologiesforbuildingontologies–MultilingualOntologies-OntologyDevelopment processandLifecycle–Methods forOntologyLearning–OntologyEvolution–Versioning.

MODULEIII DESCRIBINGWEBRESOURCES 9RDFOverview-ThebasicelementsofRDF-RDFTriples-Fundamentalrulesof

RDF Aggregation and distributed information-RDF tools-RDFS,Taxonomy, andOntology-NeedforRDFS-CoreelementsofRDFS

MODULEIV WEBONTOLOGYLANGUAGE 9Requirements for Ontology Languages-OWL Sublanguages-Descriptionofthe OWL Language-Layering of OWL-Examples for OWL-OWL inOWL-Namespaces,Classesof Classes,ClassEquivalence,BuildingClasses

fromOtherClasses,RestrictingPropertiesofClasses.MODULEV REAL-WORLDEXAMPLESAND 9

APPLICATIONS

DevelopmentTools forSemanticWeb–JenaFramework–SPARL–Queryingsemanticweb-SemanticWikis-SemanticWebServices–Modellingandaggregatingsocialnetworkdata-Ontologicalrepresentationofsocialrelationships,Aggregating andreasoningwithsocialnetworkdata.

L–45; TOTALHOURS–45

TEXTBOOKS:1. DieterFensel,FedericoMicheleFacca,ElenaSimperl,IoanToma,“Sema

nticWebServices” SpringerBerlinHeidelberg,2014.2. AlexanderMaedche,“OntologyLearningfortheSemanticWeb”,Springer,

SecondEdition,2012.3. GrigorisAntoniou,FrankVan,“SemanticWebPrimer”,MITPress5,

SecondEdition, 2008.REFERENCES:

1. LiyangYu,“IntroductiontotheSemanticWebandSemanticWebServices”,ChapmanandHall/CRC,September23, 2019.

2. DeanAllemangandJamesHendler,“SemanticWebfortheWorkingOntologist:EffectiveModelinginRDFSandOWL,MorganKaufmann”,SecondEdition, 2011.

COURSEOUTCOMES:CO1:Understandsemanticwebbasics,architectureandtechnologies.

CO2:Compareconventionalwebwithsemanticweb.IdentifythecomponenttechnologiesoftheSemanticWebandexplain theirroles.

CO3:UnderstandthesemanticrelationshipsamongthedataelementsusingResourceDescriptionFramework(RDF).CO4:Knowthemethodstodiscover,classifyandbuildWebontologytools.Listthelimitationsofsemanticwebtechnologiesandawareoftheservicesitcanandcannotdeliver.

CO5:Designandimplementreal-worldapplicationsthat“discovers”thedataand/orother webservicesviathesemanticweb.Board ofStudies(BoS):15thBoS ofCAon22.06.2021


https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor%3A%22Dieter%2BFensel%22&source=gbs_metadata_r&cad=4

https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor%3A%22Federico%2BMichele%2BFacca%22&source=gbs_metadata_r&cad=4

https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor%3A%22Elena%2BSimperl%22&source=gbs_metadata_r&cad=4

https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor%3A%22Ioan%2BToma%22&source=gbs_metadata_r&cad=4

https://www.google.co.in/search?sa=X&hl=en&sxsrf=ALeKk01P0VUHF6fZuWr0oRYxA3fgkwJsUQ%3A1624814666500&q=inauthor%3A%22Alexander%2BMaedche%22&tbm=bks

https://www.routledge.com/search?author=Liyang%20Yu

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SDG9:Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovation.The techniques taught to the learner would be able to introduce thesemanticweb,semanticwebservicesandontologytoolstobuildrealworldapplicationsinacosteffectivemanner.Theoutcomesof thecoursearemeasurableandwouldenablethelearnertobuildmorewebservicesviasemanticweb.

CADY360 CONTENTMANAGEMENTSYSTEMS

L T P C

SDG:4 3 0 0 3

COURSEOBJECTIVES:

COB1:ToimpartknowledgeininstallingCMSandhowCMSdifferfromwebsitebuilder

COB2:Tointroducethedesignlayoutandcreatethefunctionalitywithcorrectpermissions

COB3:Totrainthestudentonthee-commerceworkshopandtroubleshooting

COB4:Provideknowledgeonthecoremodules,usingSmartytobuildtemplateswithownfunctionality

COB5:Totrainthestudentsinusinganopensourcecontentmanagement(CMS) tool –Joomla,Apowerfulandrobusttool

MODULEI INTRODUCTION 9ContentManagementSystem(CMS)–Introduction-GettingStarted-CMSversuswebsitebuilder–CreatingPagesandNavigation.MODULEII DESIGNANDTROUBLESHOOTING 9DesignandLayout-UsingCoremodules–UsersandPermissions–UsingThird-partyModules–CreatingOwnFunctionality-E-commerceworkshop-AdvancedUse ofCMS -AdministrationandTrouble Shooting.

MODULEIII WEBPAGEADMINISTRATION 9Introductiontodynamicwebpagesanddevelopmenttoolsfordynamiccontent–Downloading tools for dynamic content – Downloading andinstallingacontentManagementSystem(Joomla!)–AdministrationelementsofaContentManagementSystem–Organizing Content.MODULEIV WEBCONTENTMANAGEMENTSYSTEM 9Introduction to WordPress - WordPress.orgvs.WordPress.com-InstallingWordPress-Exploringtheadmininterface-Contentcreation:Postsvs.pages- Contentcustomization:images,video,audio,tags,formats.MODULEV CASESTUDY 9Basic elements: pages, menus and navigation – incorporatecomponents,modules, plug-ins and languages – Case Studies: Marketingstrategiesandplanningforwebsites–Designandcreateaschoolwebsite,restaurantwebsite,blogsite,e-commercebusinesswebsite-SecuringContentManagementSystem.


TEXTBOOKS:

1. CMS Madesimple1.5,SofiaHauschildt,2010.2. Joomla!1.5:AUser’sGuide–BarrieM.NorthSecondEdition,PrenticeHall.

COURSEOUTCOMES:CO1:ApplytheknowledgetobuildaCMSfor areal-timewebsite.

