PROGRAMME: M.Sc. COURSE : COMPUTER SCIENCE · computer science enthusiasts and provide them with the perfect amalgamation of theory as well ... Spanning Trees, Prim’s Algorithm,

DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science

PAGE NO: 1

Seva Mandal Education Society’s

Smt. Kamlaben Gambhirchand Shah Department of Computer Applications

under

Dr. Bhanuben Mahendra Nanavati College of Home Science (Autonomous)

NAAC Re-Accredited ‘A+’ Grade with CGPA 3.69 / 4

UGC Status: College with Potential for Excellence

‘Best College Award 2016-17’ adjudged by S.N.D.T. Women’s University

Smt. Parmeshwari Devi Gordhandas Garodia Educational Complex

338, R.A. Kidwai Road, Matunga, Mumbai - 400019. Tel: 24095792 Email: [email protected]

PROGRAMME: M.Sc. COURSE : COMPUTER SCIENCE

Program Objectives

This program will enable the students to:

1. Gain in-depth knowledge in the key areas of computer science and practice in emerging,

cutting edge Computational Technologies.

2. Develop software solutions to real world problems through Information Technological skills

with international standards and facilitate them to be outstanding professionals.

3. Contribute to scientific research by independently designing, conducting and presenting the

results of small-scale research.

4. Be a part of skilled manpower in the various areas of computer science such as Algorithm

Analysis and Design, Data warehousing and Mining, Software Engineering, Advanced

Computing technologies, Web-based Applications Development, and Data Science.

Program Outcome

The completion of the post-graduation programme:

1. Takes forward the knowledge gained by the students at the undergraduate level and provides

them with an advanced level of learning and understanding of the subject.

2. Provides students with higher educational degree of technical skills in problem solving and

application development.

3. Helps students to acquire an analytical and managerial skills to enhance employment potential.


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Program Specific Outcome

1. The main outcome of this programme is enhancement in the Technical and Analytical skills of

computer science enthusiasts and provide them with the perfect amalgamation of theory as well

as practical knowledge in the various thrust areas of the field.

2. The students will acquire broad knowledge in core areas of computer science, current and

emerging computing technologies.

3. The students also acquire a research oriented professional approach to provide sustainable

solution to real life problems which can be solved using computational technologies.

Eligibility

A Science Graduates in

o BSc. (Physics),

o BSc. (Maths.),

o BSc (Elect.),

o BSc. (IT),

o B.Sc. (CS) or

o BCA or

o any engineering graduate in allied subject from the recognized university

with an aggregate mark not less than 50% (Open Category) and 45%

(Reserved category).

Mathematics at 12th Level or 100 marks mathematics studied at graduation level

is minimum requirement.


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M.SC. (COMPUTER SCIENCE)

SYLLABUS

M.Sc. (COMPUTER SCIENCE) SEMESTER - I (FIRST YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int. Ext. Total

MCS101 Programming Concepts and Design,

Analysis of Algorithms 4 - 2 2 4 2

MCS102

Data Communication and

Networking

4 - 2 2 4 2

MCS103 Operating Systems 4 - 2 2 4 2

MCS 104 Software Engineering 4 - 2 2 4 2

MCSL105 Programming Concepts Lab

- 2 1 1 2 1

MCSL106 Networking Lab - 2 1 1 2 1

MCSL107 Software Testing Lab - 2 1 1 2 1

MCSL108 Advanced Web Technology Lab - 2 1 1 2 1

Total 16 8 24 -

SEMESTER-I

1 Credit=25 Marks

Total Credits = 24

Total Marks = 24*25=600


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M.Sc. (COMPUTER SCIENCE) SEMESTER - II (FIRST YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int.

Ext.

Total

MCS201 Mobile Communication and

Wireless Technology 4 - 2 2 4 2

MCS202 Data Analytics and Mining

4 - 2 2 4 2

MCS203 Research Methods and Statistical

Analysis 4 - 2 2 4 2

MCS204 Elective I 4 - 2 2 4 2

MCSL205 Data Analytics and Mining Lab

- 2 1 1 2 1

MCSL206 Statistics Lab - 2 1 1 2 1

MCSL207 Advanced Java Lab - 2 1 1 2 1

MCSL208 Advanced Python Lab - 2 1 1 2 1

Total 16 8 24 -

SEMESTER-II

1 Credit=25 Marks

Total Credits = 24


Elective – I

Course Code Course Nomenclature

MCS204A Distributed Systems

MCS204B Computer Graphics

MCS204C Advanced Python

MCS204D Natural Language Processing


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M.Sc. (COMPUTER SCIENCE) SEMESTER - III (SECOND YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int. Ext. Total

MCS301 Big Data Analytics and Machine

Learning 4 - 2 2 4 2

MCS302 Artificial Intelligence 4 - 2 2 4 2

MCS303 Mobile Application Development 4 - 2 2 4 2

MCS304 Information and Cyber Security 4 - 2 2 4 2

MCSL305 Big Data Analytics Lab -

2 1 1 2 1

MCSL306 Machine Learning Lab - 2 1 1 2 1

MCSL307 Mobile Application Development

Lab - 2 1 1 2 1

MCSL308 Ethical Hacking Lab - 2 1 1 2 1

Total 16 8 24 -

SEMESTER-III

1 Credit=25 Marks

Total Credits = 24



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M.Sc. (COMPUTER SCIENCE) SEMESTER - IV (SECOND YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int.

Ext.

Total

MCS401 Cloud Computing 4 - 2 2 4 2

MCS402 Elective II 4 - 2 2 4 2

MCSL403 Research Paper Writing - 4 2 2 4 -

MCSL404 Software Project - 12 6 6 12 -

Total 8 16 24 -

SEMESTER-IV

1 Credit=25 Marks

Total Credits = 24


Elective II


MCS402A Digital Image Processing

MCS402B Robotics

MCS402C Blockchain Technology

MCS402D Modeling and Simulation


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M.SC. (COMPUTER SCIENCE)

SYLLABUS

M.Sc. (COMPUTER SCIENCE) SEMESTER - I (FIRST YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int. Ext. Total

MCS101 Programming Concepts and Design,

Analysis of Algorithms 4 - 2 2 4 2

MCS 102


Networking

4 - 2 2 4 2


MCS 104 Software Engineering 4 - 2 2 4 2


- 2 1 1 2 1




Total 16 8 24 -

SEMESTER-I

1 Credit=25 Marks

Total Credits = 24


COURSE: PROGRAMMING CONCEPTS AND DESIGN, ANALYSIS OF ALGORITHMS CREDIT - 04 Objectives:

To introduce students to the programming concepts

To introduce the classic algorithms in various computer domains, and techniques for designing efficient algorithms.

To make the students aware of and well-trained in the use of the tools and Techniques of designing and analyzing algorithms.

Outcomes:

The course will help:

To prove the correctness and analyze the running time of the basic algorithms for those classic problems in various domains;


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To apply the algorithms and design techniques to solve problems

To appreciate the impact of algorithm design in practice

To analyze the complexities of various problems in different domains.

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total

MCS101 Programming Concepts and

Design, Analysis of Algorithms 4 - 2 2 4 2

Module

No.

Objective Content Evaluation

1

To introduce

students to

programming

concepts

Programming Concepts

Object Oriented Programming, Review of OOP -

Objects and classes, inheritance, polymorphism,

abstraction, Event driven programming, graphics

programming, event handling, generic programming –

generic classes – generic methods – generic code and

virtual machine

Assignment

(Marks–05)

2

To explain and use

various types of

analyses of

algorithms

To study the role of

available tools in

solving a problem;

Design strategies and Analysis of Algorithms Role of Algorithms in Computing: Algorithms as a

technology, Characteristics and building blocks of

Algorithm. Getting Started: Designing algorithms,

Well known Sorting algorithms (Insertion sort,

Bubble Sort, Selection Sort, Shell Sort, Heap Sort).

Divide-and-Conquer Technique: The maximum-

subarray problem, Integer Multiplication, Strassen’s

algorithm for matrix multiplication, the substitution

method for solving recurrences. Probabilistic

Analysis and Randomized Algorithms: The hiring

problem, Indicator random variables, Randomized

algorithms.

Analyzing algorithms, Growth of Functions: Some

Useful Mathematical Functions & Notations,

Asymptotic Functions & Notation.

Unit Test-1

(Marks-25)

3

To study and apply

the dynamic

programming and

greedy algorithms

for solving

problems.

Advanced Design

Dynamic Programming: Rod cutting, Elements of

dynamic programming, longest common

subsequence, The Problem of Making Change, Matrix

Multiplication Using Dynamic Programming. Greedy

Algorithms: An activity-selection problem, Elements

of the greedy strategy, Huffman codes, Minimum

Spanning Trees, Prim’s Algorithm, Kruskal’s

Algorithm, Dijkstra’s Algorithm.

Oral

Presentation

(Marks 10)

4

To study and apply

various graph

search techniques.

Graph Algorithms

Representations of graphs, Traversing Trees, Breadth-

first search, Depth-first search, Best-First Search &

Minimax Principle, Topological Sort. Single-Source

Shortest Paths: The Bellman-Ford algorithm, Single-

source shortest paths in directed acyclic graphs

Class Test

(Marks 10)


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EVALUATION:

1) On Four Modules of 50 marks

2) Final examination of 50 marks

3) Total marks = Internal 50 + External 50 = 100

TEXT BOOKS:

1. Richard F Gilberg, B. A. (2005). Data Structure A Pseudocode Approach with C. (Second

ed.). USA: Cengage Publisher.

2. Thomas H. Cormen, C. E. (2009). Introduction to Algorithms (Third ed.). New Delhi: PHI

Learning Pvt. Ltd.

REFERENCE BOOKS:

1. Bhargava, A. (n.d.). 1. Grokking Algorithms: An illustrated guide for programmers and

other curious people http://www.manning.com/bhargava. India: MEAP.

2. Lipschutz, S. (2014). Shaum‟s Outlines Data Structure. TMH.

3. Sanjoy Dasgupta, C. H. (2006). Algorithms. India: McGraw-Hill Higher Education.

4. T.Goodrich, M. (2010). Data Structures and Algorithms in C++. Wiley Publications.

_______________________________________________________________________________

COURSE: DATA COMMUNICATION AND NETWORKING

CREDIT - 04

Objectives:

To help students to get a grounding of network components and architecture.

To explore networking models.

To learn the way protocols are used in networks and their design issues.

Outcomes:

The students will be able to:

Comprehend the basic concepts of computer networks and data communication systems.

Analyse basic networking protocols and their use in network design

Explore various advanced networking concepts.

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total

MCS102


Networking

4 - 2 2 4 2


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Module

No.


1

To introduce to

basic concepts of

networking

Introduction to Networking

Internet and Intranet, Protocol layer and their services,

Network Applications like Web, HTTP, FTP and

Electronic Mail in the Internet, Domain Name System,

Transport-Layer Services, Multiplexing and

Demultiplexing, UDP, TCP, TCP Congestion Control,

Network Layer, Virtual Circuit and Datagram

Networks, Need of Router, The Internet Protocol (IP),

Routing Algorithms, Routing in the Internet.

Students will

be evaluated

by taking

viva.

(Marks 05)

2

To elaborate

network

virtualization

Network Virtualization

Need for Virtualization, The Virtual Enterprise,

Transport Virtualization-VNs, Central Services Access:

Virtual Network Perimeter, A Virtualization

Technologies primer: theory, Network Device

Virtualization, Data-Path Virtualization, Control-Plane

Virtualization, Routing Protocols.

Written Unit

Test – I

(Marks 25)

3

To elaborate the

concept of Adhoc

networking

Adhoc Networking

Introduction, application of MANET, challenges,

Routing in Ad hoc networks, topology & position-based

approaches, Routing protocols: topology based, position

based, Broadcasting, Multicasting, & Geocasting,

Wireless LAN, Transmission techniques, MAC protocol

issues, Wireless PANs, The Bluetooth technology.

Written Class Test will be conducted. (Marks 10)

4

To elaborate

wireless sensor

networks

Wireless sensor networks

Need and application of sensor networks, sensor

networks design considerations, empirical energy

consumption, sensing and communication range, design

issues, localization scheme, clustering of SNs, Routing

layer, Sensor networks in controlled environment and

actuators, regularly placed sensors, network issues,

RFID as passive sensors.

Assignments

will be given

for the above

topics.

(Marks 10)

EVALUATION:




TEXT BOOKS:

1. Carlos de Morais Cordeiro, D. P. (2011). Ad Hoc and Sensor Networks: Theory and

Applications ( 2nd edition ed.). World Scientific Publishing Company.

2. James F. Kurose, K. W. (2012). Computer Networking: A Top-Down Approach (6th edition

ed.). Pearson.

3. Victor Moreno, K. R. (2006). Network Virtualization. Cisco Press.

REFERENCE BOOKS:

1. Carlos de Morais Cordeiro, D. P. (2011). Ad Hoc and Sensor Networks: Theory and

Applications ( 2nd edition ed.). World Scientific Publishing Company.


