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Criminal Justice Data Analysis - 4


Description

Research is the core of modern professional internal security issues today. Criminal justice is increasingly a data-driven function and CJ organizations process substantial data that reflect the social, political, economic factors impacting Indian society. Vast amount of criminal justice data is being collected throughout India. The nation-wide Crime and Criminal Tracking Network and Systems (CCTNS), as well sixty years of Crime in India data, new sources such as ‘Dial 100’ system across the country are providing big data to practitioners, researchers and policy makers. Combined with additional social, political and economic data and geographic information systems that provide spatial maps and allow incorporation of temporal phase, significant new heights can be scaled for effective crime control with data analytics and visualization.

The major objective of this course will be to train security personnel in analytic tools for study of crime and its control in India. Big Data analytics is among today’s fastest-growing professions and this course seeks to build skills in applying its techniques for internal security of the country. The course will incorporate subject matter from the disciplines of criminology, computing science, mathematics, geography, economics, psychology, management, philosophy, and ethics, with special emphasis upon gender, race and ethnic studies. Topics will cover Big Data Analytics comprising machine learning and AI applications as well as qualitative methods. Students will study ways of understanding and modeling of the complex social & political environment, and with these models better understand how to improve approaches to crime reduction and the use of analytics in criminal justice issue. 

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For more details, please visit the CCJR website by clicking here.

Student feedback for the course CJDA-1

Instructors

Dr. Arvind Verma
(arvindcjus@cse.iitk.ac.in)

Dr. Hanif Qureshi 

Dr. Nisheeth Srivastava 

Dr. Ramyaa

Dr. Shankar Prawesh

Dr. S K Lodha        

Operational Assistants

Dr. Neeta Singh
Deepak Kumar
Abhishek Shukla
Akash Misra
Revathy K T

Course Content

Module 1 Weeks 1-2: Criminological Perspectives    Feb 7-20
Class Meetings:           Tuesday, Thursday & Friday at 9.00 pm IST on Zoom
                                       We will have guest lectures during these weeks
Assignments weeks 1-2 Due by Feb 20

Module 2 Weeks 3-4: Machine Learning        Feb 21-Mar 06
Class Meetings:           Monday, Wednesday & Friday at 5.00 pm IST on Zoom
                                       NO guest lectures during these two weeks
Assignments weeks 3-4 Due by Mar 06

Note :
All ML readings/ Jupiter Notebooks/ Data Exercises are available from Feb 20 onwards
The class held by instructor Dr. Nisheeth is largely to help understand these contents. It is expected that everyone will do the readings/ exercises etc and ask questions about them during the one-hour session. Please feel free to ask any question about the concepts/ exercises and also the assignments.

Module 3 Weeks 5-6: Qualitative Analysis and Historiographic    Mar 07-20
Class Meetings:            Tuesday, Thursday & Friday at 9.00 pm IST on Zoom
                                       We will have guest lectures during these weeks
Assignments weeks 5-6 Due by Mar 20

Module 4 Weeks 7-8:    Network Analysis    Mar 21-Apr 03
Class Meetings:              Tuesday & Thursday at 8.30 pm IST on Zoom
                                          We will have guest lectures during these weeks
Assignments weeks 7-8 Due by Apr 03

Note :
All Network Analysis readings/ Directions to download and use Gephi/ Data Exercises are available from Mar 20 onwards
The class held by instructor Dr. Shankar is largely to help understand these contents. It is expected that everyone will do the readings/ exercises etc. and ask questions about them during the one-hour session. Please feel free to ask any question about the concepts/ exercises and also the assignments.

Module 5 Weeks 9:     Data Visualization    Apr 04-10
Class Meetings:            Tuesday, Thursday & Friday at 9.00 pm IST on Zoom
                                        We will have guest lectures during these weeks
Assignments will be part of class work

Module 6 Weeks 10:     Project Presentations    Apr 11-Apr 16
Class Meetings:             All weekdays at 9.00 pm IST on Zoom
                                        We may have guest lectures on these days too

Audience

The course is designed for officers of Indian police, private security managers, senior officers of defense services, internal security policy analyst, CJ practitioners, and researchers. In particular, this course is designed for those seeking to enhance their skills and capabilities in crime prevention techniques. Graduate students of political science, sociology, criminology, computer science, mathematics, and management will also benefit from this course.

Educational Requirements

The minimum educational requirement is a bachelor’s degree and experience of working with computers. In this course, you’ll learn quantitative and qualitative methods for data analysis through hands-on exercises and video instruction from IIT Kanpur faculty and guest faculty from other institutions around the globe.

Time Frame

You can complete all course requirements in this course and earn your certificate in 12 weeks, spending 5-7 hours per week.

Evaluation

Weekly Assignments/ Exercises/ Quizzes    90%
 Presentation                                                 10%

Outcomes

  • Theories of crime that suggest why people commit crime
  • Criminal Justice Data Sources
  • Criminal Justice Data Visualization
  • Big data analytics, simulation, machine learning, network analysis, survey design in criminal justice issues, Data Security
  • Formulate crime control questions that can be tested using various methods.
  • Use CJ data to test policy outcome

About Certificates

After completion of all the assignments and successful presentation, the students will be awarded a Certificate in Criminal Justice Data Analysis by IIT Kanpur.