Six week summer training report on

CRIME SCENE ANALYSIS

A six week summer Training project report submitted in partial fulfillment of the

requirements for the award of the degree of

Bachelor of Technology

Submitted by: Under the Guidance of

Aminuz Zaman Mondal Mr. Nikesh Bajaj SirReg. No. 11112408. Asst. Professor.

Program: B. Tech. Lovely Professional University.Section: E2104 Dated: 05/08/2014

LOVELY PROFESSIONAL UNIVERSITY

CERTIFICATE

It certifies that this project report entitled “Crime Scene Analysis” submitted by “AMINUZ

ZAMAN MONDAL, Reg. No.11112408” student of Electronics and Communication

Engineering Department, Lovely Professional University, Phagwara, Punjab who carried out

the project work under my supervision.

This report has not been submitted to any other university or institution for the award of any degree.

SIGNATURE SIGNATURE

Mr. Nikesh Bajaj Mr. Nikesh Bajaj

HEAD OF THE DEPARTMENT SUPERVISOR

ECE

ABSTRACT

This is a report on the six week summer training I have gone through during the period from 9th June 2014 to 18th July 2014 under cognizant technology solutions limited (CTS) organized by Lovely Professional University.

As a part of the digital world, the world has been eased to work on with the help of big data and data analysis. Throughout the training we have been taught the basic concepts of working on the data using different tools and programming skills like SAS, Microsoft Excel, and Open Office.

As a part of the training program I have done this project on detailed analysis on scene of crimes and criminals in England and Wales.

In this report I presented crime scene on the basis of different categories of crimes, different types of criminals according to sex, age group, place of crime and weather sentenced with period or taken under remand or still un-judged.

And finally I tried to find some resolutions so that we can work those that can help reduce the frequencies of the crimes, and can make great awareness against crimes and criminals.

ORGANISATION OVERVIEW

Cognizant Technology Solutions Corporation is a multinational information technology, consulting and business process outsourcing company. It is headquartered in Teaneck, New Jersey, United States, North America.

Industry : IT services, IT consulting

Predecessor(s) : Dun & Bradstreet

Founded : January 26, 1994

Founder(s) : Kumar Mahadeva

Srini Raju

Headquarters : Teaneck, New Jersey, United States

Area served : Worldwide.

Key people : John E. Klein (Chairman),

Francisco D'Souza (CEO).

Revenue : US$ 8.843189 billion (2013)

US$ 7.346472 billion (2012)

Operating income: US$ 1.67791 billion (2013) [1]

US$ 1.361496 billion (2012)

Services : IT, business consulting and outsourcing services.

Employees : 178,000 (March 2014)

Website : www.cognizant.comCognizant provides information technology, consulting and BPO services. These include business & technology consulting, systems integration, application development & maintenance, IT infrastructure services, analytics, business intelligence, data warehousing, CRM, supply chain management, engineering & manufacturing Solutions, ERP, R&D outsourcing, and testing solutions.

Cognizant Technology Solutions Corporation originally founded as an in-house technology unit of Dun & Bradstreet in 1994, with headquarters in Chennai, India, Cognizant started serving external clients in 1996. In 1997, the headquarters were moved from Chennai to Teaneck, New Jersey. Cognizant's IPO was launched in 1998, after a series of corporate splits and restructures of its parent companies, the first Indian software services firm to be listed on the NASDAQ. During the dot com bust, it grew by accepting the application maintenance work that the bigger players were unwilling to perform. Gradually, it ventured into application development, complex systems integration and consulting work.

Cognizant saw a period of fast growth during the 2000s, becoming a Fortune 500 company in 2011. In 2011, the Fortune magazine named it as the world's third most admired IT Services Company after Accenture and IBM.