PAGE NO: 11

2. Forouzan, B. (2009). TCP/IP Protocol Suite (4 edition ed.). McGraw-Hill Science.

3. Garg, V. (2002). Wireless network evolution: 2G to 3G. Prentice Hall.

4. James F. Kurose, K. W. (2012). Computer Networking: A Top-Down Approach (6th edition

ed.). Pearson.

5. Jonathan Loo, J. L. (2011). Mobile Ad Hoc Networks: Current Status and Future Trends.

CRC Press .

6. Schiller, S. J. (2012). Mobile Communications (Second Edition ed.). Pearson Education.

7. Stallings, W. (2013). Wireless Communications and Networks. Pearson Education.

8. Stojmenovic, I. (2010). Handbook of Wireless Networks and Mobile Computing (Wiley

India Edition ed.).

9. Victor Moreno, K. R. (2006). Network Virtualization. Cisco Press.

_______________________________________________________________________________

COURSE: OPERATING SYSTEMS

CREDIT - 04

Objectives:

To learn the fundamentals of Operating Systems.

To learn the mechanisms of operating system to handle processes and threads and their

communication

To learn the mechanisms involved in memory management in contemporary operating

systems

Outcomes:


Analyze the structure of OS and basic architectural components involved in operating

system design

Conceptualize the components involved in designing a contemporary operating system

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1

To introduce to

basic concepts of

operating systems

Introduction to Operating System

Introduction to Linux kernel, Types of kernel

(monolithic, micro, exo), Operating system booting

process GRUB-I, GRUB-II. Processes, Interprocess

Communication, Scheduling.

Written

Unit Test –

I

(Marks 25)

2

To elaborate

memory

management in

Memory management and virtual memory in Linux

Basic memory management, swapping, virtual memory,

Page replacement algorithms, Design issues for paging


PAGE NO: 12

operating system systems, segmentation. Case Study: Linux memory

management.

3

To elaborate the

concept of Input

and Output

operations

Input/ Output in Linux Principles of I/O Hardware, Principles of I/O Software,

Deadlocks, RAM Disks, Disks, Terminals. File Systems:

Files, Directories, File System Implementation, Security,

Protection mechanisms in different Linux versions


4

To elaborate

android operating

system

Android Operating System

The Android Software Stack, The Linux Kernel – its

functions, essential hardware drivers. Libraries - Surface

Manager, Media framework, SQLite, WebKit, OpenGL.

Android Runtime - Dalvik Virtual Machine, Core Java

Libraries. Application Framework - Activity Manager,

Content Providers, Telephony Manager, Location

Manager, Resource Manager. Android Application –

Activities and Activity Lifecycle, applications such as

SMS client app, Dialer, Web browser, Contact manager

Assignment

s will be

given for

the above

topics.

(Marks 15)

EVALUATION:




TEXT BOOKS:

1. Avi Silberschatz, P. B. (2009). Operating System Concepts with Java ( Eight Edition ed.).

John Wiley & Sons, Inc.

2. Evi Nemeth, G. S. (2011). UNIX and Linux System Administration Handbook ( Fourth

Edition ed.). Pearson Education, Inc.

3. Meier, R. (2012). PROFESSIONAL Android™ 4 Application Development. John Wiley &

Sons, Inc. .

4. Pramod Chandra, P. B. (2014). An Introduction to Operating Systems: Concepts and

Practice (GNU/Linux) (4th edition ed.).

REFERENCE BOOKS:

1. Andrew S. Tanenbaum, A. S. (2006). Operating Systems: Design and Implementation

(Third Edition ed.). Prentice Hall.

2. Developers, A. (n.d.).

3. Documentation, F. (n.d.).

4. Documentation, O. U. (n.d.).

_______________________________________________________________________________


PAGE NO: 13

COURSE: SOFTWARE ENGINEERING

CREDIT: 4

Objectives:

The basic objective of software engineering is to develop methods and procedures

for software development that can scale up for large systems.

It can be used consistently to produce high-quality software at low cost and with a small

cycle of time.

Outcome:

Students will be able to:

Apply use of knowledge of Software Life Cycle to successfully implement the projects in

the corporate world

Identify the Inputs, Tools and techniques to get the required Project deliverable and Product

deliverable using knowledge areas of Project Management.

Code Course

Teaching Period /

Week Credit

Duration

of

Theory

Exam

(in Hrs.) L Pr./ Tu Int. Ext. Total

MCS104 Software Engineering 4 - 2 2 4 2

Module

No


1 The objective of

this module is to

introduce the

student to the basic

foundations of

software

development using

software

engineering

principles.

Introduction to software engineering and project

management

Introduction to Software Engineering, Software

Components, Software Characteristics, Software

Crisis, Software Engineering Processes, Similarity

and Differences from Conventional, Engineering

Processes, Software Quality Attributes. Software

Development Life Cycle (SDLC), Models: Water

Fall Model, Prototype Model, Spiral Model,

Evolutionary Development Models, Iterative

Enhancement Models.

Unit Test-1

(Marks-25)

2

To introduce

students to

Software

Requirement

elicitation

techniques

Software Requirement Analysis and Specification

Requirement Engineering Process: Elicitation,

Analysis, Documentation, Review and Management

of User Needs, Feasibility Study, Information

Modeling, Data Flow Diagrams, Entity Relationship

Diagrams, Data Dictionary Decision Tables, SRS

Document, IEEE Standards for SRS. Requirement

Elicitation: Interviews, Questionnaire,

Brainstorming, Facilitated Application Specification

Technique (FAST), Use Case Approach. SRS Case

study.

Online Test

(Marks-15)


PAGE NO: 14

3

This will introduce

the students to the

basic concepts of

software project

scheduling &

design

Software Project Planning and Scheduling

Business Case, Project selection and Approval,

Project charter, Project Scope management: Scope

definition and Project Scope management, Creating

the Work Breakdown Structures, Scope

Verification, Scope Control. Staffing Level

Estimation, Effect of schedule Change on Cost,

Degree of Rigor & Task set selector, Project

Schedule, Schedule Control

Software Design

Basic Concept of Software Design, Architectural

Design, Low Level Design: Modularization, Design

Structure Charts, Pseudo Codes, Flow Charts,

Coupling and Cohesion Measures, Design

Strategies: Function Oriented Design, Object

Oriented Design, Top-Down and Bottom-Up

Design. Software Measurement and Metrics:

Various Size Oriented Measures: Halestead’s

Software Science, Function Point (FP) Based

Measures, Cyclomatic Complexity Measures:

Control Flow Graphs.

4

To understand the

importance of

Software Testing

strategies and

Quality Assurance

during the software

development

process.

Software Testing and Quality Assurance

Testing Objectives, Unit Testing, Integration

Testing, Acceptance Testing, Regression Testing,

Testing for Functionality and Testing for

Performance, Top-Down and Bottom-Up Testing

Strategies: Test Drivers and Test Stubs, Structural

Testing (White Box Testing), Functional Testing

(Black Box Testing), Test Data Suit Preparation,

Alpha and Beta Testing of Products.

Static Testing Strategies: Formal Technical Reviews

(Peer Reviews), Walk Through, Code Inspection,

Compliance with Design and Coding Standards

Software Quality Assurance (SQA): Verification

and Validation, SQA Plans, Software Quality

Frameworks, ISO 9000 Models, SEI-CMM Model.

Assignment

(Marks-5)

5 The objectives of

this module is to

introduce the

fundamentals of

software costing and

maintenance

To describe three

metrics for software

productivity

assessment.

Software Maintenance and Software Project

Management

Software as an Evolutionary Entity, Need for

Maintenance, Categories of Maintenance:

Preventive, Corrective and Perfective Maintenance,

Cost of Maintenance, Software Re- Engineering,

Reverse Engineering. Software Configuration

Management Activities, Change Control Process,

Software Version Control, An Overview of CASE

Tools. Software Estimation: Size Estimation:

Function Point (Numericals). Cost Estimation:

COCOMO (Numericals), COCOMO-II

(Numericals). Software Risk Analysis and

Management.

Assignment

(Marks-5)


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EVALUATION:



3) Total marks = Internal50 + External 50 = 100

TEXT BOOKS:

1. Pressman, R. S. (2019). Software Engineering (5th and 7th edition ed.). McGraw Hill

publication.

2. Schwalbe, K. (2011). Managing Information Technology Project (6th edition ed.). Cengage

Learning publication.

REFERENCE BOOKS:

1) Bell, D. (2005). Software Engineering for students: A Programming Approach. Pearson

publication.

2) Inc(), K. L. (2012). Software Engineering. Dreamtech Press. .

3) KK Agrawal, Y. S. (2007). Software Engineering (3rd

ed.). New Age International

publication.

4) Marchewka, J. T. (2013). Information Technology Project Management. Wiley India

publication.

_______________________________________________________________________________

COURSE: PROGRAMMING CONCEPTS LAB

CREDIT: 2

Objectives:

Identify the way of implementation algorithms required for sorting searching, sorting array

Identify the method of implementation of graph related algorithms

Outcomes:


Understand the concept of implementation of various algorithms

Understand the measuring of performance values of various algorithms

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


- 2 1 1 2 1

Module

No


1 To implement sorting

algorithms

Implementation of Sorting

Algorithms

Insertion sort, Bubble Sort, Selection

Sort, Shell Sort

Students will be

evaluated using Lab

Manual. (Marks 5)


PAGE NO: 16

2

To implement divide

and conquer method-

based algorithms

Implementation of Algorithms based

on divide and conquer

Quick sort implementation, Binary

search algorithm

Class Test

(Marks 10)

3

To implement shortest

path and minimum

spanning tree

algorithm

Implementation of MST and Shortest

path algorithm

Find Minimum Cost Spanning Tree of a

given undirected graph using Kristal‟s

algorithm, from a given vertex in a

weighted connected graph, find shortest

paths to other vertices using Dijikstra‟s

algorithm.

4

To implement graph

traversal algorithms

Implementation of Graph Algorithms Traverse a graph using Breadth-first

search, Depth-first search

Practical Exam will be

conducted.

(Marks 10)

Programming Language: C/C++

EVALUATION:




TEXT BOOKS:

1) Narasimha Karumanchi, (2016), Data Structures and Algorithms Made Easy: Data

Structures and Algorithmic Puzzles, CareerMonk Plublications

REFERENCE BOOKS:

1. Bhargava, A. (2016). Grokking Algorithms: An illustrated guide for programmers and

other curious people. MEAP.

2. Sanjoy Dasgupta, C. H. (2006). Algorithms. McGraw-Hill Higher Education .

3. Thomas H. Cormen, C. E. (2009). Introduction to Algorithms (Third ed.). New Delhi: PHI

Learning Pvt. Ltd.

____________________________________________________________________________

COURSE: NETWORKING LAB

CREDIT: 2

Objectives:

This practical subject introduces the student actual implementation of various types of

networks using simulating software

The objective of this subject is to give hands on experiment of hardware establishment of

networks using simulating software

Outcomes:

The students will be able to configure various types of networks

Students will implement various networks using simulating software


PAGE NO: 17

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1

To introduce

students to IDE of

simulating software

Study of simulating software interface

Basic Configuration of router, assigning

ipv4 and ipv6 addresses to the interfaces of

the routers

Lab manual for 05

marks

2

To elaborate the

configuration of

VLANs and PPP

Configure VLANs on the router, Spanning

tree, Configuration of PPP

Online test of 10

marks

3

To demonstrate the

configuration of

RIPv2, EIGRP and

OSPF

Configure RIPv2, Configure EIGRP,

Configure OSPF

4

To implement

configuration of

switch

Access List Configuration, Configuration of

NAT, Configuration of DCHP,

Configuration of switch

Practical test of 10

marks

Practical’s to be done Packet Tracer (or other simulating software)

EVALUATION:




TEXT BOOK:

1. A., F. B. (2004). Computer Networks, PHI Data Communication and Networking (Third

ed.). McGraw Hill.Andrew Tenenbaum.

REFERENCE BOOKS:

1. Keshav, S. (2002). An Engineering Approach to Computer Networking. Addision-Wesley.

2. Kurose, J. ,. (2005). Computer Networking: A Top-Down Approach Featuring the Internet

(Third ed.). Addison-Wesley.

_______________________________________________________________________________

COURSE: SOFTWARE TESTING LAB

CREDIT: 2

Objectives:

Identify the need of software testing in current industry scenario, techniques and tools in

area of software testing

Demonstrate the ability to apply multiple methods to check reliability of a software system

and to identify and apply redundancy and fault tolerance for a medium-sized application,


PAGE NO: 18

Identify the Fault in program logic that fails to validate data and values properly before

they are used

Discuss the distinctions between validation and defect testing,

Understand types of testing and essential characteristics of tool used for test automation

Outcomes:


Understand the concept and need of software testing

Understand the need and usage of software tools required for manual and automated testing

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No


1

To understand

the concepts of

software testing

Introduction to Software Testing

Functional and non-functional Testing, Writing Test cases,

Testing Framework, Test Documents, Static Testing: Data

Flow Analysis, Control Flow Analysis, Cyclomatic

Complexity, White Box Testing: Statement Coverage,

Branch Coverage, Path Coverage, State Transition, Black

Box Testing: Equivalence Class Partitioning, Boundary

Value Analysis, Cause Effect Graphing and Decision table

technique, Use case testing

Students

will be

evaluated

using Lab

Manual.