CO2:Designanddevelopthee-commercesoftwaretomeetthecustomerandindustryneeds.

CO3:Uselatestsoftwareandtoolsforcreatinganinteractivemechanismandsatisfythe needs ofe-commerceindustryandsociety.

CO4:Developadministrationcapabilitytodealasindividualmemberorteamtomanagetheprojectsinthewebsite development process.

CO5:ByinculcatingthefundamentalconceptsofCMSwillpaveawaytobecomeanentrepreneurinthesoftware domain.

Board ofStudies(BoS):15thBoSofCA heldon22-06-2021


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SDG4:QualityEducation–Ensureinclusiveandequitablequalityeducationandpromotelifelong learning opportunitiesforall.Website Content Management concepts are taught in this course forthelearners with respect to the course outcomes is measurable and usefultoimprove the website design and development skill of the learner. As thee-commerce and e-learning industries growing rapidly, this course willenablethelearnerstoexplorethevarioustechnologiestounderstandandimplementtheondemandsoftwareforthebenefitofthe learners andsociety.

CADY361 PHPPROGRAMMING L T P C

3 0 0 3SDG:4

COURSEOBJECTIVES:

COB1:Introducetheconcepts ofPHPanditsstructure.

COB2: Learntodesignformsusing HTML.

COB3: Understandtheuseof JavascriptwithPHP.

COB4:Knowabouttheworkingofsessionsandcookiesandgetfamiliarwithdatabaseconnectivity.

COB5:Gettounderstandthewebdevelopmentframework.

MODULEI INTRODUCTION 9Introduction to PHP – Evaluation of PHP, Basic Syntax, Defining variableandconstant, PHP Data type, Operator and Expression. Decisions and loopMakingDecisions,DoingRepetitivetaskwithlooping,MixingDecisionsandloopingwithHtml. Array Anatomy of an Array, Creating index based and AssociativearrayAccessingarray,ElementLoopingwithIndexbasedarray,Loopingwithassociativearrayusingeach() andforeach(),SomeusefulLibraryfunction.

MODULEII HANDLINGHTMLFORMS 9Form Handling –PHP Interactive Forms-PHP GET & POST-FormValidation-PHP Form sanitization-PHP Form URL/E-mail –Basics of ComputerGraphics-CreatingImage-ManipulatingImage-UsingTextinImage-WatermarkstoImage.

MODULEIII JAVASCRIPTWITHPHP 9JavaScript-Variables,datatypes,expressions,operators;Conditional,iteration,statements;Functions;Arrays;DOM,Events,EventsHandling;Client-sidePersistence;Object-OrientedJS;Ajax.OverviewofJavaScriptLibraries/Frameworks.

MODULEIV DATABASECONNECTIVITY 9Session and Cookie – Introduction to Session Control, SessionFunctionalityWhat is a Cookie, Setting Cookies with PHP. Using Cookies withSessions,Deleting Cookies, Registering Session variables, Destroying thevariablesandSession.DatabaseManagement–IntroductiontoMySQL–MySQLCommands–MySQL Database Creation –Connecting MySQL and PHP –QueryingMySQLDatabasewithPHP.

MODULEV WEBDEVELOPMENTFRAMEWORKS 9

Web Development Frameworks –Introduction – Model View Controller –PHPframework–PHP XML Parsers-PHP XML Expat-PHP XML DOM-PHP Mail–Pilot Project.


TEXTBOOKS:1. Robin Nixon, Learning PHP, MySQL & JavaScript 5e: With jQuery,

CSS&HTML5(LearningPHP,MYSQL,Javascript,CSS&HTML5),5thedition,OReillyPublishers,USA,2018.

2. Luke Welling, Laura Thomsan, PHP and MySQL WebDevelopment(Developer's Library), Pearson Education Publishers, 5th

edition, US,2017.3. MikeMcGrath,PHP&MySQLineasysteps:CoversMySQL8.0,2nd

edition, InEasyStepsLimitedPublishers,India,2018.REFERENCES:

1. StevenHolzner,PHP:TheCompleteReference,McGrawHillEducation,India,2017

2. JohnDuckett,PHP&MySQL:Server-sideWebDevelopment,WileyPublishers, 1stedition,USA,2021.

COURSEOUTCOMES:CO1:Implementthebasicsof PHPprogramming.

CO2:Applythestudiedformdesigningfeaturestodevelopforms usingHTML.

CO3:IdentifytheroleofJavascript indeveloping webpages.

CO4:Familiarizewithsessions,cookiesandgainknowledgeofestablishingdatabaseconnectivitywith PHP.

CO5:Developaweb pageusingPHPanddeployit.

Board ofStudies(BoS):15thBoSofCA heldon22.06.2021


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https://www.amazon.in/s/ref%3Ddp_byline_sr_book_1?ie=UTF8&field-author=Mike%2BMcGrath&search-alias=stripbooks


SDG4:QualityEducation–Ensureinclusiveandequitablequalityeducationandpromotelifelonglearning opportunitiesfor allDesigning and programming skills taught in this course with respect tothecourseoutcomesimprovesthesoftwaredevelopmentskillofthelearner.Thiswouldhelpthelearnerindevelopingsoftwareusinghisknowledgeinthewebdevelopment.

CADY362 WEBMINING L T P C

3 0 0 3SDG:9

COURSEOBJECTIVES:

COB1:FocusonthebasicsofInformationretrievalandWebsearch.

COB2:ExploresocialmediadatausingappropriatewebminingtechniquesandmaindesignprinciplesofWebcrawlers.COB3:FamiliarizethefundamentalsforstructuredataextractionandtechniquesforprocessingWebdocuments.COB4:Learntoextractandprocessopinionsandsentimentsintextcontents.COB5:Illustratethevariousaspects of webusagemining.

MODULEI INTRODUCTION 9A brief history of the Web and the Internet – Web Data Mining –Informationretrieval and Web search: Information retrieval Models-RelevanceFeedback-TextandWebpagePre-processing–InvertedIndex –Latent SemanticIndexing–WebSearch– Meta-Search–WebSpamming.