HISTORY:-

Cognizant has its roots in The Dun & Bradstreet Corporation, a joint venture between Dun & Bradstreet (76%) and Satyam Computers (24%).Srini Raju was the CEO of this company established in 1994.[8] Kumar Mahadeva played a major role in convincing D&B to invest $2 million in the joint venture. He was born in Sri Lanka, where his father led his nation's civil service. Mahadeva travelled to England for his studies, earning a master's degree in electrical engineering from Cambridge in 1973. Originally called DBSS, the unit was established as an in-house technology unit, and focused on implementing large-scale IT projects for D&B businesses. In 1996, the company started pursuing customers beyond the D&B fold.

In 1996, Dun & Bradstreet (D&B) spun off several of its subsidiaries including Erisco, IMS International, Nielsen Media Research, Pilot Software, Strategic Technologies and DBSS, to form a new company called Cognizant Corporation. Three months later, in 1997, DBSS was renamed as Cognizant Technology Solutions. In July 1997, D&B bought Satyam's 24% stake in DBSS for $3.4 million. Headquarters were moved to the United States, and in March 1998, Kumar Mahadeva was named CEO. Operating as a division of the Cognizant Corporation, the company mainly focused on Y2K-related projects and web development.

In 1998, the parent company Cognizant Corporation was split into two companies: IMS Health and Nielsen Media Research After this restructuring, Cognizant Technology Solutions became a public subsidiary of IMS Health. In June 1998, IMS Health partially spun off the

company, conducting an initial public offering of the Cognizant stock. The company raised $34 million, less than what the IMS Health underwriters had hoped for. The money was earmarked for debt payments and upgrading of the company's offices.

Kumar Mahadeva decided to reduce the company's dependence on Y2K projects: by Q1 1999, 26% of company's revenues came from Y2K projects, compared with 49% in early1998. Believing that the $16.6 billion ERP software market was saturated, Mahadeva decided to refrain from large-scale ERP implementation projects. Instead, he focused on applications management, which accounted for 37% of Cognizant's revenue in Q1 1999. Cognizant's revenues in 2002 were $229 million, and the company had zero debt with $100 million in the bank. During the dotcom bust, the company grew by taking on the maintenance projects that larger IT services companies did not want.

In 2003, IMS Health sold its entire 56% stake in Cognizant, which instituted a poison pill provision to prevent hostile takeover attempts. Kumar Mahadeva resigned as the CEO in 2003, and was replaced by Lakshmi Narayanan. Gradually, the company's services portfolio expanded across the IT services landscape and into business process outsourcing (BPO) and business consulting. Lakshmi Narayanan was succeeded by the Kenya-born Francisco D'Souza in 2006. Cognizant experienced a period of fast growth during the 2000s, as reflected by its appearance in Fortune magazine's "100 Fastest-Growing Companies" list for ten consecutive years from 2003 to 2012.

DECLARATION

I hereby certify that the work, which is being presented in the report/ Training/Project entitled “Crime Scene Analysis”, in partial fulfilment of the requirement for the award of the Degree of Bachelor of Technology submitted to the institution is an authentic record of our own work carried out during the period 9th June -2014 to 18th July -2014 under the supervision of Mr. Nikesh Bajaj, CTS representative, Lovely Professional University. I also cited the reference about the texts and figure from where they have been taken.

Date: 04-08-14 Signature of the Candidate

This is to certify that the above statement made by the candidate is correct to the best of my /our knowledge.

Mr. Nikesh Bajaj

Date: 04-08-14 Signature of the Supervisor

Objectives of Training and Project

The six week summer training under CTS title organized by Lovely Professional University was very interactive and effective in learning new things for practical industrial professional life. The mentors were enough cooperative and helpful to guide us with every new and difficult steps. Looking the span of the report size we will try to summarize the report in few key points of our learning. The training was an efficient try for six week time period, during the period we have gone through many new concepts, those are basically

Learning working on statistical huge data.

Learnt basic concepts of working on SQL Server.

Working on data using SAS Programming.

Learnt basic features of Microsoft Excel, plotting graph, pivot tables etc.

We have been taught to make dashboard

Made a basic concept of programming in open office data sheet.