(Marks 5)

2

To perform

manual testing

Software Testing Strategies and Manual Testing

Characteristics, Integration Testing, Functional Testing,

Object-oriented Testing, Alpha and Beta Testing, overview

of testing tools, Manual Testing on existing Project

Class Test

(Marks 10)

3

To perform

automation

testing using

QTP

Automation Testing using QTP

QTP Introduction, recording and replaying test cases, QTP

Synchronization Point, QTP Parameterization, QTP

Checkpoints (Windows and Web application), Recording

modes in QTP

4

To perform

automation

testing using

Bugzilla

Automation Testing using Bugzilla Bugzilla Introduction and usage, Creating Reporting a new

bug, Viewing Bug reports, Modifying Bug reports,

Performance Testing Concepts: Load Testing, Stress

Testing

Practical

Exam will

be

conducted.

(Marks 10)

Note: Manual Testing (MT), Automation Testing (AT)

EVALUATION:





PAGE NO: 19

REFERENCE BOOKS:

1. Shende. (2010). Testing in 30 + open source tools, . SPD.

2. Spillner, D. (2014 ). Software testing foundations. SPD.

____________________________________________________________________________

COURSE: ADVANCED WEB TECHNOLOGY LAB

CREDIT: 2

Objectives:

The students will Study the architecture of Dot Net framework

Understand the basic principles of website development using IDE

Learn advanced windows and web development techniques using dot NET

Outcomes:

The students will be able to create user interface-based applications

Design and develop secure web applications using asp.net according to industry standards

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1

To introduce

students to

IDE of Asp.net

web

application

Asp.Net Web Application

ASP.net server controls: Button, TextBox,

Labels, CheckBoxex, Radio Buttons, List

Controls. Web config and global.aspx files, data

types, variables, statements, organizing code

Lab manual for 05

marks

2

To elaborate

the use of

validation

controls in

asp.net

Validation Control

Validation techniques, state, management using

view state, using session state, using application

state, using cookies and URL encoding, Master

page, content pages, nesting master pages,

accessing master page controls from a control

page, Site navigation Controls

Online test of 10

marks

3

To

demonstrate

the use of data

base

connectivity

Database Connectivity

Introduction, using SQL data sources, GridView

Control, DetailView and FormView Controls,

ListView and DataPager Controls in ASP.NET

Practical exam of

10 marks

4

To implement

LINQ with

asp.net

LINQ

Operators, implementation, LINQ to objects,

XML and ADO.net, AJAX: Introduction and

working, asp.net Ajax server control, JQuery:

Introduction, UI Library, working


PAGE NO: 20

EVALUATION:



3) Total marks = Internal 25 + External 25= 50

TEXT BOOKS:

1. Walther, S. (2010). ASP.NET MVC Framework. Unleashed.

2. Walther, S. (2011). ASP.NET 3.5 Unleashed. SAMS Publishing.

REFERENCE BOOKS:

1. Christian Nagel, Bill Evjen, Jay Glynn, Karli Watson, Morgan Skinner(2012) , Professional

C# and .NET 4.5. Wrox Publication.

2. Andrew Stellman, Jennifer Greene (2013), Head First C# ( Second Edition ed.). O’Reilly.

3. Benjamin Perkins, Jacob Vibe Hammer, Jon D. Reid (2017), C#, B.,Wrox Publication.

4. Kamal, R. (2017). Internet and Web Technologie. Tata McGraw Hill.

5. Mukhi, V. (2003). C# with Visual Studio. BPB.

6. Murach’s. (2010). ADO. Net 4 Database Programming with C# (4th Edition ed.).

7. Murach’s. (2010). ASP. Net 4. 0 Web Programming with C#.

8. Patel, C. (2010). Advance .NET Technology ( second edition ed.). DreamTech Press.

9. Ralph Moseley & M. T. Savaliya. (2011). Developing Web Application (Second Editon

ed.). Wiley.

10. Swedberg, J. C. (2013). Learning jQuery (Third Edition ed.). SPD Publication.

11. Trolsen, A. (2012). Pro C# 5.0 and the .NET 4.5 Framework. APress.

http://www.oreillynet.com/pub/au/2454

http://www.oreillynet.com/pub/au/2455


PAGE NO: 21

M.Sc. (COMPUTER SCIENCE) SEMESTER - II (FIRST YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int.

Ext.

Total




4 - 2 2 4 2



MCS204 Elective I 4 - 2 2 4 2


- 2 1 1 2 1

MCSL206 Statistics Lab - 2 1 1 2 1



Total 16 8 24 -

SEMESTER-II

1 Credit=25 Marks

Total Credits = 24


Elective - I


MCS204A Distributed Systems

MCS204B Computer Graphics

MCS204C Advanced Python

MCS204D Natural Language Processing


PAGE NO: 22

COURSE: MOBILE COMMUNICATION AND WIRELESS TECHNOLOGY

CREDIT - 04

Objectives:

To learn the concepts of wireless communication and mobile networks

To identify different wireless technologies and its applications

To acquire knowledge on generation of cellular networks and its standards used

Outcomes:


Understand the concept of cellular communications, advantages and its limitations

Compare the various wireless technologies and its applications

Apply the appropriate technology in the applications

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total



Module

No.


1

To introduce

to basic

concepts of

wireless

networking

Fundamentals of Wireless Technology

Introduction to Mobile and wireless communications,

Overview of radio transmission frequencies, Signal

Antennas, Signal Propagation, Multiplexing – SDM,FDM,

TDM,CDM, Modulation – ASK,FSK,PSK, Advanced

FSK, Advanced PSK, OFDM, Spread Spectrum –

DSSS,FHSS, Wireless Transmission Impairments – Free

Space Loss, Fading, Multipath Propagation, Atmospheric

Absorption, Error Correction – Reed Solomon, BCH,

Hamming code, Convolution Code (Encoding and

Decoding),

Students will

be evaluated

by taking

viva.

(Marks 05)

2

To elaborate

wireless and

cellular

wireless

network

Wireless and Cellular wireless Networks

Wireless network, Wireless network Architecture,

Classification of wireless networks – WBAN, WPAN,

WLAN, WMAN, WWAN., IEEE 802.11, IEEE 802.16,

Bluetooth – Standards, Architecture and Services, Cellular

wireless Networks, Principles of cellular networks –

cellular network organization, operation of cellular

systems, Handoff., Generation of cellular networks – 1G,

2G, 2.5G, 3G and 4G.

Written Unit

Test – I

(Marks 25)

3

To elaborate

the concept of

mobile

communicatio

n system

Mobile Communication System

GSM – Architecture, Air Interface, Multiple Access

Scheme, Channel Organization, Call Setup Procedure,

Protocol Signaling, Handover, Security, GPRS –

Architecture, GPRS signaling, Mobility management,



PAGE NO: 23

GPRS roaming, network, CDMA2000- Introduction,

Layering Structure, Channels,Logical Channels, Forward

Link and Reverse link physical channels, W-CDMA –

Physical Layers, Channels, UMTS – Network

Architecture, Interfaces, Network Evolution, Release 5,

FDD and TDD, Time Slots, Protocol Architecture, Bearer

Model, Introduction to LTE

4

To elaborate

different layers

of mobile

network

Mobile network, transport and application layers

Mobile IP – Dynamic Host Configuration Protocol, Mobile

Ad Hoc Routing Protocols– Multicast routing, TCP over

Wireless Networks – Indirect TCP – Snooping TCP –

Mobile TCP – Fast Retransmit / Fast Recovery

Transmission/Timeout Freezing-Selective Retransmission

– Transaction Oriented TCP , TCP over 2.5 / 3G wireless

Networks, WAP Model- Mobile Location based services -

WAP Gateway –WAP protocols – WAP user agent profile,

Caching model-wireless bearers for WAP - WML –

WMLScripts – WTA.

Assignments

will be given

for the above

topics.

(Marks 10)

EVALUATION:




TEXT BOOKS:

1. Garg, V. K. (2011). Wireless Network Evolution 2G to 3G. Pearson Publications.

2. Misra, S. (2010). Wireless Communications and Networks, 3G and Beyond (Second ed.).

McGraw Hill Education.

REFERENCE BOOKS:

1. Dr. Sunilkumar S. Manvi, M. S. (2010). Wireless and Mobile Networks, Concepts and

Protocols. Wiley India.

2. K. Fazel, S. K. (2010). Multi-Carrier and Spread Spectrum Systems - From OFDM and

MC-CDMA to LTE and WiMAX (Second Edition ed.). Wiley publications.

3. Yi Bang Lin, I. (2008). Wireless and Mobile Network Architectures. Wiley India.

4. Yi-Bing Lin, A.-C. P. (2012). Wireless and Mobile All-IP Networks. Wiley Publications.

COURSE: DATA ANALYTICS AND MINING CREDIT - 4 Objectives:

To acquire the knowledge of various concepts and tools behind mining data for business intelligence

To Study data mining algorithms, methods and tools

To Identify business applications of data mining


PAGE NO: 24

Outcomes:


Apply data mining concepts for data analysis and report generation

Develop industry level data mining skills using software tools

Make use of relevant theories, concepts and techniques to solve real-world business

problems

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


4 - 2 2 4 2

Module

No.


1

This module introduces students to the concept of data analytics

Data Analytics

Introduction, Data Summarization and

visualization, Linear, Non-linear regression, model

selection

Online Test

(Marks 5)

2

This module provides background on data objects and statistical concepts. It introduces techniques for preprocessing data before mining.

Data Mining and Data Preprocessing What is data mining?, Knowledge discovery- KDD

process, related technologies - Machine Learning,

DBMS, OLAP, Statistics, Data Mining Goals,

stages of the Data Mining Process, Data Mining

Techniques, Knowledge Representation Methods.

Data cleaning, Data transformation, Data reduction,

Discretization and generating concept hierarchies.

introduction to data warehousing, OLAP, and data

generalization. Data Cube Computation and

Multidimensional Data Analysis

Written

Unit Test –

I

(Marks 25)

3

This unit covers supervised learning method as classification and Prediction

Classification and Prediction

Decision tree, Bayesian classification, rule-based

classification, neural networks, support vector

machines, associative classification, k-nearest-

neighbor classifier, case-based reasoning.

Assignmen

ts will be

given for

the above

topics.

(Marks 10)

4

This unit covers unsupervised learning method as clustering and association rule mining To gain detailed insights of outlier detection

Clustering and Association Rule Mining

Partitioning, hierarchical, density-based, grid-

based, and model-based methods data clustering. Mining Frequent Patterns, Associations, and

Correlations

Outlier Detection: Detection of anomalies, such as

the statistical, proximity-based, clustering-based,

and classification-based methods.

Assignmen

ts will be

given for

the above

topics. (Marks 10)

EVALUATION:

1) On Four Modules of 50 marks 2) Final examination of 50 marks 3) Total marks = Internal 50 + External 50 = 100


PAGE NO: 25

TEXT BOOKS:

1. Avi Silberschatz, H. F. (2010). Database System Concepts (5th edition ed.). McGraw-Hill.

2. Chawla, S. S. (2003). Spatial Databases: A Tour. Prentice Hal.

REFERENCE BOOKS:

1. Gehrke, R. R. (2002). Database Management Systems (3rd edition ed.). McGraw-Hill.

2. M. Böhlen, J. G. (2006). Multi-dimensional aggregation for temporal data. . In Proc. of

EDBT.

3. Navathe, E. a. (2003). Fundamentals of Database Systems (6thEdition ed.). Addison.

Wesley.

4. Pelagatti, S. C. (1984). Distributed Database; Principles & Systems. McGraw-Hill

International Editions.

5. Ponniah, P. (2010). Data Warehousing fundamentals. JohnWiley.

6. Schneider, R. G. (2005). Moving objects databases. Morgan Kaufmann Publishers, Inc.

7. Singh, S. K. (2011). Database Systems: Concepts, Design and Applications (2nd edition

ed.). Pearson Publishing.

_______________________________________________________________________________

COURSE: RESEARCH METHODS AND STATISTICAL ANALYSIS

CREDIT: 4

Objectives:

To understand Research and Research Process

To acquaint students with identifying problems for research and develop research strategies

To familiarize students with the techniques of data collection, analysis of data and

interpretation

Outcome:

Students will be able to:

Prepare a preliminary research design for projects in their subject matter areas

Accurately collect, analyse and report data

Present complex data or situations clearly

Review and analyse research findings Get the knowledge of objectives and types of

research

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total




PAGE NO: 26

Module

No


1

To introduce

students to the

concept of research

Introduction to Research methodology

An Introduction Objectives of Research, Types of

Research, Research Methods and Methodology,

defining a Research Problem, Techniques involved

in Defining a Problem

Unit Test-1

(Marks-25)

2

To elaborate

importance of

literature review

and research design

Review of Literature, Research Design

Need for Research Design, Features of Good

Design, Different Research Designs, Basic

Principles of Experimental Designs, Sampling

Design, Steps in Sampling Design, Types of

Sampling Design, Sampling Fundamentals,

Estimation, Sample size Determination, Random

sampling. Measurement and Scaling Techniques

Measurement in Research

3

To learn data

collection and

processing methods

Data Collection and Processing

Methods of Data Collection and Analysis Collection

of Primary and Secondary Data, Selection of

appropriate method Data Processing Operations,

Elements of Analysis.