MODULEII WEBLINKMINING 9Social Network Analysis – Co-Citation and Bibliographic Coupling –PageRank–HITS–CommunityDiscovery.Webcrawling–Basics–ImplementationIssues–UniversalCrawlers–FocusedCrawlers–Evaluation–CrawlerEthicsandConflicts.MODULEIII STRUCTURED DATA EXTRACTION AND

INTEGRATION9

WrapperGeneration:Preliminaries-WrapperInduction-Instance-BasedWrapperLearning ·- Automatic Wrapper Generation - String Matching andTreeMatching -Multiple Alignment - Building DOM Trees - Extraction Basedon aSingle and Multiple List Pages. Information Integration:IntroductiontoSchemaMatching– Pre-Processing–Schema-Domain andInstance LevelMatching–CombiningSimilarities.MODULEIV WEBOPINIONMINING 9

TheProblemofOpinionMining–DocumentSentimentClassification–Aspect-basedOpinionMining–OpinionSearchandRetrieval–OpinionSpamDetection.MODULEV WEBUSAGEMINING 9DataCollectionandPre-Processing-DataModelingforWebUsageMining–WebUsagePatternAnalysisandDiscovery-RecommenderSystemsandCollaborativeFiltering–QueryLogMining.


TEXTBOOKS:1. BingLiu,“WebDataMining:ExploringHyperlinks,Contents,andUsageDat

a”,Springer,Second Edition,2012.2. SoumenChakrabarti,“MiningtheWeb–DiscoveringKnowledgefrom

HypertextData”,Elsevier,2002.REFERENCES:

1. GuandongXu,YanchunZhang,LinLi,“WebMiningandSocialNetworking-TechniquesandApplications”,Springer,2017.

2. FedericoAlberto Pozzi,Elisabetta Fersini,EnzaMessinaandBingLiu,“SentimentAnalysisinSocialNetworks”,MorganKaufmann,2017.

COURSEOUTCOMES:CO1:AcquireknowledgeonInformationRetrieval methods

CO2:Analyzesocialnetworks anddevelopschemestocrawl,organizeandindextheweb data

CO3:Designwrappersandextractdatafromtheweb

CO4:Understandthechallengesandlimitationsofopinionminingtechniques

CO5:Identifyandextractusagepatternsinlogdata



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SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThetechniquestaughttothelearnerinthiscoursewillhelpthemdiscoverpatternsfromthe WorldWideWeb.ThepatternsdiscoveredcanbeusedbyE-CommerceindustriestoanalyzetrendsandfacilitatemoreE-servicesthat

areeffective,enhancewebdesign,introducepersonalizationserviceandprovidemoreeffective browsing.

CADY363DATAMININGANDDATAWAREHOUSING

L T P C

3 0 0 3

SDG:9

COURSEOBJECTIVES:

COB1:UnderstandthebasicconceptsofDatamining,dataqualityandtechniqu

esfor preprocessingofdata.COB2:Explorethekindsofpatternsthatcanbediscoveredbyassociationrulemi

ning.COB3:Imparttheknowledgeonhowtoimplementclassificationmodelsandal

gorithms.COB4:Learntosegregategroupswithsimilartraits.

COB5: Illustratethevariousaspectsofmodelinganddesignof datawarehouses.MODULEI INTRODUCTION 9Data Mining: Introduction- Kinds of Data and Patterns–Major issues indatamining- Data Objects and attribute types –Statistical description of data-Measuring data similarity and dissimilarity. Data preprocessing:Overview-Datacleaning-Dataintegration–DataReduction-Datatransformationanddiscretization.MODULEII ASSOCIATIONRULEMINING 9Basicconcepts-Frequentitemsetminingmethods:Apriorialgorithm-Apatterngrowth approach for miningfrequent item sets—Pattern evaluation methods-Mining multilevel,multi-dimensional space-constraint based frequent patternmining.MODULEIII CLASSIFICATION 9Basicconcepts-DecisionTreeInduction-BayesClassificationMethods–RuleBased Classification-Model evaluation and selection - Techniques to

improveclassificationaccuracy–SupportVectorMachines-Classificationusingfrequentpatterns.MODULEIV CLUSTERING 9Cluster analysis- Partitioning methods- Hierarchical methods- Densitybasedmethods–Gridbasedmethods–Model-BasedClusteringMethods–ClusteringHighDimensionalData-ConstraintbasedClusterAnalysis–Introductiontooutlier analysis-Data MiningApplications.

MODULEV DATAWAREHOUSING 9

Datawarehouse-basicconcepts-Modeling–Designandusage-Implementation–DatageneralizationbyAttribute-orientedinductionapproach–Datacubecomputationmethods.


TEXTBOOKS:1.JiaweiHanandMichelineKamber,“DataMiningConceptsand

Techniques”,3rdEdition,Elsevier,2012.REFERENCES:

1. MargretH.Dunham,”DataMining:IntroductoryandAdvancedTopics”,17thEdition,PearsonEducation,2013.

2. CharuC.Aggarwal,“DataMining:TheTextbook”,KindleEdition,Springer,2015.

3. GuptaG.K.,“IntroductiontoDataMiningwithCaseStudies”,EasternEconomyEdition,Prentice Hall ofIndia,2006.

4. ParteekBhatia,“DataMiningandDataWarehousing-PrinciplesandPracticalTechniques”,CambridgeUniversityPress,2019.

COURSEOUTCOMES:CO1:Identifythekeyprocessesofdataminingandknowledgediscoveryprocess.CO2:Generatepatternsusing associationrulemining inlargedatasets.

CO3:Compareandcontrastvariousclassifiers.

CO4:Clustersimilarobjectsinmultivariatedatasets.

CO5:Designadatawarehousewithdimensionalmodeling.



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SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovation

The techniques taught to the learner in this course will help themsignificantlyimprovetheirunderstandingandanalyzingcapabilitytoutilizeanysortofdataeffectivelytoprovidemeaningfulbusinessinsights.Theinsightsextractedcanbeused bybusinessesformaking strategicdecisions.