We did some case studies on big data analysis daily wise.

We have been given different demos and sites that provide us detailed examples of different aspects of data analysis.

I made this project work entitled “CRIME SCENE ANALYSIS” on the data of crime report during a specific period as a part of our learning and result.

We are very thankful to our mentors and the organizers of the training programme and their great efforts.

MOTIVATION OF THE TRAINING

During training we are regularly motivated by our organizational director MR.NIKESH BAJAJ and our training coordinator MR.BARJINDER, MR.RAVINDER SIR, who gave me proper guidance on every step of learning

There are some factors that exist in our organization that boost up our confidence level to a much higher level and determine our levels of motivation whether positive or negative. Fortunately, each of the students is equally motivated and dedicated to the organization during their training.

BASIC FACTORS THAT ENHANCES OUR MOTIVATION

1. Team work

2. Structure of work

3. Weekly evaluation

Team work-during our training period team teamwork is our basic motivational factor that made our work easier with my friends and coordinator. Team work during our leads good coordination amongst us and helps in accomplishment of our task

Structure of work- training period is inherently motivational, creative, imagination, and high levels of energy. Training work involves communicating, negotiating, and interacting with other people in order to gain their cooperation with others and well brings out the best part of us. It was exciting and challenging.

However, an enormous amount of work is done in standardized, reutilized, and made relatively unexciting in order to be done efficiently and cost effectively. Its very interesting to learn new thing from our training coordinator, practical on daily basis made our understanding much betterOrganizations always trying to structure the work so as to match the nature of the work with the students and to make the work as interesting and enjoyable as possible.

Weekly evaluation-during our training session our training coordinator takes weekly online test on the basis of which we have been provided regular grades and we were able to improve our performance during our training.

Leadership-during my six weeks training session: some moments our coordinator asked us to step out to explain the things which we have learnt during training. This enhanced leadership quality as well the confidence.

Challenges faced: the challenges faced during the understanding assignment and new technology enhanced my will to learn and absorb new things those came along.

PREFACE

Partial knowledge is an impotent suffix to theoretical knowledge; one cannot merely rely upon the theoretical knowledge. Classroom make the fundamental concept clear, but practical survey in a firm has significant role to play in a subject of Business Management to develop managerial skills, it is necessary that they combine their classroom's learning with the knowledge of real business environment.

I am extremely happy to place before the esteemed Teachers/Management the Report of the project entitled "Crime Scene Analysis".

It has not only helped me to enhance my knowledge about various fields of Human Resources & Company responsibilities towards their welfare but also gave new dimension to my knowledge about psychology & attitude of the Employees towards the work & their duties.

ACKNOWLEDGEMENTS

I ‘AMINUZ ZAMAN MONDAL’ would like to take this opportunity to thank Mr. Nikesh Bajaj Sir who has given us such a wonderful platform to enhance our practical skills and helping us throughout the training and providing valuable suggestions and support wherever required. Also we are thankful to all the authorities who organized the training program in our campus and have always provided their hand whenever we needed it.

Also, I want to thank members of Lovely Professional University for allowing us to take this summer training.

The data I collected is from United Kingdom government site https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/276084/prison-population-tables-q3-2013.xls

CONTENTS

1. Introduction 2. Brief discussion of data analysis 3. Our analyzed figures and results

3.1 Analysis of prison population by continents. 3.2 Comparison of prisoners by continent and subcontinent. 3.3 Frequency of prisoners by their nationality. 3.4 Analysis of prisoners by their sex. 3.5 Analysis of prison population by their different age groups. 3.6 Differentiation of Prisoners by their type of custody 3.7 Analysis of prison Population by the different time period 3.8 Prisoners and Crimes according to type of violence. 3.9 Comparison of prisoners by the length of the tariff or sentence. 3.10. Male and Female prisoners by different ages. 4. Learning Outcomes5. Conclusion

REFERENCES

Chapter 1

INTRODUCTION

Data analysis is a process a series of connected activities designed to obtain meaningful information from data that have been collected, as the graphics demonstrate. The process can be conceptualized in different ways (linear or cyclical).