Assignment

(Marks-10)

4

To learn data

analysis and

presentation of the

results

Statistical Analysis and Presentation

Statistics in Research, Measures of Dispersion,

Measures of Skewness, Regression Analysis,

Correlation, Quantitative data analysis, Techniques

of Hypotheses, Parametric or Standard Tests Basic

concepts, Tests for Hypotheses I and II, Important

parameters limitations of the tests of Hypotheses,

Chi-square Test, Comparing Variance, As a non-

parametric Test, Conversion of Chi to Phi, Caution

in using Chi-square test, representation of research.

Online Test

(Marks-15)

EVALUATION:



3) Total marks = Internal50 + External 50 = 100

TEXT BOOKS:

1. Oates, B. J. (2006). Researching Information Systems and Computing. Sage Publications

India Pvt Ltd.

REFERENCE BOOKS:

1. Kahn, J. W. (2010). Research in Education. PHI Publication.

2. Kothari, C. (1985). Research Methodology, Methods and Techniques (third edition ed.).

New Age International.

3. Strauss, J. C. (2008). Basic of Qualitative Research (3rd Edition ed.). Sage Publications.

4. Willkinson K.P, L. B. (2010). Formulation of Hypothesis. Mumbai : Hymalaya Publication.

_______________________________________________________________________________


PAGE NO: 27

COURSE: ELECTIVE I – DISTRIBUTED SYSTEMS CREDIT - 4 Objectives:

To learn the principles, architectures, algorithms and programming models used in distributed systems.

To examine state-of-the-art distributed systems, such as Google File System.

To design and implement sample distributed systems.

To transform students’ computational thinking from designing applications for a single computer system, towards that of distributed systems.

Outcomes: The students will be able to:

Identify the core concepts of distributed systems: the way in which several machines orchestrate to correctly solve problems in an efficient, reliable and scalable way.

Examine how existing systems have applied the concepts of distributed systems in designing large systems, and will additionally apply these concepts to develop sample systems.

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total

MCS204A Distributed Systems 4 - 2 2 4 2

Module

No.


1

This module

will enable

students to

introduce

concepts

related to

distributed

computing

systems.

Characterization of Distributed Systems Introduction, Examples of distributed Systems, Resource

sharing and the Web Challenges. Architectural models,

Fundamental Models. Theoretical Foundation for

Distributed System: Limitation of Distributed system,

absence of global clock, shared memory, Logical clocks,

Lamport’s & vectors logical clocks. Concepts in Message

Passing Systems: causal order, total order, total causal

order, Techniques for Message Ordering, Causal ordering

of messages, global state, termination detection.

Written Unit

Test – I

(Marks 25)

2

This module covers solutions to the problem of mutual exclusion, which is important for correctness in distributed systems with shared resources.

Distributed Mutual Exclusion

Classification of distributed mutual exclusion, requirement

of mutual exclusion theorem, Token based and nontoken-

based algorithms, performance metric for distributed

mutual exclusion algorithms. Distributed Deadlock

Detection: system model, resource Vs communication

deadlocks, deadlock prevention, avoidance, detection &

resolution, centralized dead lock detection, distributed

dead lock detection, path pushing algorithms, edge

chasing algorithms.

Assignments

will be given

for the above

topics.

(Marks 10)


PAGE NO: 28

3

To introduce

students to the

concept of

Agreement

protocol and

the abstraction

& use of file

systems

Agreement Protocols

Introduction, System models, classification of Agreement

Problem, Byzantine agreement problem, Consensus

problem, Interactive consistency Problem, Solution to

Byzantine Agreement problem, Application of Agreement

problem, Atomic Commit in Distributed Database system.

Distributed Resource Management: Issues in distributed

File Systems, Mechanism for building distributed file

systems, Design issues in Distributed Shared Memory,

Algorithm for Implementation of Distributed Shared

Memory.

Assignments

will be given

for the above

topics.

(Marks 5)

4

The students

will learn

about the

Failure

Recovery in

Distributed

Systems and

Fault

Tolerance

concepts

Failure Recovery in Distributed Systems

Concepts in Backward and Forward recovery, Recovery in

Concurrent systems, Obtaining consistent Checkpoints,

Recovery in Distributed Database Systems. Fault

Tolerance: Issues in Fault Tolerance, Commit Protocols,

Voting protocols, Dynamic voting protocols.

Online Class

test will be

conducted.

(Marks 5)

5

The students

will

understand the

transactions

and

concurrency

Control

mechanisms in

Distributed

systems

Transactions and Concurrency Control

Transactions, Nested transactions, Locks, Optimistic

Concurrency control, Timestamp ordering, Comparison of

methods for concurrency control. Distributed

Transactions: Flat and nested distributed transactions,

Atomic Commit protocols, Concurrency control in

distributed transactions, Distributed deadlocks,

Transaction recovery. Replication: System model and

group communication, Fault - tolerant services, highly

available services, Transactions with replicated data.

Online Class

test will be

conducted.

(Marks 5)

EVALUATION:




TEXT BOOKS:

1) Ramakrishna, G. (2007). Database Management Systems. Mc Grawhill.

2) Shivaratri, S. &. (2006). Advanced Concept in Operating Systems. McGraw Hill.

REFERENCE BOOKS:

1. Coulouris, D. K. (2005). Distributed System: Concepts and Design. Pearson Education.

2. Tel, G. (2012). Distributed Algorithms. Cambridge University Press.

3. Tenanuanbaum, S. (2001). Distributed Systems, PHI.

_______________________________________________________________________________


PAGE NO: 29

COURSE: ELECTIVE I – COMPUTER GRAPHICS

CREDIT - 4

Objectives:

To understand the concepts of output primitives of Computer Graphics.

To learn 2D and 3D graphics Techniques.

Outcomes:


Demonstrate the algorithms to implement output primitives of Computer Graphics

Apply and analyse 2D and 3D techniques

Code Course

Teaching

Period /

Week

Credit Duration of

Theory

Exam (in

Hrs.) L Pr./

Tu Int. Ext. Total

MCS204B Computer Graphics 4 - 2 2 4 2

Module

No.


1

To introduce

students to

computer

graphics

Introduction to Computer Graphics

Elements of Computer Graphics, Graphics display systems

Written Unit

Test – I

(Marks 25)

2

To elaborate on primitive algorithms to generate outputs

Output primitives and its algorithms

Points and Lines, Line Drawing algorithms: DDA line

drawing algorithm, Bresenham’s drawing algorithm,

Circle and Ellipse generating algorithms: Mid-point Circle

algorithm, Mid-point Ellipse algorithm, Parametric Cubic

Curves: Bezier curves. Fill area algorithms: Scan line

polygon fill algorithm, Inside-Outside Tests, Boundary fill

algorithms, Flood fill algorithms

3

To introduce

students to

various

transformation

and clipping

2D Geometric Transformations & Clipping

Basic transformations, Matrix representation and

Homogeneous Coordinates, Composite transformation,

shear & reflection. Transformation between coordinated

systems, Window to Viewport coordinate transformation,

Clipping operations – Point clipping Line clipping: Cohen

– Sutherland line clipping, Midpoint subdivision, Polygon

Clipping: Sutherland – Hodgeman polygon clipping

,Weiler – Atherton polygon clipping

Online Class

test will be

conducted.

(Marks 15)

4

To elaborate

on basic 3D

and fractal

concepts

Basic 3D concepts and Fractals

3D object representation methods: B-REP, sweep

representations, CSG, Basic transformations, Reflection,

shear, Projections – Parallel and Perspective Halft one and

Dithering technique. Fractals and self-similarity: Koch

Curves/snowflake, Sirpenski Triangle

Assignments

will be given

for the above

topics.

(Marks 10)


PAGE NO: 30

EVALUATION:




TEXT BOOKS:

1) David F. Rogers, J. A. (1990). Mathematical elements for computer graphics. McGraw-Hill.

REFERENCE BOOKS:

1) Donald Hearn, M. P. (2002). Computer Graphics C Version. Pearson Education.

2) Rafael C. Gonzalez, R. E. (2011). Digital Image Processing (3rd ed.). Pearson Education.

_______________________________________________________________________________

COURSE: ELECTIVE I – ADVANCED PYTHON

CREDIT - 4

Objectives:

To introduce students to use of Python programming to solve data analytics problems

To elaborate students to statistical analysis using Python programming

Outcomes:

The students will be able to improve Problem solving and programming capability

The students will be able to perform data analytics using appropriate data mining methods

Code Course

Teaching

Period /

Week

Credit Duration of

Theory

Exam (in

Hrs.) L Pr./

Tu Int. Ext. Total

MCS204C Advanced Python 4 - 2 2 4 2

Module

No.


1

To introduce

use of python

for data

analytics

Introduction to Data Analytics

Why Analytics, Traditional Data Management, Analytical

tools, Types of Analytics, Hind sight, ore sight and

insight, Dimensions and measures, why learn Python for

data analysis, Using the IPython notebook

Written Unit

Test – I

(Marks 25)

2

To describe various libraries required for data analytics

Libraries for data analytics

Anaconda, Numpy, Scipy, Pandas, Matplotlib, Seaborn,

Scikit-learn, Jupyter Notebook: Create Documentation,

Code mode, Markdown mode

Assignments

will be given

for the above

topics.

(Marks 10)


PAGE NO: 31

3

To elaborate

statistical

analysis using

Python

Statistics using python

Mean, Median, Mode, Z-scores, Bias -variance

dichotomy, Sampling and t-tests, Sample vs Population

statistics, Random Variables, Probability distribution

function, Expected value, Binomial Distributions, Normal

Distributions, Central limit Theorem, Hypothesis testing,

Z-Stats vs T-stats, Type 1 type 2 error, Chi Square test

ANOVA test and F-stats

Assignments

will be given

for the above

topics.

(Marks 5)

4

To study

special

libraries in

Python

Study of Numpy, Scipy, Matplotlib

NUMPY: Creating NumPy arrays, Indexing and slicing in

NumPy, Downloading and parsing data, creating

multidimensional arrays, NumPy Data types, Array

tributes, Indexing and Slicing, creating array, views

copies, Manipulating array shapes I/O,

SCIPY: Introduction to SciPy, Create function, modules of

SciPy

MATPLOTLIB: Scatter plot, Bar charts, histogram, Stack

charts, Legend title Style, Figures and subplots, plotting

function in pandas, Labelling and arranging figures, Save

plots

Online Class

test will be

conducted.

(Marks 10)

EVALUATION:




TEXT BOOK:

1) Brown, M. C. (n.d.), (2018), Complete Reference: Python. McGraw Hill.

2) Chun, W. J. (2018). Core Python Programming. Prentice Hall.

REFERENCE BOOKS:

1) Allen Downey, J. E. (2017). How To Think Like A Computer Scientist: Learning With Python.

DreamTech.

2) Mark Lutz, D. A. (2016). Learning Python. O’Reilly.

_______________________________________________________________________________

COURSE: ELECTIVE I – NATURAL LANGUAGE PROCESSING

CREDIT - 4

Objectives:

This course introduces the fundamental concepts and techniques of natural language

processing (NLP).

Students will gain an in-depth understanding of the computational properties of natural

languages and the commonly used algorithms for processing linguistic information.


PAGE NO: 32

Outcomes:


Understand key concepts from NLP those are used to describe and analyze language

Understand POS tagging and context free grammar for English language

Understand semantics and pragmatics of English language for processing

Code Course

Teaching

Period /

Week

Credit Duration of

Theory

Exam (in

Hrs.) L Pr./

Tu Int. Ext. Total

MCS204D Natural Language Processing 4 - 2 2 4 2

Module

No.


1

To introduce

students to text

representation

in computers

Introduction

Human languages, models, ambiguity, processing

paradigms; Phases in natural language processing,

applications., Text representation in computers, encoding

schemes., Linguistics resources- Introduction to corpus,

elements in balanced corpus, TreeBank, PropBank,

WordNet, VerbNet etc. Resource management with XML,

Management of linguistic data with the help of GATE,

NLTK.

Written Unit

Test – I

(Marks 25)

2 To elaborate on finite state automata

Language Grammar

Regular expressions, Finite State Automata, word

recognition, lexicon, Morphology, acquisition models,

Finite State Transducer, N-grams, smoothing, entropy,

HMM, ME, SVM, CRF. Part of Speech tagging-

Stochastic POS tagging, HMM, Transformation based

tagging (TBL), Handling of unknown words, named

entities, multi word expressions. A survey on natural

language grammars, lexeme, phonemes, phrases and

idioms, word order, agreement, tense, aspect and mood

and agreement, Context Free Grammar, spoken language

syntax.