CADY364 DATAANALYTICSANDVISUALIZATION

L T P C

SDG:4 3 0 0 3

COURSEOBJECTIVES:

COB1: Summarizeandpresentdatainmeaningfulways.

COB2: Selecttheappropriateclusteringmethoddependingonthedataandinformation.COB3:Analyzetheconcept ofregression.

COB4: Understandandverifytheunderlyingassumptionsandanalysisonlabeleddata.COB5: Conduct,present, andinterpretcommonstatisticalanalysesusing R with basic theory and practical implementation details tosolverealworld problems.

MODULEI INTRODUCTIONTODATAANDMACHINELEARNING

9

Importance of analytics and visualization with data abundance- Reviewofprobability-statisticsandrandomprocesses-Estimationtheory-Machinelearning-supervisedandunsupervisedlearning-gradientdescent-overfitting,regularization.MODULEII UNSUPERVISED LEARNING &

EVALUATIONMETHODS9

Clusteringtechniques:K-means,Gaussianmixturemodelsandexpectation-maximization-agglomerativeclustering-evaluationofclustering-Randindex,mutualinformationbasedscores,Fowlkes-Mallowsindex.MODULEIII SUPERVISEDLEARNING&REGRESSION 9Supervised classification methods: K-nearest neighbor- naiveBayes-logisticregression- decision tree- support vector machine- Introductionto artificialneuralnetworks(ANNs)-Regression:Linearmodels-ordinaryleastsquares-ridgeregression-LASSO-GaussianProcessesregression.MODULEIV ANALYSINGDATA 9Normaldistribution-Sampling-TheCentralLimitTheorem-One-WayAnalysisofVariance-F-testforANOVA-EvaluatingGroupDifferences-TypeIandTypeIIErrors-IssueswithMultipleComparisonsAnalysisforproportions-Analysisforproportions-Two-SampleTestsforProportionsMODULEV DATAVISUALIZATION 9Basic principles- categorical and continuous variables- exploratorygraphicalanalysis- Creating static graphs- animated visualizations- loops,GIFs

andVideos-DatavisualizationinPythonandRProgramming-DataStructuresandexamples.


TEXTBOOKS:1. NathanYau,“DataPoints:VisualizationThatMeansSomething”,Wileypubl

ications, 5 April2013.(ISBN:978-1-118-46219).2. ChristopherBishop,“PatternRecognitionandMachineLearning”,Springer

,1sted.2006.(ISBN-13:978-0387-31073-2).3. GabrielA.Canepa,“WhatyouneedtoknowaboutMachineLearning”,

September2018.REFERENCES:

1. Jaejin Hwang, Youngjin Yoon, “Data Analytics and VisualizationinQualityAnalysisusingTableau”,publishedbyTaylor&FrancisGroup,LLC,2021.

2. ClausO.Wilke,“FundamentalsofDataVisualization”, O'ReillyMedia,1stedition,18March2019.(ISBN-10:1492031089)

3. JakeVanderPlas,“PythonDataScienceHandbook”,O'ReillyMedia,Inc.,2016.(ISBN:9781491912058).

COURSEOUTCOMES:CO1:Thestudentwillgaindetailedknowledgeaboutthegoalandtechniquesof thedataanalysisandvisualizationprocess.CO2:Thestudentwillbeabletobuildmodelsfordatathathasnolabeledtrainingdataavailable:UnsupervisedlearningCO3:Applysuitablemachinelearningand/orvisualizationtechniquesandanalyzetheresultsobtainedtoenableoptimaldecision-making.CO4:The student will understand the steps in characterizing andunderstandingdataandwillbeabletobuildeffectivepredictivemodels.CO5:ThestudentwillbeabletousesoftwareapplicationsandabletobuildmodelsinRProgramming.Board ofStudies(BoS) :15thBoSofCAheldon22.06.2021


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SDG4:EnsureinclusiveandequitablequalityeducationandpromotelifelonglearningopportunitiesforallTheunderstandingofusingvisualizationanddataanalyticsfosterscontinuouslearningandsustainablequalityeducationthroughprogramminglanguage.

CADY365 SOCIALMEDIAANALYTICS L T P C

3 0 0 3SDG: 9

COURSEOBJECTIVES:COB1:Understandhumanbehaviourinsocialwebandrelatedcommunities.

COB2:Familiarizethelearnerswiththetoolsof socialnetworkanalytics.

COB3:Learnknowledgerepresentationusing Sentimentanalysis.

COB4:Implementthesocialmediaanalyticstools.

COB5:IntegratetheManagementDomainexpertiseindesigningtheDSStoprovide BusinessIntelligence.

MODULEI INTRODUCTION TO SOCIALMEDIAANALYTICS

9

Introduction to Social Media Analytics (SMA): Social media

landscape,NeedforSMA;SMAinSmallandlargeorganizations;Applicationof

SMA.Network fundamentals and models: The social networks perspective

-nodes,tiesandinfluencers,socialnetworkandwebdataandmethods.

MODULEII SOCIALNETWORKANALYSIS 9Introduction to Social Network Analysis (SNA): definition and origin,

corefeatures of the SNA, Foundation of social network analysis. Networks:

nodes,edges,adjacencymatrix,oneandtwo-modenetworks,nodedegree,central

ity,betweenness,reach,cliques,andpaths.GraphMining:Communitydetection,

Clustering,Communitystructure,Modularity,Overlappingcommunities.MODULEIII MODELLING 9Predictivemodeling:link/attributeprediction.InfluenceinSocialnetworks.Sentiment

Analysis,RecommendationinSocialNetworks:Collaborative

Filtering,andContentbasedRecommendationSystems.MODULEIV MINING COMMUNITIES IN SOCIAL

NETWORKS9

ExtractingevolutionofWebCommunityfromaSeriesofWebArchive,detectingcom

munitiesinsocialnetworks,Definitionofcommunity,evaluatingcommunities,Meth

odsforcommunitydetectionandmining,Applicationsof

communityminingalgorithms.