Crimes are the most critical issue in civilized world. Since uncivilized human era, criminals and crimes remained the most difficult factor to tackle with.

So, I am here trying to analyze the crimes in figures and data in England and Wales and compared with different parts of the world. I am here trying to find some results and conclusions that can help a little bit to see the crime scene in structured format and can be used to help reduce the frequency of the crime.

I applied different statistical methodology to find the results according to different type of category.

Mostly I tried to present the results in graphical format so that we can easily visualize the conclusion. There are explanations and comparisons in each graph and chart we presented in this report. I tried to find ratios between different aspects of crimes, criminals, and the domain of the crimes.

Chapter 2

A brief discussion on Data Analysis

The process of evaluating data using analytical and logical reasoning to examine each component of the data provided. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed and then analyzed to form some sort of findings and conclusions. There are variety of specific data analysis method, some of which include DATA MINING, text analytics, business intelligence and visualizations.

Data Mining is the process of shifting through very large amount of data for useful information, data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools (such as cluster analysis) to reveal trends, patterns, and relationships, which might otherwise have remained undetected. In contrast to an expert system (which draws inference from the given data on the basis of a given set of rules). Data mining attempts to discover hidden rules underlying the data, also called data surfing.

Business Intelligence (BI) is computer techniques used in spotting, digging out, and analyzing hard business data, such as sales revenues by products or departments or associated costs and incomes. Objectives of BI exercise include

(1) Understanding of a firms internal and external strengths and weaknesses,(2) Understanding the relationship between different data for better decision making.(3) Detection of opportunities for innovation. And(4) Cost reduction and optional deployment of resources.

In data analysis, the data which is collected is arranged according to some pattern or a particular format and this analyzation of the data is mainly done to provide the data with a meaning.

In the beginning the data is raw in nature but after it is arranged in a certain format or a meaningful order this raw data takes the form of the information. The most critical and essential supporting pillars of the research are the analysation and the interpretation of the data.

Both these aspects of the research methodology are very sensitive in nature and hence it is required that both these concepts are conducted by the researcher himself or under his very careful and planned supervision. With the help of the interpretation step one is able to achieve a conclusion from the set of the gathered data.

Analysis of the data can be best explained as computing some of the measures supported by the search for relationship patterns, existing among the group of the data.

Research depends a great deal on the collected data but it should be seen that this collected data is not just a collection of the data but should also provide good information to the researcher during the various research operations. Hence to make data good and meaningful in nature and working, data analysis plays a very vital and conclusive role. In this step data is made meaningful with the help of certain statistical tools which ultimately make data self explanatory in nature.

According to Willinson and Bhandarkar, analysis of data ‘involves a large number of operations that are very closely related to each other and these operations are carried out with the aim of summarizing the data that has been collected and then organizing this summarized data in a way that helps in getting the answers to the various questions or may suggest hypothesis.

Purpose of data analysis:

The purpose of the scientific analysis was first explained by Leon Festinger and Daniel Katz and according to both of them; the purpose of the analysis of the data can be explained as follows –

1. Should be very productive in nature, with high significance for some systematic theory.

2. Should be readily disposed to the quantitative treatment.

Procedure for the Analysis of the dataData collected can be used in the best possible effective manner by performing the following activities-1. Carefully reviewing all the data collection.2. Analyzing the data then with the help of certain suitable techniques.3. Result obtained from the analyzation of the data should be then related to the

study’s hypothesis.

Analyzation Steps:The various steps of the analyzation of the data were given by Herbert Hyman and can be summarized as follows –

1. Tabulation of the data after conceptualization, relating to every concept of the procedure is done which ultimately provides an explanation based on the quantitative basis.