Assignments

will be given

for the above

topics.

(Marks 10)

3

To introduce

students on

parsing

Parsing

Unification, probabilistic parsing, TreeBank. Semantics-

Meaning representation, semantic analysis, lexical

semantics, WordNet Word Sense Disambiguation-

Selectional restriction, machine learning approaches,

dictionary-based approaches. Discourse- Reference

resolution, constraints on co-reference, algorithm for

pronoun resolution, text coherence, discourse structure

Assignments

will be given

for the above

topics.

(Marks 5)

4

To

demonstrate

uses of NLP

Applications of NLP

Spell-checking, Summarization Information Retrieval-

Vector space model, term weighting, homonymy,

polysemy, synonymy, improving user queries. Machine

Translation– Overview.

Online Class

test will be

conducted.

(Marks 10)


PAGE NO: 33

EVALUATION:




TEXTBOOKS:

1) Daniel Jurafsky and James H Martin. (2009), Speech and Language Processing, 2e,

Pearson Education

REFERENCE BOOKS:

1) James A. (1994), Natural language Understanding 2e, Pearson Education

2) Bharati A., Sangal R., Chaitanya V.. (2000), Natural language processing: a Paninian

perspective, PHI

3) Siddiqui T., Tiwary U. S.. (2008), Natural language processing and Information

retrieval, OUP

COURSE: DATA ANALYTICS AND MINING LAB

CREDIT: 4

Objectives:

To acquire the knowledge of various concepts and tools behind data mining for business intelligence

To Study data mining algorithms, methods and tools

To Identify business applications of data mining

Outcomes:


Apply data mining concepts for analysis of data

Develop industry level data mining skills using software tools

Make use of relevant theories, concepts and techniques to solve real-world business

problems

Code Course

Teaching

Period /

Week

Credit Duration

of Theory

Exam (in

Hrs.) L Pr./

Tu Int. Ext. Total


- 2 1 1 2 1


PAGE NO: 34

Module

No


1

To elaborate the

concept of data

preprocessing

Data Preprocessing

Data cleaning, data transformation, Data

reduction, Discretization and generating

concept hierarchies, Installing Weka 3 Data

Mining System, experiments with Weka -

filters, discretization

Students will be

evaluated using Lab

Manual.

(Marks 05)

3

To implement

classification and

prediction

Data Mining (Supervised Learning) Using

Weka/R Miner

Classification

Prediction

Practical Exam will

be conducted.

(Marks 15)

4

To implement

clustering and

association rule

mining

Data Mining (Unsupervised Learning) using

Weka/R Miner

Clustering

Association Rule Mining

2

To gain detailed

insights of outlier

detection

Outlier Detection

Detection of anomalies, such as the statistical,

proximity-based, clustering-based, and

classification-based methods.

Class Test

(Marks 05)

Softwares used: Advanced Excel, XLMiner,Weka, IBM SPSS Statistics

EVALUATION:




TEXT BOOKS:

1. S. C. Gupta, V. K. Kapoor, (2014), Fundamental of Mathematical Statistics

2. Efraim Turban, Ramesh Sharda, Dursun Delen, David King, (2013), Business Intelligence (2nd

Edition), Pearson

REFERENCE BOOKS:

1. Swain Scheps, (2008), Business Intelligence for Dummies, Wiley Publications

2. Inmon, (1993), Building the Data Warehouse, Wiley

3. Dunham, Margaret H, (2006), Data Mining: Introductory and Advanced Topics, Prentice Hall

4. Witten, Ian and Eibe Frank, (2011), Data Mining: Practical Machine Learning Tools and

Techniques, Second Edition, Morgan Kaufmann

5. MacLennan Jamie, Tang ZhaoHui and Crivat Bogdan, (2009), Data Mining with Microsoft

SQL Server 2008, Wiley India Edition

_______________________________________________________________________________


PAGE NO: 35

COURSE: STATISTICS LAB

CREDIT: 2 Objectives:

To equip the students with a working knowledge of probability, statistics, and modelling in the presence of uncertainties.

To understand the concept of hypothesis and significance tests

To help the students to develop an intuition and an interest for random phenomena and to introduce both theoretical issues and applications that may be useful in real life.


Distinguish between quantitative and categorical data

Apply different statistical measures on data

Identify, formulate and solve problems

Code Course

Teaching

Period /

Week

Credit Duration

of Theory

Exam (in

Hrs.) L Pr./

Tu Int. Ext. Total

MCSL206 Statistics Lab

- 2 1 1 2 1

Module

No.


1 To elaborate software

for data analysis

Introduction to the software used for

data analysis

Environment, entering data and

formatting, handling data files, performing

calculations, handling utilities, formulae

and functions

Lab manual for 05

marks

2

To demonstrate

visualization of data

Visualizing

Handling different types of data variables,

creating tables, frequency distribution

tables and presenting the data in the forms

of Charts, Diagrams, graphs, polygons and

plots

Online test of 10

marks

3

To implement the

methods to find

Measures of Central

Tendency, dispersion,

Skewness

Data Descriptors and Hypothesis

Testing

Measure of Central Tendencies,

Dispersions, skewness, Hypothesis testing

and estimation, Goodness of Fit


marks

4

To perform

Correlation and

regression to analyse

data

Correlation and Regression Using SPSS Statistics find correlation and

regression in sample data

Note: Softwares used: Advanced Excel, XLMiner, IBM SPSS Statistics


PAGE NO: 36

EVALUATION:




TEXT BOOK:

1. S. C. Gupta, V. K. Kapoor, (2014), Fundamental of Mathematical Statistics

REFERENCE BOOKS:

1. Efraim Turban, Ramesh Sharda, Dursun Delen, David King, (2013), Business Intelligence (2nd

Edition), Pearson

2. Swain Scheps, (2008), Business Intelligence for Dummies, Wiley Publications

_______________________________________________________________________________

COURSE: ADVANCED JAVA LAB

CREDIT: 2

Objectives:

To prepare students to excel and succeed in industry / technical profession through global, rigorous education.

Excellence through application development.

To provide students with a solid foundation on Tools, Technology and Framework Outcomes:

Students will demonstrate a high degree of proficiency in programming enabling them for careers in software engineering with competencies to design, develop, implement and integrate software applications and computer systems.

Students will develop confidence for self-education and ability for life-long learning

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam (in

Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1 To implement database connectivity in Java Application

JDBC All data base operation using Access /oracle/MySQL as backend

Lab manual for 05 marks

2

To demonstrate the use of Servlets

Servlets A Simple Servlet Generating Plain text/ HTML, program based on cross page posting and post back posting (client request and server response)

Online test of 15 marks


PAGE NO: 37

3

To demonstrate the use of

Java Server Pages

JSP

Sample program to demonstrate

JSP syntax and semantics, program

based on directive and error object,

program based on cookies and

Sessions


marks

4

To implement MVC

architecture

Introduction to Framework:

Struts

Basic Configuration for struts,

Program based on Action validation

and control in struts, Program based

on integration of JSP and Servlets

with struts


marks

EVALUATION:




TEXT BOOKS:

1) Herbert schildt(2017), The complete reference JAVA2, 5th Ed., Tata McGraw Hill

2) Sharanam Shah and Vaishali Shah(2010), Core Java for beginners, Shroff Publishers and

Distributors

REFERENCE BOOKS:

1) Sharanam Shah and vaishali shah(2014), Struts 2 for beginners, SPD

2) Dreamtech(2007), Advance Java-Savalia,Core, Java 6 Programming Black Book, Wiley

3) Marty Hall and Larry Brown(2003), Core Servlets and Java Server Pages: Vol I: Core

Technologies 2/e , Pearson

4) Sharnam Shah and Vaishali Shah(2011), Java EE 6 for Server Programming for

professionals, SPD

_______________________________________________________________________________

COURSE: ADVANCED PYTHON LAB

CREDIT - 2

Objectives:

To introduce students to use of Python programming to solve data analytics problems

To elaborate students to statistical analysis using Python programming

Outcomes:

The students will be able to improve Problem solving and programming capability

The students will learn data analytics through python programming


PAGE NO: 38

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam (in

Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1

To describe various libraries required for data analytics

Operations using Libraries for data analytics Anaconda, Numpy, Scipy, Pandas, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook: Create Documentation, Code mode, Markdown mode

Lab manual for 05 marks

2

To elaborate statistical analysis using Python

Practical on Statistics using python Mean, Median, Mode, Z-scores, Bias -variance dichotomy, Sampling and t-tests, Sample vs Population statistics, Random Variables, Probability distribution function, Expected value, Binomial Distributions, Normal Distributions, Central limit Theorem, Hypothesis testing, Z-Stats vs T-stats, Type 1 type 2 error, Chi Square test ANOVA test and F-stats

Practical test of 5 marks

3

To study special libraries in Python such as Numpy and Scipy

Practical on Numpy, Scipy NUMPY: Creating NumPy arrays, Indexing and slicing in NumPy, Downloading and parsing data, creating multidimensional arrays, NumPy Data types, Array tributes, Indexing and Slicing, creating array, views copies, Manipulating array shapes I/O, SCIPY: Introduction to SciPy, Create function, modules of SciPy

Practical test of 10 marks

4

To study special libraries in Python such as Numpy and Scipy

Practical on Matplotlib MATPLOTLIB: Scatter plot, Bar charts, histogram, Stack charts, Legend title Style, Figures and subplots, plotting function in pandas, Labelling and arranging figures, Save plots

Online Class test of 5 marks

EVALUATION:

1) On Four Modules of 25 marks 2) Final examination of 25 marks 3) Total marks = Internal 25 + External 25 = 50

TEXT BOOK:

1) Martin C. Brown, (2018), Complete Reference: Python., McGraw Hill REFERENCE BOOKS:

1) Allen Downey, Jeff Elkner and Chris Meyers, (2017), How To Think Like A Computer Scientist: Learning With Python,DreamTech

2) Wesley J Chun, (2018), Core Python Programming, Prentice Hall


PAGE NO: 39

M.Sc. (COMPUTER SCIENCE) SEMESTER - III (SECOND YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int. Ext. Total



MCS302 Artificial Intelligence 4 - 2 2 4 2



MCSL305 Big Data Analytics Lab -

2 1 1 2 1

MCSL306 Machine Learning Lab - 2 1 1 2 1


Lab - 2 1 1 2 1

MCSL308 Ethical Hacking Lab - 2 1 1 2 1

Total 16 8 24 -

SEMESTER-III

1 Credit=25 Marks

Total Credits = 24


COURSE: BIG DATA ANALYTICS AND MACHINE LEARNING

CREDIT - 04

Objectives:

To study the basic of Hadoop

To study the basic of Map-Reduce

To study the basic of NoSQL, Hive, Pig,

To study the basic of Machine Learning

Outcomes:

The course will help:

To understand and learn Hadoop, Map-Reduce, NoSQL

To understand and learn Hive, Pig, Machine Learning


PAGE NO: 40

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total



Module

No.


1

To introduce

student to the

concept of big

Data,

Statistical and

Soft

Computing

Analysis of

Big Data.

Introduction to Big Data Big data: Introduction to Big data Platform, Traits of big

data, Challenges of conventional systems, Web data,

Analytic processes and tools, Analysis vs Reporting,

Modern data analytic tools, Statistical concepts:

Sampling distributions, Re-sampling, Statistical

Inference, Prediction error. Data Analysis: Regression

modeling, Analysis of time Series: Linear systems

analysis, Nonlinear dynamics, Rule induction, Neural

networks: Learning and Generalization, Competitive

Learning, Principal Component Analysis and Neural

Networks, Fuzzy Logic: Extracting Fuzzy Models from

Data, Fuzzy Decision Trees, Stochastic Search Methods.

Unit Test-1

(Marks-25)

2

To introduce

students with

Map-Reduce

based

computing

environment

used for Big

Data Analysis

MAP REDUCE

Introduction to Map Reduce: The map tasks, grouping

by key, the reduce tasks, Combiners, Details of

MapReduce Execution, Coping with node failures.

Algorithms Using MapReduce: Matrix-Vector

Multiplication, Computing Selections and Projections,

Union, Intersection, and Difference, Natural Join.

Extensions to MapReduce: Workflow Systems,

Recursive extensions to MapReduce, Common map

reduce algorithms.

Oral

Presentation

(Marks 10)

3

To

demonstrate

standard linear

methods used

in Machine

Learning

Machine Learning- Standard Linear methods

Statistical Learning, Assessing Model Accuracy. Linear

Regression: Simple Linear Regression, Multiple Linear

Regressions, Other Considerations in the Regression

Model, The Marketing Plan, Comparison of Linear

Regression with K-Nearest Neighbors. Classification:

An Overview of Classification, Why Not Linear

Regression, Logistic Regression, Linear Discriminant

Analysis, A Comparison of Classification Methods.