MODULEV VISUALIZATIONANDCASESTUDIES 9

SocialNetworksVisualization,ProcessingandVisualizingData,InfluenceMaximization,Socialnetworkanalysiscasestudies:Twitter,Facebook,Last.fm,DBLP,andIMDB,Pilotproject.


TEXTBOOKS:1. GoharF.Khan

“SevenLayersofSocialMediaAnalytics:”MiningBusinessInsightsfromso

cialmediaText,Actions,Networks,Hyperlinks,Apps,Search

Engine,andLocationData-2015.

2. Luke Welling, Laura Thomsan, PHP and MySQL Web

Development(Developer's Library), Pearson Education Publishers, 5th

edition, US,2017.

3. MathewA.Russel“MiningtheSocialWeb:AnalyzingDatafromFacebook,T

witter,LinkedIn,andOtherSocialMediaSites,First

edition, Jan201-.REFERENCES:

1. TracyL.Tuten,MichaelR.Solomon“SocialMediaMarketing”,SAGEPublic

ations Ltd,2015.

2. IanMcCulloh,HelenArmstrongandAnthonyJohnson,“SocialNetworkAna

lysis withApplications”,WileyPublications,2013.

3. ChristinaPrell,“SocialNetworkAnalysis:History,TheoryandMethodology”, 1stEdition, SAGEPublicationsLtd, 2012.

COURSEOUTCOMES:CO1:Predicthumanbehaviourinsocialwebandrelatedcommunities.

CO2:Applystatisticalmodels inrealtimeapplications.

CO3:RepresentknowledgeusingSentimentanalysis.

CO4:Makebetterbusinessdecisions byleveragingsocialmediadata.

CO5:Applyvisualizationtechniquesinsocialnetworks.



https://www.pdfdrive.com/mining-the-social-web-analyzing-data-from-facebook-twitter-linkedin-and-other-social-media-sites-e159158343.html

https://www.pdfdrive.com/mining-the-social-web-analyzing-data-from-facebook-twitter-linkedin-and-other-social-media-sites-e159158343.html

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SDG 9: Industry, Innovation and Infrastructure – Build resilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovation.

TheSocialmediaanalyticshelptheleanerstoinculcatethetoolsformakingbusin

ess decisions and to implement real time projects. Social media

dataanalysishelpsthecompaniesforbetterunderstandingoftheneeds,expecta

tionsoftheircustomers,improvetheefficiencyofcustomerservice,marketresear

chcarriedoutonsocialchannelsandincreasetheir competitiveintelligence.

CADY366 HEALTHCAREDATAANALYTICS

L T P C


COB1: This course aims to equip students with highly demandedhealthanalytics skills to select, prepare, analyze, and easilyinterprethealthdata.

COB2:Thiscoursealsogivessoundknowledgeonstatisticalviewstoevaluatetheoperationaldatatoimproveoutcomesinthecurrenthealthcarejobmarket.

COB3:ThiscoursewillexplaintheHealthcaredataanalysistechniquesandtheiroperations.

COB4:Thiscourse willillustratethe importanceofHealth care analyticswithreal-timecasestudies.

COB5:GraduatesoftheProgrammewillbeabletodothedataanalysisusingadvancedtechniquestosolvethecomplexityofhealthdata.

MODULEI ANINTRODUCTIONTOHEALTHCAREDATAANALYTICS

9

Introduction-HealthcareintheDigitalEra-Thesignificanceofpredictiveanalytic inhealth care- Electronic Health Records -Health Data Overview-Clinical Dataanalysis (CAD)- Key Findings in CDA. The opportunitiesandchallengesofdataanalyticsinhealthcare-Sensitivityofcaredecisions-Problematicdataconventions.MODULEII BASICTECHNIQUESANDMETHODSUSED

INCLINICALDATAANALYSIS9

Health data analytics methodology - Data Categorization-DataPreprocessingandMissingDataImputation-FeatureExtractionandSelection-LinearRegression-Evaluation and Validation- Brier Score - AccuracyandotherEvaluationMetricsBasedonConfusionMatrix–ROC (ReceiverOperatingCharacteristic)Curve-C-index.MODULEIII ADVANCED DATA ANALYTICS FOR

HEALTHCARE9

SupervisedTechniques-UnsupervisedTechniques-ExampleApplications.DecisionTreesArtificialNeuralNetworks-CostSensitiveLearning-AdvancedPredictionModelsMultipleInstanceLearning-ReinforcementLearning.MODULEIV MANAGEMENTANDIT CHALLENGESIN

HEALTHCARESECTOR9

.HealthcareinformationsystemStandards-Securityofhealthcareinformationsystems-Organizinginformationtechnologyservices-Efficiencyofoperational

Management-ITgovernanceandmanagement.Management’sroleinmajorITinitiatives-Assessingandachievingvalueinhealthcareinformationsystems.MODULEV CASESTUDY–HEALTHDATAANALYTICS 9HealthcareRepositoryOverview-RepositoriesDataUnderstandingbasedontheDiseaseSelection-Datasetfetching-DataPreprocessing-Techniques Implementation- Modelselection-Validation-Resultsanddiscussion.


TEXTBOOKS:1. HEALTHCARE ANALYTICS, From Data to Knowledge to

HealthcareImprovement by John Wiley & Sons, Inc., Published byJohn Wiley&Sons,Inc.,Hoboken,NewJerseyPublishedsimultaneouslyinCanada,2016.

2. Machine Learning and AI for Healthcare ISBN-13 (pbk):978-1-4842-3798-4ISBN-13(electronic):978-1-4842-3799-1,Copyright©2019byArjunPanesar,2019.

3. Healthcare,dataanalytics. Edited by. Chandank. Reddy.Detroit,Michigan, USA. Charu C. Aggarwal. IBM T. J. WatsonWayneState University2019.

4. KarenAWager,FrancesWickhamLee,JohnPGlaser,“ManagingHealth Care Information Systems: A Practical Approach forHealthCare Executives”,JohnWiley,2ndedition2009.