2. Tabulation in the same way is carried out for every sub group, which gives quantitative description.

3. To get statistical descriptions consolidating data for different aspects is brought into use.

4. Examination of such data is then done, which helps in improving the evaluation of the findings.

5. Different qualitative and non statistical methods are brought into the use for obtaining quantitative description but only if it is needed.

Types of Analysis:

1. Descriptive analysis:• Also referred to as the one dimensional analysis. • Mainly involves the study of the distribution of the variable.• Depicts the benchmark data.• Helps in the measurement of the condition at the particular time.• Acts as the prelude to the bivariate and multivariate analysis.• Such an analysis may be based on the one variable, two variables or more

than two variables.• Helps in getting the profiles of the various companies, persons, work

groups, etc.2. Causal analysis:

• Also referred to as the Regression Analysis.• Has their root in the study of how one or more variables affect the changes

in the other variable.• Explains the functional relationship between two or more variables.• Helps in experimental research work.• Explains the affect of one variable on the other.• Involve the use of the statistical tools.

3. Co – Relative Analysis:• Involves two or more variables.• Helps in knowing correlation between these two or more variables.• Offers better control and understanding of the relationships between the

variables.4. Inferential Analysis:

Involves tests of significance for the testing of the hypothesis. Helps in the estimation of the population values. Helps in the determination of the validity data which can further lead to draw

some conclusion. Takes an active part in the interpretation of the data.

Chapter 3

Our Analyzed Figures and Results

The data I selected for our analyzation project is downloaded from the United Kingdom government website that is given below:

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/276084/prison-population-tables-q3-2013.xls

I analyzed the data in different aspects and the results I concluded with attached herewith

3.1.

Figures and frequencies of Criminals in Different parts of the Globe in England and Wales.

Criminals from subcontinents of Asia

India

Pakistan

Combodia

Bangaladesh

China

Vietnam

0 200 400 600 800 1000 1200

criminals from Asiacriminals from Asia totalcriminals from Asia male

IndiaPakistan

Cambodia

Bangladesh China

Vietnam

total 426 548 0 276 130 247

male 416 538 0 273 116 219

female 10 10 0 3 14 28

In this graph I only showed highest criminals and lowest criminals from the countries of a continent with figures and plot.

Criminals from African Countries:

Nigeria Somalia Zimbabwe Algeria Namibia Egypt

469 430

174 1351 22

414 425

160135

1 22

555

140

0 0

Chart Titlecriminals from Africa criminals from Africa total criminals from Africa totalcriminals from Africa male criminals from Africa male criminals from Africa female

criminals from Africa Nigeria Somalia

Zimbabwe Algeria

Namibia Egypt

total 469 430 174 135 1 22

male 414 425 160 135 1 22

female 55 5 14 0 0 0

Criminals From the European countries

from Europe Poland

Romania Ireland

Lithuania Albania

Kyrgyzstan

total 938 547 779 502 275 0

male 907 493 741 485 272 0

female 31 54 38 17 3 0

Poland

Romania

Ireland

Lithuania

Albania

kyrgystan

938

547

779

502

275

0

907

493

741

485

272

0

31

54

38

17

3

0

criminals from Africa from Europe criminals from Africa total criminals from Africa male

3.2.

Comparison of Criminals from different Continents:

Asia Africa Europe South America

North America

Middle East Oceania West Indies0

100020003000400050006000700080009000

10000

2012 2312

4702

179 68 481 22910

19432190

4439

158 58474

21

847

78122

263

21 107

1

63

Prisoners by continent Prisoners by continent total Prisoners by continent totalPrisoners by continent male Prisoners by continent male Prisoners by continent female

Prisoners by continent Asia Africa Europe

South America

North America

Middle East Oceania

West Indies

total 2012 2312 4702 179 68 481 22 910

male 1943 2190 4439 158 58 474 21 847

female 78 122 263 21 10 7 1 63

3.3.