Class Test

(Marks 10)

4

To

demonstrate

standard non-

linear methods

used in

Machine

Learning

Machine Learning- Non-Linear Learning methods

Polynomial Regression, Step Functions, Basis

Functions, Regression Splines, Smoothing Splines,

Local Regression, Generalized Additive Models, Tree-

Based Methods: The Basics of Decision Trees. Bagging,

Random Forests, Boosting., Support Vector machines,

Principle Component Analysis and Clustering

Assignment

(Marks 05)


PAGE NO: 41

EVALUATION:




TEXT BOOKS:

1. Anand Rajaraman and Jeffrey David Ullman (2012), Mining of Massive Datasets, Cambridge

University Press.

2. Michael Minelli, (2013), Big Data, Big Analytics: Emerging Business Intelligence and Analytic

Trends for Today's Businesses, Wiley

REFERENCE BOOKS:

1. J. Hurwitz, et al., (2013), Big Data for Dummies, Wiley

2. Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George Lapis, (2012),

Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data,

McGraw-Hill

3. James Manyika , Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles

Roxburgh, Angela Hung Byers, (2011), Big data: The next frontier for innovation, competition,

and productivity, McKinsey Global Institute

4. Pete Warden, (2011), Big Data Glossary, O’Reilly

5. David Loshin, (2013), Big Data Analytics: From Strategic Planning to Enterprise Integration

with Tools, Techniques, NoSQL, and Graph, Morgan Kaufmann Publishers

6. Kevin P Murphy, (2012), Machine Learning: A Probabilistic Perspective: The MIT Press

Cambridge

7. Ethem Alpaydın, (2015), Introduction to Machine Learning (Third Edition): The MIT Press

8. Christopher M. Bishop, (2006) Pattern Recognition and Machine Learning: Springer

9. Peter Harrington, (2012), Machine Learning in Action: Manning Publications

10. Brett Lantz, (2013), Machine Learning with R: Packt Publishing

_____________________________________________________________________________

COURSE: ARTIFICIAL INTELLIGENCE

CREDIT - 4

Objectives:

● To Understand various Artificial Intelligence concepts ● To enable the students to identify and describe problems that are open to be solved by AI

methods

Outcomes:


Understand various problems which will be solvable by using Artificial Intelligence

concepts

Learn to write programs using Artificial Intelligence programming languages (LISP and

PROLOG)


PAGE NO: 42

Code Course

Teaching Period

/ Week Credit Duration of

Theory Exam (in

Hrs.) L Pr./

Tu Int. Ext. Total

MCS302

Artificial Intelligence 4 - 2 2 4 2

Module

No.


1 To learn the

concepts of AI

Introduction to Artificial Intelligence Introduction: Concepts & definitions of AI, Brief

history of AI, State space search: Generate and test,

Simple search, Depth First Search (DFS), Breadth First

Search (DFS), Comparison and quality of solutions. Best

First Search (BFS), Hill Climbing, A* algorithm.

Written Unit

Test – I

(Marks 25)

2

To study

propositional logic

and first order

predicate logic and

use the technique

to solve logical

reasoning

problems.

To develop and use

fuzzy arithmetic

tools in solving

problems

Knowledge Representation

Propositional and Predicate Logic: Syntax and

semantics for prepositional logic (PL) and first order

propositional logic (FOPL), Properties of well-formed

formula (wff), Inference rules. First Order Predicate

Logic: Syntax of Predicate Logic, Prenex Normal

Form (PNF), (Skolem) Standard Form, Applications of

FOPL. Deductive Inference Rules and Methods: Basic

Inference Rules and Application in PL, Basic Inference

Rules and Application in FOPL, Resolution Method in

PL and FOPL. Fuzzy Logic: Fuzzy Sets, Fuzzy

Operators & Arithmetic, Membership Functions, Fuzzy

Relations.

Assignments

will be given

for the above

topics.

(Marks 5)

3

T To learn to write

programs using the

syntax of AI

programming

languages (LISP

and PROLOG)

AI Programming Languages & Applications of AI

AI Programming Languages: Introduction to LISP,

Syntax and Numeric Functions, Basic List Manipulation

Functions in LISP Functions, Predicates and

Conditionals, Input, Output, and Local Variables,

Iteration and Recursion, Property Lists and Arrays,

PROLOG: List, Operators, Arithmetic, Cut and Fail

operator, Backtracking.

Assignments

will be given

for the above

topics. (Marks

5)

4

To make a detailed

study of Expert

System

Expert Systems: Introduction and Concept of Planning,

Representing and Using Domain Knowledge Expert

System Shells, Knowledge Acquisition. Intelligent

Agents: Agents and environments, Rationality and other

performance measures, Nature of environments, Structure

of agents.

Online Class

test will be

conducted.

(Marks 15)

EVALUATION:





PAGE NO: 43

TEXT BOOK:

1) Deepak Khemani, (2013), A First course in Artificial Intelligence, Tata McGraw Hill

Education (India) private limited

2) Ben Coppin, Jones, (2004), Artificial Intelligence Illuminated, Bartlett Publishers Inc.

REFERENCE BOOKS:

1) Stuart Jonathan Russell, Peter Norvig, (2010), Artificial Intelligence: A Modern

Approach, 3e, Prentice Hall Publications.

2) M Tim Jones (2008), Artificial Intelligence A Systems Approach, Firewall media, New

Delhi

3) George Lugar, (2002), Artificial Intelligence -Structures and Strategies for Complex

Problem Solving., 4/e, Pearson Education

_____________________________________________________________________________

COURSE: MOBILE APPLICATION DEVELOPMENT CREDIT - 04 Objectives:

To Understand the entire Android Apps Development Cycle

To Apply the advanced android development techniques

To Conceptualize the design of user applications using User Experience Design.


Demonstrate Android activities life cycle

Apply proficiency in coding on a mobile programming platform.

Design and develop innovative android applications

Create real life application with end-to-end understanding of User experience practices

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1 To identify android

platform features

Introduction to Android

The android platform, the layers of android, Four

kinds of android components, understanding the

androidManifest.xml file, creating an android

application

Unit Test-1

(Marks-25)

2

To introduce UI

and data operations

User Interface, Storing and Retrieving data

Creating the activity, working with views, using

resources Working with intents and services, Using

the file system, working with shared preferences,

Oral

Presentation

(Marks 10)


PAGE NO: 44

3

To integrate

android platform

with API

Location Sensors and REST API Integration

Using Location Manager and Location Provider,

working with maps, Working with GPS, Bluetooth

and WiFi, Integrating google maps, services for

push notificationGoogleads, UsingAsyncTask to

perform network operations, introduction to

HtttpUrlConnection and JSON, performing

network operations asynchronously, working with

OkHttp, Retrofit and Volley

Class Test

(Marks 10)

4

To learn database

connectivity in

android application

Database connectivity and distributing android

application

SQLite Programming, Android database

connectivity using SQLite, distribution options,

packaging and testing the application, distributing

applications on google play store

Assignment

(Marks 05)

EVALUATION:




TEXT BOOKS:

1) W. Frank Ableson, Robi Sen, Chris King, C. Enrique Ortiz(2011), Android in action,

Third Edition, Dreamtech Press.

REFERENCE BOOKS

1) Wei-Meng Lee (2012), Beginning Android 4 Application Development, Wrox

Publications

2) Helllo, (2015), Android Introducing Google’s Mobile Development Platform, Fourth Ed,

Burnette, SPD Publications.

_______________________________________________________________________________

COURSE: INFORMATION AND CYBER SECURITY

CREDIT - 04

Objectives:

To develop an understanding of information security as practiced in computer operating

systems, distributed systems, networks and representative applications.

To gain familiarity with prevalent network and distributed system attacks, and defences

against them.

To develop a basic understanding of cryptography, how it has evolved, and some key

encryption techniques used today.

To develop an understanding of security policies (such as authentication, integrity and

confidentiality), as well as protocols to implement such policies in the form of message

exchanges.


PAGE NO: 45

Outcomes:

The students will be able to gain:

Knowledge about the technical and legal terms relating to the cybersecurity, cyber offences

and crimes.

Gain an insight to the Indian Act 2000 and the organizational implications of cyber

Security

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1

To introduce

student to

different types

of computer

security attack

and ethical

hacking

Computer Security Principles of Security, Different Attacks: malicious and

non-malicious program, Types of Computer Criminals.

Operating System Security: Protected objects and

methods of protection. Memory address protection:

Fence, Relocation, Base/Bound Registers, Tagged

Architecture, Segmentation, Paging, Directory, access

control list. Database Security: Security requirements,

Integrity, Confidentiality, Availability, Reliability of

Database, Sensitive data, Multilevel database, Proposals

for multilevel security. Introduction to Ethical Hacking

Students

will be

evaluated

by taking

viva.

(Marks 05)

2

To elaborate

the concept of

Authentication

, Internet

Security,

network

security and

Kerberos

Network Security Different types of network layer attacks, Firewall (ACL,

Packet Filtering, DMZ, Alerts and Audit Trials) – IDS,

IPS and its types (Signature based, Anomaly based,

Policy based, Honeypot based). Web Server Security:

SSL/TLS Basic Protocol-computing the keys- client

authentication-PKI as deployed by SSL Attacks fixed in

v3- Exportability-Encoding-Secure Electronic

Transaction (SET), Kerberos, Secret Key Cryptography,

public key cryptography, Hash function and message

digest

Written

Unit Test –

I

(Marks 25)

3

To elaborate

cloud data

security

Cloud Security

How concepts of Security apply in the cloud, User

authentication in the cloud; How the cloud provider can

provide this- Virtualization System Security Issues: e.g.

ESX and ESXi Security, ESX file system security-

storage considerations, backup and recovery-

Virtualization System Vulnerabilities, security

management standards- SaaS, PaaS, IaaS availability

management- access control- Data security and storage

in cloud.

Written

Class Test

will be

conducted.

(Marks 10)


PAGE NO: 46

4

To

demonstrate

wireless

communicatio

n security

Mobile Security

Mobile system architectures, Overview of mobile

cellular systems, GSM and UMTS Security & Attacks,

Vulnerabilities in Cellular Services, Cellular Jamming

Attacks & Mitigation, Security in Cellular VoIP

Services, Mobile application security. Securing Wireless

Networks: Overview of Wireless Networks, Scanning

and Enumerating 802.11 Networks, Attacking 802.11

Networks, Bluetooth Scanning and Reconnaissance,

Bluetooth Eavesdropping, Attacking & Exploiting

Bluetooth, Zigbee Security & Attacks.

Assignmen

ts will be

given for

the above

topics.

(Marks 10)

EVALUATION:




TEXT BOOKS:

1) Charles P. Pfleeger, Charles P. Pfleeger, Shari Lawrence Pfleeger, (2006), Security in

Computing 4th edition, Prentice Hall; 4th edition

2) Kia Makki, Peter Reiher, (2007), Mobile and Wireless Security and Privacy, Springer

REFERENCE BOOKS:

1) Tim Mather, Subra Kumaraswamy, Shahed Latif., (2009), Cloud Security and Privacy:

An Enterprise Perspective on Risks and Compliance (Theory and practice), O'Reilly

Media; 1 edition

2) Ronald L. Krutz, Russell Dean Vines, (2010), Cloud Security: A Comprehensive Guide to

Secure Cloud Computing, Wiley

3) Charlie Kaufman, Radia Perlam, Mike Speciner, (2010), Network Security, Prentice Hall,

2nd Edition

4) Atul Kahate, (2013), Cryptography and Network Security 3rd edition, Tata McGraw Hill

Education Private Limited

5) William Stallings, (2013), Cryptography and Network Security: Principles and practice

6th edition, Pearson Education

_______________________________________________________________________________

COURSE: BIG DATA ANALYTICS LAB

CREDIT: 2

Objectives:

To enable the students to gain practical knowledge about Hadoop, Map-Reduce

To enable the students to gain practical knowledge about NoSQL, Hive, Pig

Outcomes:


Understand various problem-solving methods using Big Data Analytics techniques

Learn the map-reduce based programming techniques


PAGE NO: 47

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total

MCSL305 Big Data Analytics Lab

- 2 1 1 2 1

Module

No


1

To demonstrate use of

Map-Reduce based

framework to analyse

letters in large text

Occurrences of Letter

Implement Hadoop system, Map-reduce program to

count the number of occurrences of each alphabetic

character in the given dataset. The count for each

letter should be case-insensitive (i.e., include both

upper-case and lower-case versions of the letter;

Ignore non-alphabetic characters).

Students

will be

evaluated

using Lab

Manual.

(Marks 5)

2

To demonstrate use of

Map-Reduce based

framework to analyse

words in large text

Occurrences of Words

Map-reduce program to count the number of

occurrences of each word in the given dataset. (A word

is defined as any string of alphabetic characters

appearing between non-alphabetic characters like

nature's is two words. The count should be case-

insensitive. If a word occurs multiple times in a line,

all should be counted)

Class Test

(Marks 10)

3 To implement Pig

system

Implementation of Pig System

Pig installation, Load Data in Pig from Local

Environment and Query the Data

Practical

Exam will

be

conducted.