REFERENCES:1. Marion J. Ball, Charlotte Weaver, Joan Kiel,” Healthcare

InformationManagement Systems: Cases, Strategies, and Solutions”,Springer,2010,3rd.edition

2. RudiVanDeVeldeandPatriceDegoulet,“ClinicalInformationSystems:AComponentbasedapproach”,Springer 2005.

3. Kevin Beaver, Healthcare Information Systems, Second editionBestPractices,CRC Press,2002.

4. ChallengesandTrendsinclinicalDataAnalytics,September2020,DOI:10.46243/jst.2020.v5.i4.pp348-360

COURSEOUTCOMES:Studentswhocompletethis coursewillbeabletoCO1:DemonstratetherolesthatdataanalysesserveintheDecisionsupportsystemIdentifyandreferencesourcesofpublic healthdataandinformation.

CO2:Examinetheaccuracy,integrity, andcomparabilityofhealthdata.CO3:Interpretresultsofdataanalysesfoundinpublic healthstudiesandresearch.

CO4:Applythegraphicalanddescriptivetechniquesandsuitablearchitectureondesignandimplementationtosummarizepublichealthdata.

CO5:Usecurrenttechniquesandtools necessaryfor complexcomputing

practices.BoardofStudies(BoS):15thBoSofCAheldon22.06.2021


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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainable,industrializationandfosterinnovationDesign and development skills taught in this course for the learnerswithrespect to the course outcomes are measurable and useful in improvingtheClinicalsupportdecision-makingcapacityofthelearner.Asthefutureindustrialmanagementpersonnel,the learnerwouldmake decisionswiththehelpofcomputationalintelligencebasedDataanalyticstechniques.

CADY367 RPROGRAMMING L T P C

3 0 0 3SDG:9

COURSEOBJECTIVES:

COB1:UnderstandthebasicsinRprogramming

COB2:ToknowhowRprogrammingusedforBig Dataanalytics

COB3:TolearntheneedforTextProcessing

COB4:UnderstandandabletoknowtheRprogrammingfromastatisticalapproach

COB5: Learnandanalyzing Rwithotherlanguages

MODULEI INTRODUCTION 9IntroducingtoR–RDataStructures–HelpfunctionsinR–Vectors–Scalars–Declarations–recycling–CommonVectoroperations–Usingallandany–Vectorized operations – NA and NULL values – Filtering – Vectorized if-thenelse–VectorEquality –VectorElementnames.

MODULEII MATRICES,ARRAYSANDLISTS 9Creating matrices – Matrix operations – Applying Functions to MatrixRowsandColumns–Addinganddeletingrowsandcolumns–Vector/MatrixDistinction – Avoiding Dimension Reduction – Higher Dimensional arrays–lists–Creatinglists–Generallistoperations–Accessinglistcomponentsandvalues – applying functions tolists–recursive list.

MODULEIII DATAFRAMES 9Creating Data Frames – Matrix-like operations in frames – MergingDataFrames – Applying functions to Data frames – Factors and Tables –factorsandlevels–Commonfunctionsusedwithfactors–Workingwithtables-Otherfactors and table related functions - Control statements – ArithmeticandBoolean operators and values – Default values for arguments -ReturningBoolean values – functions are objects – Environment and Scopeissues –Writing Upstairs - Recursion – Replacement functions – Tools forcomposingfunctioncode–Mathand Simulations in R.

MODULEIV OBJECTORIENTEDPROGRAMMINGINR 9S3Classes–S4Classes–Managingyourobjects–Input/Output–accessingkeyboard and monitor – reading and writing files – accessing the internet–StringManipulation–Graphics–CreatingGraphs–CustomizingGraphs–Saving graphstofiles –Creatingthree-dimensionalplots.

MODULEV INTERFACING 9InterfacingRtootherlanguages–ParallelR–BasicStatistics–LinearModel

–GeneralizedLinearmodels–Non-linearmodels–TimeSeriesandAuto-correlation –Clustering.

L-45;TOTALHOURS–45

TEXTBOOKS:1. JaredP.Lander,“R

forEveryone:AdvancedAnalyticsandGraphics”,Addison-WesleyData&AnalyticsSeries,2013.

2. NormanMatloff,“TheArtofRProgramming:ATourofStatisticalSoftwareDesign”,NoStarchPress,2011.

REFERENCES:1. MarkGardener,“BeginningR–TheStatisticalProgrammingLanguage”,Wi

ley,2013.2. RobertKnell,“IntroductoryR:ABeginner'sGuidetoDataVisualization,Stati

sticalAnalysisandProgramminginR”,AmazonDigitalSouthAsiaServices Inc,2013.

COURSEOUTCOMES:CO1:UnderstandthebasicsinRprogrammingintermsof constructs, control

statements,stringfunctionsCO2:UnderstandtheuseofRfor BigDataanalytics

CO3:LearntoapplyRprogrammingforTextprocessing

CO4:Able to appreciate and apply the R programming from astatisticalperspective

CO5:Learntomakeanalysiswithotherlanguages

BoardofStudies(BoS):15hBoSofCAheldon22.06.2021


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SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationInlearningRProgrammingconcepts,studentswillbeabletoadaptwithcloudenvironmentandtocomparewithotherprogramminglanguagesasliketechnicalsurveyintheirapplicationdevelopment.

CADY368 DECISIONSUPPORTSYSTEM L T P C


COB1: Introduce the concepts of Decision support system andDecision-making.

COB2:Learn thedevelopment methodologiesofComputerizedDecisionSupportsystem.

COB3:ComprehendtheprocessmetricsinvolvedinDesigningCRMSolutionmethodologies.

COB4:UnderstandtheprocessmetricsinvolvedinDesigningSCMSolutionmethodologies.

COB5:IntegratetheManagementDomainexpertiseindesigningtheDSStoprovideBusinessIntelligence.