Prisoners by nationality In England and Wales

total

male

female

84163

80356

3807

72561

69376

3185

10695

10130

565

907

850

57

prisoners by NationalityNot recorded Foreign British All

prisoners by Nationality All British Foreign

Not recorded

total 84163 72561 10695 907

male 80356 69376 10130 850

female 3807 3185 565 57

3.4.

Frequency of Male prisoners by time period

30 September2012; 86457

31 December 2012; 83757

31 March 2013; 83769

30 June2013; 83842

30 September2013; 84488

31 December 2013; 84163

quarter

30 Septembe

r2012  

31 Decembe

r 2012  

31 March

2013  

30 June2013  

30 Septembe

r2013  

prisoner 86,457 83,757 83,769 83,842 84,488

Our Analysis was carried for the time period from 30th September 2012 to 30th December 2013 differentiated by four months quarter.

Action taken against Criminals

30 September2012

31 December 2012

31 March 2013

30 June2013

30 September2013

31 December 20130

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

86457 83757 83769 83842 84488 84163

11749 10661 10768 10971 11429 11256

73406 71855 71638 70913 71113 70919

prisoner Remanded sentenced

30 Septemb

er2012  

31 Decemb

er 2012  

31 March

2013  

30 June2013  

30 Septemb

er2013  

prisoner 86,457 83,757 83,769 83,842 84,488

Remanded 11,749 10,661 10,768 10,971 11,429

sentenced 73,406 71,855 71,638 70,913 71,113

The percentage change of remand from 30th Sept. 2012 to 31st Dec. 2103 is 0%

The percentage change of sentenced from 30th Sept. 2012 to 31st Dec. 2103 is -1%

3.5 Male Population in prison by different age groups:

30 Septem

ber

201231 Dece

mber

2012 31 Marc

h

2013 30 June

2013

30 Septem

ber

2013

31 December

201366,00068,00070,00072,00074,00076,00078,00080,00082,00084,000

74240 72414 72648 73047 73663 73869

69376447 6361 6084 6044 5727

1157976 891 858 848 760

>21 yrs Series2 18-20 yrs Series4 15-17 yrs Series6

3.6.

Male Prisoners by type of custody:

30 Sept.2012

31 Dec.2012

31 March 2013

30 June2013

30 Sept.2013

31 Dec. 2013

0 10000 20000 30000 40000 50000 60000 70000 80000 90000

82687

80158

80237

80326

80902

80707

82334

79837

79900

79989

80555

80356

122

120

104

104

110

109

231

201

233

233

237

242

types of custody

training centre children home children home prisonprison total total Series1

3.7. Frequency of Male prisoners by time period

30 Sept.2012 31 Dec.

2012 31 March 2013

30 June2013 30 Sept.

2013 31 Dec. 2013

3300

3400

3500

3600

3700

3800

3900

4000

4100

4200

3818

36713658

3657 3743

3619

282

236202

188189

188

23

139

81

0

Series1 >21 yrs 18-10 yrs 15-17 yrs

Types of custody for female prisoners

30 Sept.2012

31 Dec.2012

31 March 2013

30 June2013

30 Sept.2013

31 Dec. 2013

0500

10001500

20002500

30003500

40004500

4177

3965

3915

3899

3994

3870

4123

3920

3869

3853

3933

3807

26

22

16

16

23

29

28

23

30

30

38

34

training centre children home children home prisonprison total total Series1

Female prisoners by age group:

30 Sept.2012

31 Dec.

201231

March 2013

30 June2013

30 Sept.2013

31 Dec.

2013

3300

3400

3500

3600

3700

3800

3900

4000

4100

4200

3818

36713658 3657 3743

3619

282

236202

188189

188

23

139

81

0

Series1 >21 yrs >21 yrs 18-10 yrs 18-10 yrs 15-17 yrs

Significantly there is a -20% reduction in the age group18-20 years

And the number of criminal females of age group 15-17 years has reduced from 23 to 0 from September 2012 to December 2013.