(Marks 10) 4

To implement Hive

System Implementation of Hive System

Hive queries, Hive Storage and HDFS

The experiments may be done using software/tools like Hadoop / WEKA / R / Java etc.

EVALUATION:


2) Final examination of 25 markss


TEXT BOOKS:

1. Anand Rajaraman and Jeffrey David Ullman, (2016) Mining of Massive Datasets, Cambridge

University Press.

2. Michael Minelli, (2013), Big Data, Big Analytics: Emerging Business Intelligence and Analytic

Trends for Today's Businesses, Wiley

REFERENCE BOOKS:

1. J. Hurwitz, et al., (2013), Big Data for Dummies, Wiley


PAGE NO: 48

2. Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George Lapis, (2012),

Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data,

McGraw-Hill

3. James Manyika , Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles

Roxburgh, Angela Hung Byers, (2011), Big data: The next frontier for innovation, competition,

and productivity, McKinsey Global Institute





Cambridge




10. Brett Lantz,(2013), Machine Learning with R: Packt Publishing

_______________________________________________________________________________

COURSE: MACHINE LEARNING LAB

CREDIT: 2

Objectives:

To enable the students to gain practical knowledge about algorithms of linear methods in

Machine Learning

To enable the students to gain practical knowledge about algorithms of non-linear methods

in Machine Learning

Outcomes:


Understand various problem-solving methods machine learning techniques

Learn in depth linear and non-linear methods of machine learning

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total

MCSL306 Machine Learning Lab -

2 1 1 2 1

Module

No


1

To demonstrate standard

linear methods

(regression) used in

Machine Learning

Standard Linear methods - Regression

Practical sessions on Statistical Learning,

Assessing Model Accuracy. Linear Regression:

Simple Linear Regression, Multiple Linear

Regressions, Other Considerations in the

Regression Model, The Marketing Plan,

Comparison of Linear Regression with K-Nearest

Neighbors.

Students

will be

evaluated

using Lab

Manual.

(Marks 5)


PAGE NO: 49

2


linear methods

(classification) used in

Machine Learning

Standard Linear methods - Classification

Practical Sessions on Classification: Logistic

Regression, Linear Discriminant Analysis, A

Comparison of Classification Methods

performance.

Class Test

(Marks 10)

3


non-linear tree-based

methods used in

Machine Learning

Non-Linear Learning methods - Tree-Based

Methods

Practical sessions on Polynomial Regression, Step

Functions, Basis Functions, Regression Splines,

Smoothing Splines, Local Regression, Generalized

Additive Models, Tree-Based Methods: The

Basics of Decision Trees. Bagging, Random

Forests, Boosting

Practical

Exam will

be

conducted.

(Marks 10)

4


non-linear SVM, PCA

methods used in

Machine Learning

Non-Linear Learning methods - SVM

Practical sessions on Support Vector machines,

Principle Component Analysis and Clustering

The experiments may be done using software/tools like Hadoop / WEKA / R / Java etc.

EVALUATION:




TEXT BOOKS:




Cambridge

REFERENCE BOOKS:





5. Brett Lantz,(2013), Machine Learning with R: Packt Publishing

COURSE: MOBILE APPLICATION DEVELOPMENT LAB

CREDIT: 2 Objectives:

To Understand the entire Android Apps Development Cycle

To Apply the advanced android development techniques

To Conceptualize the design of user applications using User Experience Design.


Demonstrate Android activities life cycle


PAGE NO: 50

Apply proficiency in coding on a mobile programming platform.

Design and develop innovative android applications

Create real life application with end-to-end understanding of User experience practices

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Lab -

2 1 1 2 1

Module

No


1

To demonstrate the basic components and event handling of an Android application.

Android Platform Introduction to the Android platform and the Android Studio IDE, Android components, Activities, User Interface Design, Intents, Activity lifecycle, UI Design: Widgets and Layouts, UI Events, Event Listeners

Students

will be

evaluated

using Lab

Manual.

(Marks 5)

2

To describe the basics of graphics and multimedia support in Android.

To demonstrate basic skills of using an Android SDK for implementing Android applications.

Graphics Support in Android Drawables, Basics of Material Design, 2D graphics: Canvas/Drawing using a view, multimedia in Android: Audio playback and MediaPlayer, SoundPool

Class Test

(Marks 10)

3

To demonstrate skills of using networking concepts in Android

Networking support Basics of networking in Android, AsyncTask, HttpURL Connection

Practical

Exam will

be

conducted.

(Marks 10)

4

To demonstrate use of database connectivity in Android

Database connectivity and distributing and android application SQLite Programming, Android database connectivity using SQLite, distribution options, packaging and testing the application, distributing applications on google play store

EVALUATION:




TEXT BOOKS:

1) W. Frank Ableson, Robi Sen, Chris King, C. Enrique Ortiz(2011), Android in action,

Third Edition, Dreamtech Press.


PAGE NO: 51

REFERENCE BOOKS:

1) Wei-Meng Lee(2012), Beginning Android 4 Application Development, Wrox

Publications

2) Helllo, Android Introducing Google’s Mobile Development Platform, Fourth Edition, Ed

Burnette(2015), SPD Publications

_____________________________________________________________________________

COURSE: ETHICAL HACKING LAB

CREDIT: 2

Objectives:

To acquire hands-on working skill set which includes Vulnerability Assessment, Network

Infrastructure, Network Securities, Network Exploitation, Red Hat Linux Security.

Outcomes:


Demonstrate ethical hacking techniques

Learn security of sensitive data and websites

Code Course

Teaching

Period /

Week

Credit Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total

MCSL308 Ethical Hacking Lab -

2 1 1 2 1

Module

No


1

To learn

Footprinting

concept

Introduction

Introduction to Ethical Hacking, Foot printing,

Surveying & Gathering Data, Understanding IP

& MAC addresses., concepts of TCP/IP, Basic

networking concepts, Understanding domain

registrations & Webhosting concepts

Students will be

evaluated using Lab

Manual.

(Marks 5)

2

To learn scanning

of network

Scanning Network

Overview of Network Scanning, CEH Scanning

Methodology, Check for Live Systems, ICMP

Scanning, Ping Sweep Tools, Check for Open

Ports, Network scanning, Network Pentesting,

Viruses, worms & Trojans, Ethical hacking

Methods (Key loggers, phishing, RAT)

Class Test

(Marks 10)

3

To learn methods

of password

security

Password Security

Passwords Cracking, Hacking through Social

Engineering, Cryptography, Steganography

Practical Exam will

be conducted.

(Marks 10)

4

To learn the

concept of Denial

of Service attack

Denial of Service attack

SQL Injections, Denial of Service, Cross-site

scripting (XSS), Firewalls configurations &

Bypassing


PAGE NO: 52

EVALUATION:




TEXT BOOKS:

1) Shekhar Mishra, (2017), Ethical Hacking for Beginners 2019: Complete step by step

Guide Beginner to Advance, PHI

REFERENCE BOOKS:

1) Patrick Engebretson, (2015), The Basics of Hacking and Penetration Testing: Ethical

Hacking and Penetration Testing Made Easy, Syngress Basics Series

2) James Clark (2017), Geek Collection 7 in 1 Box Set: Computer Hacking Guide for

Beginners, SQL, Google Drive, Project Management, Amazon FBA, LINUX, Excel, TMH


PAGE NO: 53

M.Sc. (COMPUTER SCIENCE) SEMESTER - IV (SECOND YEAR)

Code Subject Title

Teaching

Period /

Week

Credit

Duration

of

Theory

Exam

(in Hrs.)

L Pr./

Tu Int.

Ext.

Total


MCS402 Elective II 4 - 2 2 4 2



Total 8 16 24 -

SEMESTER-IV

1 Credit=25 Marks

Total Credits = 24


Elective II


MCS402A Digital Image Processing

MCS402B Robotics

MCS402C Blockchain Technology

MCS402D Modeling and Simulation

_______________________________________________________________________________

COURSE: CLOUD COMPUTING

CREDIT - 04

Objectives:

To learn the concept of parallel and distributed computing

To enable the students to gain knowledge of cloud-based computing technologies

To learn to deploy cloud-based computing environment

Outcomes:


Design and implement software application in a cloud environment.

Manipulate large data sets in a parallel computing environment.


PAGE NO: 54

Code Course

Teaching Period

/ Week Credit

Duration

of

Theory

Exam

(in Hrs.) L

Pr./

Tu Int. Ext. Total


Module

No.


1

To elaborate the

concept of

parallel and

distributed

computing and

virtualization

Parallel, Distributed Computing and

Virtualization

Elements of parallel computing, elements of

distributed computing, Technologies for distributed

computing: RPC, Distributed object frameworks,

Service oriented computing, Virtualization –

Characteristics, taxonomy, virtualization and cloud

computing.

Unit Test-1

(Marks-25)

2

To introduce

students with

cloud computing

services

Computing Platforms and Cloud technologies

Cloud Computing definition and characteristics,

Enterprise Computing, The internet as a platform,

Cloud computing services: SaaS, PaaS, IaaS,

Enterprise architecture, Types of clouds, Cloud

computing platforms, Web services, AJAX, mashups,

multi-tenant software, Concurrent computing: Thread

programming, High-throughput computing: Task

programming, Data intensive computing: Map-

Reduce programming.

Oral

Presentation

(Marks 10)

3

To demonstrate

the use of cloud-

based software

architecture

Software Architecture Dev 2.0 platforms, Enterprise software: ERP, SCM,

CRM, Custom enterprise applications and Dev 2.0,

Cloud applications.

Class Test

(Marks 10)

4

To demonstrate

the use of cloud-

based services

provider

Amazon Web Services (AWS) Essentials Architecting on AWS, building complex solutions

with Amazon Virtual Private Cloud (Amazon VPC),

Leverage bootstrapping and auto configuration in

designs, Architect solutions with multiple regions,

Employ Auto Scaling design patterns, Amazon

CloudFront for caching, Big data services including

AWS Data Pipeline, Amazon Redshift and Amazon

Elastic MapReduce. AWS OpsWorks.

Assignment

(Marks 05)

EVALUATION:




TEXT BOOKS:

1) Gautam Shroff, (2010), Enterprise Cloud Computing Technology, Architecture,

Applications, Cambridge University Press

2) Mastering In Cloud Computing (2013), Tata Mcgraw-Hill Education


PAGE NO: 55

REFERENCE BOOKS:

1) Rajkumar Buyya, Christian Vecchiola And Thamari Selvi S, (2009), Cloud Computing: A

Practical Approach, Anthony T Velte, Tata Mcgraw Hill

2) Michael J. Kavis, (2014), Architecting the Cloud: Design Decisions for Cloud Computing

Service Models (SaaS, PaaS, and IaaS), Wiley CIO

3) Kris Jamsa, Jones (2013), Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business

Models, Mobile, Security and More, Bartlett Learning

4) AWS Training, http://aws.amazon.com/training.

_______________________________________________________________________________

COURSE: ELECTIVE II – DIGITAL IMAGE PROCESSING

CREDIT - 4

Objectives:

To enable the understand the concepts of output primitives of Computer Graphics.

To Learn 2 D and 3 D graphics Techniques.

To Study various Image Processing techniques

Outcomes:


Understand various 2D Geometric Transformations & Clipping,

Understand the basic 3D Concepts & Fractals, Introduction of Animation, Image

Enhancement Techniques

Code Course

Teaching Period

/ Week Credit

Duration

of

Theory

Exam


MCS402A Digital Image Processing 4 - 2 2 4 2

Module

No.


1

To introduce

students to image

processing

concepts

Introduction

Fundamental Steps in Digital Image Processing:

Components of an Image Processing System, Basic

Concepts in Sampling and Quantization, Representing

Digital Images, Spatial and Gray-Level Resolution.

Written Unit

Test – I

(Marks 25)

2

To demonstrate

techniques of

image

enhancement

Image Enhancement in the Spatial Domain

Some Basic Intensity Transformation Functions: Image

Negatives, Log Transformations, and PowerLaw

Transformations. Piecewise-Linear

Assignments

will be given

for the above

topics.

(Marks 5)

3

To demonstrate

the concept of

transformation of

image

Transformation Functions

Contrast stretching, Gray-level slicing, Bit plane

slicing. Histogram Processing: Image Histogram and

Histogram Equalization, Image Subtraction, and Image

Averaging.

Assignments

will be given

for the above

topics.

(Marks 5)


PAGE NO: 56

4 To elaborate

filtering concept

Spatial Filtering

Basics of Spatial Filtering, Smoothing Spatial Filters

Smoothing Linear Filters, Order-Statistics Filters.

Sharpening Spatial Filters: Use of Second Derivatives

for Enhancement–The Laplacian, Unsharp masking and

High-Boost Filtering: Use of First Derivatives for

(Nonlinear) image sharpening - The Gradient– Robert,

Prewitt and Sobel Masks. Combining Spatial

Enhancement Methods.

Online Class

test will be

conducted.