MODULEI INTRODUCTION 9Decision Support System: Definition - Configuration - CharacteristicsandCapabilitiesofDSS-ComponentsofDSS–Subsystems:DataManagement - Model Management- User Interface -Knowledge-BasedManagement-DSSClassification.DecisionmakingSystem:Definition-Modelsandsupport–DecisionmakingPhases.MODULEII DESIGNANDDEVELOPMENTOFDECISION

SUPPORTSYSTEM9

Introduction-TraditionalSystemDevelopmentLifecycle-AlternateDevelopmentMethodologies. Prototyping: DSS Development Methodology.DSS -Technology Levels and Tools - Development Platforms -DevelopmentToolSelection–Team-DevelopedDSS-EndUserDevelopedDSS–ComputerizedDecisionSupportandFrameworkforDecisionsupport-ComputationalIntelligenceMODULEIII CUSTOMERRELATIONSHIPMANAGEMENT 9Introduction–Marketing–CommunicationwiththeCustomer-Valueofcustomer –CRMTechnologies –CRMSoftware –CRMProblemsandissues–MeasuringCRMSuccess-CRMTools-AnalyticalCRM–OperationalCRM-e-CRMSolutionsMODULEIV SUPPLYCHAINMANAGEMENT 9SupplyChainDefinition–Benefits–Components–Supplychainandvaluechain–Decisionmakingandthesupplychain–supplychainproblemsandsolutions-ITinSupplyChain-AgileSupplyChains–Supplychainforecasting.MODULEV CASESTUDY 9

IntelligentDecisionsupportsystem:BusinessIntelligence-Healthcare–EnvironmentalEcosystem–CRMsystem-HumanResourceManagementprocess–Marketresearch –FinancialDSS.


TEXTBOOKS:1. EfraimTurbanandJayE.Aronson,DecisionSupportSystemandIntelligent

Systems,PrenticeHallInternational, 7thEdition2011.2. JanatShah,SupplyChainManagement–TextandCases,PearsonEducati

on,20163. FrancisButtle,CustomerRelationshipManagement:Concepts&Tools,Els

evier,20044. SpragueR.H.JrandH.J.Watson:DecisionSupportSystems,4th

Edition,PrenticeHall,1996.REFERENCES:

1. George M. Marakas, Decision Support Systems, 2nd Edition,PearsonEducation,2005

2. JanakiramanV.SandSarukesiK,DecisionSupportSystems,PrenticeHallofIndia,6th,Edition,2009.

COURSEOUTCOMES:CO1:Createthecomputerizeddecisionsupportsystem.

CO2:SelecttheappropriatetoolandmethodologiestoimplementtheDSS

CO3:ImplementCRMDecision SupportSystem

CO4:DesignanddeploySCMDecisionSupportSystem

CO5 : Provide Business Intelligence through Computerized DSS fordifferentverticals



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SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationDesign and development skills taught in this course for the learnerswithrespect to the course outcomes are measurable and useful in improvingthedecision-making capacity of the learner. As the future industrialmanagementpersonnel,thelearnerwouldmakedecisionswiththehelpofcomputationalintelligencebaseddecisionsupport systems.

CADY369 PREDICTIVEANALYTICS L T P C

SDG: 9 3 0 0 3COURSEOBJECTIVES:

COB1:Tolearn,howtodevelopmodelstopredictcategoricalandcontinuousoutcomes,usingtechniquessuchasneuralnetworks,decisiontrees,logisticregression,supportvectormachinesandBayesiannetworkmodels.

COB2:Toknowtheuseofthebinaryclassifierandnumericpredictornodes toautomatemodelselection.

COB3:Toknowaboutthevarious modeladvantageand disadvantage

COB4:Tostudythecombinationof twomodelsforimproving prediction

COB5:Tolearnandachievereliableresultsformanagingandcoordinatinginanalyticalprocess.

MODULEI INTRODUCTIONTODATAMINING 9Introduction,whatisDataMining?KeyConceptsofDatamining,TechnologiesUsed, Data Mining Process, KDD Process Model, CRISP–DM,Miningonvariouskindsofdata,ApplicationsofDataMining,ChallengesofDataMining.MODULEII INTRODUCTIONTOANALYTICS 9WhydoweneedAnalytics?-Analyticsindecisionmaking-PowerofAnalytics-PredictiveAnalytics-AnalyticsinFinance,Manufacturing,Healthcare,IT,Telecom,Supplychain-DigitalAnalytics,PrescriptiveanalyticsMODULEIII DATA UNDERSTANDING AND

PREPARATION9

Introduction,Readingdatafromvarioussources,Datavisualization,Distributionsandsummarystatistics,Relationshipsamongvariables,ExtentofMissing Data.Segmentation, Outlier detection, Automated DataPreparation,Combiningdatafiles,AggregateData,DuplicateRemoval,SamplingDATA,DataCaching,Partitioningdata,MissingValues.MODULEIV MODELDEVELOPMENT&TECHNIQUES 9Data Partitioning, Model selection, Model Development Techniques,NeuralNetworks,DecisionTrees,LogisticRegression,Discriminantanalysis,SupportVectorMachine,BayesianNetworks,LinearRegression,CoxRegression,Associationrules.MODULEV MODELEVALUATIONANDDEPLOYMENT 9Introduction,ModelValidation,RuleInductionUsingCHAID,AutomatingModelsforCategoricalandContinuoustargets,ComparingandCombining

Models,EvaluationChartsforModelComparison,MetalevelModeling,DeployingModel,AssessingModelPerformance,UpdatingaModel.


TEXTBOOKS:1. Dr. Anasse Bari, Mohamed Chaouchi, Tommy Jung, (2016)

PredictiveAnalytics For Dummies,2nd edition,(ISBN-108126567937)2. Eric Siegel,(2016)PredictiveAnalytics:ThePowertoPredictWhoWill

Click,Buy,Lie,orDie 2ndedition,Wiley(ISBN-13-978-1119145677)REFERENCES:

1. Predictive Analytics for Business Strategy - Reasoning from DatatoActionable Knowledge 1St Edition by Jeffrey T Prince andAmarnathBose,McGrawHill,2020.