3.8 Male prisoners by type of violence:

Violence against the person

Sexual offences

Robbery

Burglary

Theft and Handling

Fraud and Forgery

Drug offences

Motoring offences

0 5000 10000 15000 20000 25000

22,009

11,488

10,095

8,620

5,151

1,388

11,512

830

21,244

11,534

9,430

8,173

4,798

1,371

11,296

746

31 Dec. 2013 Series2 30 Sept.2012

Highest number of offences is violence against person, sexual offences, drug and robbery.

female prisoners by type of violence:

Violence

against

the pers

on

Sexual o

ffences

Robbery

Burglary

Theft

and Han

dling

Fraud an

d Forge

ry

Drug offen

ces

Motoring o

ffences

0

500

1000

1500

2000

2500

1,115

97416 287

586

220

616

22

1,068

114

350

241

560

170

540

19

The female criminals are highly affected by theft and handling and it has increased from 2012 to 2013 with 20%, sexual offence increased with 13%, robbery increased with 14%, and violence against person has increased with12%, and drug offence decreased with 10%.

3.9.

Prisoners by different sentence tariff length (life sentence)

prisoners

01000

20003000

40005000

60007000

8000

2062 4003115744197 7463

TotalTariff length not available (2)Whole lifeGreater than 20 yearsGreater than 10 years to less than or equal to 20 yearsLess than or equal to 10 years

Prisoners by different sentence tariff length (Not life sentence)

Less than 2 years

2 years to less than or equal to 4 years

Greater than 4 years to less than or equal to 6 years

Greater than 6 years to less than or equal to 10 years

Greater than 10 years

Tariff length not available (2)

Total

0 2000 4000 6000

982

2,405

1,181

659

90

18

5,335

Series1Series2Series3Series4Series5total

3.10.

Male prisoners by different age groups:

all 15-17 18-20 21-24 25-29 30-39 40-49 50-59 60 and

over

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

82,334

1,157 6,937 13,36615,08021,99314,305 6,244 3,252

80,356

7605,727

12,01415,14022,414

14,2056,659 3,437

31 Dec.201330 Sept.201230 Sept.2012

Female prisoners by different age group

all

15-17

18-20

21-24

25-29

30-39

40-49

50-59

60 and over

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

4,123

23

282

516

700

1,317

868

336

81

3,807

0

188

420

649

1,293

798

360

99

30 Sept.201230 Sept.201231 Dec.2013

Chapter 4

LEARNNNIG OUTCOMES

After doing my training at the CTS (IN –HOUSE TRAINING) I felt the importance of

training in an institute and its practical applications, when I was studying the theories of different concepts I was thinking how these all will be implemented. But after the training, I learnt how all these could be put in good use. It was the result of training only that I got to see the objects in real and practical use, which I had only read about and seen as 2D objects in the book.

I have learnt many other practical application of software and uses of

Micro soft Excel SAS 9.2 Micro Soft SQL server Oracle Database Cloud Computing Data Base management system. Prediction of Data Making Of dashboard Name manager Oracle server Creation, deletion, updating the rows and columns in data in tables OLAP, OLTP Etc.

Chapter 5

CONCLUSION

After doing this project work, I have concluded with some results, those are enlisted below

a) Males are more violence prone and comprises highest number of criminals and prisoners

b) Females are comparatively less criminals than Males.

c) The age groups of 30-40 years of males are highest prisoners.

d) The age groups of 30-40 years females are highest criminals.

e) Violence against a person is done highest by both Males and Females

f) Drug offence and sexual offence are second highest violence created by males.

g) Theft and drug offence are the second highest offence done by females.

h) After Europe, Africa and Asia comprises highest number of prisoners in England and Wales.

i) There are several results concluded those discussed above along with the graphs.

References

i. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/ 276084/prison-population-tables-q3-2013.xls

ii. https://www.nikeshbajaj.in/stats/iii. http://en.wikipedia.org/wiki/database_management_system iv. www.businessdictionary.com v. https://www.mbaofficial.com

vi. www.sideshare.net/

THANK YOU