(Marks 15)

EVALUATION:




TEXT BOOKS:

1) Amrendra Sinha, ArunUdai, (2007), Computer Graphics –Tata McGraw-Hill Education

2) Rajesh K. Maurya (2011), Computer Graphics -- Wiley India Pvt. Limited

REFERENCE BOOKS:

1) Donald Hearn and M Pauline Baker, (2007), Computer Graphics C Version -- Computer

Graphics, C Version, 2/E, Pearson Education.

2) Rafael C. Gonzalez and Richard E. Woods, (2010), Digital Image Processing (3rd

Edition), Pearson Education.

3) Roy A. Plastock, Roy A. Plastock- (2009), Schaum's Outline of Computer Graphics 2/E

4) James D. Foley, Andries van Dam, Steven K. Feiner, John F. Hughes,(2000), Computer

Graphics: Principles and Practice in C, Pearson Education.

5) David F. Rogers, James Alan Adams, (1990), Mathematical elements for computer

graphics, McGraw-Hill

6) Peter Shirley, Stephen Robert Marschner (2009) Fundamentals of Computer Graphics A

K Peters, Limited, 3rd ed.

7) Anil K. Jain, (1989), Fundamentals of digital image processing, Prentice Hall

_______________________________________________________________________________

COURSE: ELECTIVE II – ROBOTICS

CREDIT - 4

Objectives:

To enable students to design an agent that is Robot

To enhance understanding in implementation of Robot

Outcomes:


Understand Robots design and implementation in detail

Understand detailed working of Robot


PAGE NO: 57

Code Course

Teaching Period

/ Week Credit

Duration

of

Theory

Exam


MCS402B Robotics 4 - 2 2 4 2

Module

No.


1

To study the basics of

the robot and the

theory behind it.

Introduction to Robotics What is a Robot? Definition, History of Robots:

Control Theory, Cybernetics, Grey Walter Tortoise,

Analog Electronic Circuit, Reactive Theory,

Braitenberg’s Vehicle, Artificial Intelligence, Vision

Based Navigation, Types of Robot Control.

Written Unit

Test – I

(Marks 25)

2

To study the different

components of the

Robot and the actions

the robot would

perform

Robot Components

Embodiment, Sensors, States, Action, Brains and

Brawn, Autonomy, Arms, Legs, Wheels, Tracks, and

What really drives them effectors and actuators:

Effector, Actuator, Passive and Active Actuation,

Types of Actuator, Motors, Degree of freedom

Locomotion: Stability, Moving and Gaits, Wheels

and Steering, Staying on the path. Manipulators: End

effectors, Teleoperation, why is manipulation hard?

Sensors: Types of Sensors, Levels of Processing,

Passive and Active sensors, Switches, Light sensors,

Resistive position sensor.

Assignments

will be given

for the above

topics.

(Marks 5)

3

To elaborate on

sensing through Sonar,

Lasers and Cameras

Sonar, Lasers and Cameras

Ultrasonic and Sonar sensing, Specular Reflection,

Laser Sensing, Visual Sensing, Cameras, Edge

Detection, Motion Vision, Stereo Vision,

Biological Vision, Vision for Robots, Feedback or

Closed Loop Control: Example of Feedback

Control Robot, Types of feedback control, Feed

forward or Open loop control.

Assignments

will be given

for the above

topics.

(Marks 5)

4 To study languages to

program Robot

Languages for Programming Robot

Algorithm, Architecture, many ways to make a

map, what is planning, Cost of planning, Reactive

systems, Action selection, Subsumption

architecture, How to sequence behavior through

world, hybrid control, Behavior based control and

Behavior Coordination, Behavior Arbitration,

Distributed mapping, Navigation and Path

planning.

Online Class

test will be

conducted.

(Marks 15)

EVALUATION:





PAGE NO: 58

TEXT BOOK:

1) Deepak Khemani, (2013), A First course in Artificial Intelligence, Tata McGraw Hill

Education (India) private limited

REFERENCE BOOKS:

1) Maja J Matarić, (2007), The Robotics Primer, MIT press Cambridge, Massachusetts,

London, England

2) Milan Sonka,Vaclav Hlavac, Roger Boyle (2007), Image Processing, Analysis, and

Machine Vision, Thomson Learning

3) Robert Haralick and Linda Shapiro (1993), Computer and Robot Vision, Vol I, II,

Addison-Wesley.

_______________________________________________________________________________

COURSE: ELECTIVE – BLOCKCHAIN TECHNOLOGY

CREDIT - 4

Objectives:

To elaborate the functional/operational aspects of cryptocurrency ECOSYSTEM.

To Understand emerging abstract models for Blockchain Technology.

To Identify major research challenges and technical gaps existing between theory and

practice in cryptocurrency domain

Outcomes:


Understand various Blockchain, Ethereum Blockchain, and Algorithms and Techniques

Understand the concept of Trust Essentials, Hyperledger, Smart Contracts, Fabric

Composition

Code Course

Teaching Period

/ Week Credit

Duration

of

Theory

Exam


MCS402C Blockchain Technology 4 - 2 2 4 2

Module

No.


1

To introduce students

to Blockchain

technology and its

fundamentals

Introduction to centralized/decentralized

currency

Intent of centralized/decentralized currency, the

consensus problem - Asynchronous Byzantine

Agreement - AAP protocol and its analysis -

Nakamoto Consensus on permission-less, nameless,

peer-to-peer network - Abstract Models for

BLOCKCHAIN - GARAY model - RLA Model -

Proof of Work ( PoW) as random oracle - formal

Written Unit

Test – I

(Marks 25)


PAGE NO: 59

treatment of consistency, liveness and fairness -

Proof of Stake ( PoS) based Chains - Hybrid

models ( PoW + PoS)

2

To introduce students

to the basics of

cryptography and

cryptocurrency

Cryptographic basics for cryptocurrency

Short overview of Hashing, signature schemes,

encryption schemes and elliptic curve

cryptography, Bitcoin - Wallet - Blocks - Merkley

Tree - hardness of mining - transaction verifiability

- anonymity - forks - double spending -

mathematical analysis of properties of Bitcoin

Assignments

will be given

for the above

topics.

(Marks 5)

3 To elaborate the

concept of EVM

Ethereum

Ethereum Virtual Machine (EVM) - Wallets for

Ethereum - Solidity - Smart Contracts - some

attacks on smart contracts.

Assignments

will be given

for the above

topics.

(Marks 5)

4

To demonstrate new

trends in Blockchain

technology

Trends and Topics Zero Knowledge proofs and protocols in

Blockchain - Succinct non interactive argument for

Knowledge (SNARK) - pairing on Elliptic curves -

Zcash.

Online Class

test will be

conducted.

(Marks 15)

EVALUATION:




TEXT BOOKS:

1) Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven

Goldfeder.(2016), Bitcoin and cryptocurrency technologies: a comprehensive

introduction. Princeton University Press.

REFERENCE BOOKS:

1) Joseph Bonneau et al, SoK: Research perspectives and challenges for Bitcoin and

cryptocurrency, IEEE Symposium on security and Privacy, 2015

2) J.A.Garay et al, The bitcoin backbone protocol - analysis and applications EUROCRYPT

2015 LNCS VOl 9057, ( VOLII ), pp 281-310. (eprint.iacr.org/2016/1048)

3) R.Pass et al, Analysis of Blockchain protocol in Asynchronous networks , EUROCRYPT

2017, ( eprint.iacr.org/2016/454) . A significant progress and consolidation of several

principles). 4. R.Pass et al, Fruitchain, a fair blockchain, PODC 2017 (

eprint.iacr.org/2016/916).

_______________________________________________________________________________


PAGE NO: 60

COURSE: ELECTIVE – MODELING AND SIMULATION

CREDIT - 4

Objectives:

To provide basic understanding of Modeling and Simulation

Students will find it easy to use this knowledge in profession for applying to various

systems and design

Outcomes:


Understand the techniques of modeling in the context of hierarchy of knowledge about a

system and develop the capability to apply the same to study systems

Students will learn different types of simulation techniques.

Code Course

Teaching Period

/ Week Credit

Duration

of

Theory

Exam


MCS402D Modeling and Simulation 4 - 2 2 4 2

Module

No.


1

Students will study the

basics of modeling

paradigms appropriate

for conducting

simulations.

Simulation Concepts

Systems, modeling, general system theory,

concept of simulation, simulation as a decision-

making tool, types of simulation.

Written Unit

Test – I

(Marks 25)

2

Students will learn

various distributions and

testing of random

numbers

Random Numbers

Pseudo random numbers, methods of generating

random varieties, discrete and continuous

distributions, testing of random numbers.

Assignments

will be given

for the above

topics.

(Marks 5)

3

Students will understand

the concept of designing

simulation experiments

Design and simulation experiments

Problem formulation, data collection and

reduction, time flow mechanism, key variables,

logic flow chart, starting condition, run size,

experimental design consideration, output

analysis and interpretation validation.

Assignments

will be given

for the above

topics.

(Marks 5)

4

Students will learn

various simulation-based

case studies

Simulation Languages and Case Studies

Comparison, and selection of simulation

languages, study of any one simulation language,

development simulation models using the

simulation language studied for systems like

queuing systems, production systems, inventory

systems.

Online Class

test will be

conducted.

(Marks 15)


PAGE NO: 61

EVALUATION: 1) On Four Modules of 50 marks 2) Final examination of 50 marks 3) Total marks = Internal 50 + External 50 = 100

TEXT BOOKS:

1. Ross, (2010), Simulation, 4e, Elsevier, ISBN-9788131214626 REFERENCE BOOKS:

1. Zeigler (2018), Theory of Modeling and Simulation, 2e, Elsevier, ISBN-9788131207406 2. Birta (2013), Modeling and Simulation: Exploring Dynamic System Behaviour, Springer,

IBSN978-81-8489-365-6 3. Jerry Banks and John, S. Carson (2009), Discrete Event System Simulation, PHI 4. Shannon, R.E (1975)., Systems Simulation, The Art and Science, PHI

_________________________________________________________________

COURSE: RESEARCH PAPER WRITING

CREDIT - 4

Objectives:

To enable the students to gain experience of identification of research problem

To perform research to solve real time problem using new technologies

Outcomes:


Understand various processes involved in writing research paper

Write research paper in specified format

Code Course

Teaching Period

/ Week Credit

Duration

of

Theory

Exam



Module

No.


1

To help students to identify research problem

Research Problem Identification and Literature

Review

Presentation

(Marks 10)

2

To design the experiment to be conducted

Research Design Presentation

(Marks 10)

3

To conduct experiment after data collection

Data Collection, Experiment Conducted Presentation

(Marks 15)

4

To perform analysis and presentation of results

Data Analysis and Result Presentation Presentation

(Marks 15)


PAGE NO: 62

EVALUATION:




TEXT BOOKS: 1) Brinoy J Oates, (2006), Researching Information Systems and Computing, Sage Publications

India Pvt Ltd REFERENCE BOOKS: 1) Kothari, C.R., (1985), Research Methodology, Methods and Techniques, third edition, New

Age International 2) Juliet Corbin & Anselm Strauss, (2008), Basic of Qualitative Research (3rd Edition), Sage

Publications 3) Willkinson K.P, L Bhandarkar, (2010), Formulation of Hypothesis, Hymalaya Publication,

Mumbai 4) John W Best and V. Kahn, (2010), Research in Education, PHI Publication. _______________________________________________________________________________

COURSE: SOFTWARE PROJECT CREDIT: 12 Objectives:

Achieve hands on experience in an organization

Relate classroom and textbook learning to the real world.

Learn the professional skills and interpersonal relationship in professional environment

Outcomes: The students will be able to

Attain an exposure to real life organizational and environmental situations

Attain technical skills as per the requirements of the domain

Adapt professional and interpersonal ethics.

Articulate SDLC phases in developing software project and in writing the project document.

Code Course

Teaching Period /

Week Credit

Duration of

Theory

Exam (in

Hrs.) L Pr./ Tu Int. Ext. Total


Module

No.


1

To help students to identify

problem, check its

feasibility, gather

requirements and analyse

them

Problem Identification, Feasibility

study, Requirement Gathering,

Requirement Analysis

Presentation 1

(50 marks)


PAGE NO: 63

2 To help students to plan

project activities

Project planning, design Presentation 2

(30 marks)

3 To perform software coding

and testing

Project Coding and Testing Presentation 3

(20 marks)

4 To present the developed

software

Final Presentation of the Project Presentation 4

(50 marks)

EVALUATION:

1) On four Modules of 150 marks



TEXT BOOKS:

1) Roger S Pressman (2019), Software Engineering, 8th edition, McGraw Hill publication.

2) Kathy Schwalbe (2014), Managing Information Technology Project, 6th edition, Cengage

Learning publication.

REFERENCE BOOKS:

1) Jack T Marchewka (2013) , Information Technology Project Management , Wiley India

publication.

2) KK Agrawal, Yogesh Singh (2008), Software Engineering 3rd edition by New Age

International publication.

3) Kogent Learning Solutions Inc(2012), Software Engineering, Dreamtech Press.

4) Douglas Bell (2005), Software Engineering for students: A Programming Approach,

Pearson publication.

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Kogent+Learning+Solutions+Inc.&search-alias=stripbooks