2. Forecasting&PredictiveAnalyticswithForecastxtm7ThEditionbyBarry Keating and J Holton Wilson and Shovan Chowdhury andJohnGaltSolutionsandINC,McGrawHill,2020

COURSEOUTCOMES:CO1:Students havemultiplestrategiesforconstructingmodelsandcanusedifferentmeasuresofmodelfitandperformancetoassessmodels.CO2:Comparetheunderlying predictivemodellingtechniques.

CO3:Selectappropriatepredictivemodellingapproachesindifferentkindofproblems.

CO4:Applypredictivemodelling approachesusinga suitablepackagesuchasSPSSModeler.CO5:Manageandcoordinatetheanalyticalprocess.



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https://www.amazon.com/Dr-Anasse-Bari/e/B08PNKM5LS/ref%3Ddp_byline_cont_book_1

https://www.amazon.com/Mohamed-Chaouchi/e/B00IZ2R986/ref%3Ddp_byline_cont_book_2

https://www.amazon.com/s/ref%3Ddp_byline_sr_book_3?ie=UTF8&field-author=Tommy%2BJung&text=Tommy%2BJung&sort=relevancerank&search-alias=books

https://www.amazon.in/Eric-Siegel/e/B00BFQ3STS/ref%3Ddp_byline_cont_ebooks_1

SDG9:Industry,InnovationandInfrastructure–Buildresilientinfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovationThelearnerwillbeabletodetectpatternsthroughvastamountsofhistoricaldatamuchmorequicklyand accuratelyandcantakebetterdecisiontothefutureIndustrialandinfrastructuralprojects.

CADY370 INTERNETOFTHINGS L T P C

3 0 0 3SDG: 9

COURSEOBJECTIVES:

COB1:TounderstandSmartObjectsandloTArchitectures

COB2:TolearnaboutvariouslOT-relatedprotocols

COB3:TobuildsimpleIoTSystemsusingArduinoandRaspberryPi.

COB4:Tounderstanddata analytics andcloudinthecontextofIoT

COB5:TodevelopIoTinfrastructureforpopularapplications

MODULEI INTRODUCTIONTOIoT 9InternetofThings-PhysicalDesign-LogicalDesign-IoTEnablingTechnologies -IoT Levels & Deployment Templates - Domain Specific IoTs-IoTandM2M-IoTSystemManagementwithNETCONF-YANG-IoTPlatformsDesignMethodology.MODULEII IoTARCHITECTURE 9M2Mhigh-levelETSIarchitecture-IETFarchitectureforIoT-OGCarchitecture-IoTreferencemodel-Domainmodel-informationmodel-functionalmodel-communicationmodel-IoT referencearchitecture

MODULEIII IoTPROTOCOLS 9ProtocolStandardizationforIoT–Efforts–M2MandWSNProtocols–SCADAandRFID Protocols – Unified Data Standards – Protocols – IEEE 802.15.4–BACNetProtocol–Modbus–ZigbeeArchitecture–Networklayer–6LowPAN- CoAP- SecurityMODULEIV BUILDINGIoT WITH RASPBERRY PI &

ARDUINO9

BuildingIOTwithRASPERRYPI-IoTSystems- LogicalDesign usingPython– IoT Physical Devices & Endpoints - IoT Device -Building blocks-RaspberryPi -Board - Linux on Raspberry Pi - Raspberry Pi Interfaces-ProgrammingRaspberryPi with Python-OtherIoT Platforms -Arduino.

MODULEV CASE STUDIES ANDREAL-WORLDAPPLICATIONS

9

Real world design constraints - Applications - Asset management,Industrialautomation,smartgrid,Commercialbuildingautomation,Smartcities-participatorysensing- DataAnalyticsforIoT–Software&ManagementToolsforIoTCloudStorageModels&CommunicationAPIs-CloudforIoT-AmazonWebServicesforIoT.


TEXTBOOKS:1. Bahga A, Madisetti V. Internet of Things: A hands-on approach.

Vpt;2014Aug 9.2. JanHoller,VlasiosTsiatsis, CatherineMulligan,Stamatis,

Karnouskos,StefanAvesand.DavidBoyle,"FromMachine-to-Machinetothe Internet of Things - Introduction to a New Age ofIntelligence",Elsevier,2014.

3. Olivier Hersent, David Boswarthick, Omar Elloumi , “The InternetofThings–Keyapplications andProtocols”,Wiley,2012.

4. DieterUckelmann,MarkHarrison,Michahelles,Florian(Eds),“ArchitectingtheInternetofThings”,Springer,2011.

5. HonboZhou,“TheInternetofThingsintheCloud:AMiddlewarePerspective”,CRCPress,2012

REFERENCES:1. Robert Barton, Patrick Grossetete, David Hanes, Jerome

Henry,Gonzalo Salgueiro, “IoT Fundamentals: NetworkingTechnologies,Protocols,andUseCasesfortheInternetofThings”,CISCOPress,2017.

2. Hanes D, Salgueiro G, Grossetete P, Barton R, Henry J.IoTfundamentals: Networking technologies, protocols, and use casesfortheinternetofthings.CiscoPress;2017May30.

3. PerryLea,“InternetofThingsforArchitects”,PACKT,2018.COURSEOUTCOMES:CO1:Developwebservicestoaccess/controlIoTdevices.

CO2:AnalyzevariousprotocolsforIoT

CO3:DesignaportableIoTusingRasperryPi

CO4:DeployanIoTapplicationandconnecttothecloud

CO5:Analyzeapplicationsof IoTinrealtimescenario



PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

CO1

CO2

CO3 L M M

CO4 L

CO5 M M H M


SDG9:BuildresilientInfrastructure,promoteinclusiveandsustainableindustrializationandfosterinnovation.Design and development skills taught in this course for the learnerswithrespect to the course outcomes are measurable and useful incollaboratingandtoincreasetheinterestofyoungpeopletoapplythetechnologyinreal-worldproblemswithhardware,sensors,ormachines.

Documents

MCA contents.docx - BSAbdur Rahman Crescent Institute of