Safety and Risk Analytics Course | IIT Kharagpur | Prof. Jhareswar Maiti
Course Details
| Exam Registration | 1035 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Management Studies |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 25 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Transform Workplace Safety with Data: An In-Depth Guide to the Safety and Risk Analytics Course
In today's data-driven industrial landscape, proactive safety management is no longer a luxury—it's a necessity. Reactive approaches are being replaced by predictive and prescriptive models that can foresee and prevent incidents before they occur. At the forefront of this revolution is the comprehensive course on Safety and Risk Analytics, offered by one of India's premier institutions, IIT Kharagpur.
This 12-week program is meticulously designed to bridge the gap between traditional safety engineering and modern data science, empowering professionals to build safer, more resilient workplaces.
Meet Your Instructor: A Pioneer in Safety Science
The course is led by Prof. Jhareswar Maiti, a distinguished professor in the Department of Industrial & Systems Engineering at IIT Kharagpur. With over 15 years of dedicated teaching, research, and industry consulting, Prof. Maiti is a stalwart in the fields of Safety Analytics, Quality Analytics, and Engineering Ergonomics.
His impressive credentials include:
- Supervision of 11 completed PhDs and 8 ongoing research candidates.
- Publication of 70+ papers in reputed international and national journals.
- Execution of numerous industry-sponsored and government-funded research projects, including a major UAY project funded by MHRD, Ministry of Steel, and Tata Steel Limited.
- Extensive experience organizing 17 training programs for industry professionals.
- Editorial roles for top-tier journals like Safety Science and Safety and Health at Work.
Prof. Maiti's expertise ensures that the course content is both academically rigorous and practically relevant, grounded in real-world applications.
Who Should Enroll in This Course?
This course is crafted for a diverse audience seeking to harness data for safety excellence:
- Students: UG and PG students in Engineering, Science, and Management.
- Working Professionals: Safety officers, risk managers, production managers, and data analysts in industrial settings.
- Industry Practitioners: Professionals in manufacturing, process industries, mining, construction, and R&D.
Pre-requisite: A basic understanding of probability and statistics is recommended to fully grasp the analytical concepts.
What Will You Learn? Course Objectives and Outcomes
The primary objective is to provide a holistic view of applying advanced analytics to safety and risk throughout a system's life cycle. Upon completion, you will be proficient in:
- Understanding the types, sources, and integration of safety data for organization-wide models.
- Visualizing and exploring safety data effectively.
- Evaluating and monitoring safety performance metrics.
- Building and interpreting predictive models for accidents and risks.
- Applying behavioral analytics and injury epidemiology principles.
- Making informed, data-driven decisions to enhance workplace safety.
The curriculum draws from engineering principles, statistics, machine learning, and data mining, focusing on the core mantra: learn from data, predict the future, and take data-driven decisions.
Weekly Course Layout: A 12-Week Journey to Expertise
| Week | Topic |
|---|---|
| Week 1 | Basics of safety and risk |
| Week 2 | Creation of safety database |
| Week 3 | Safety data quality assessment and preprocessing |
| Week 4 | Descriptive safety analytics |
| Week 5 | Safety performance evaluation and monitoring |
| Week 6 | Analysis of Safety Reports and Narratives |
| Week 7 | Risk quantification |
| Week 8 | Predictive safety analytics |
| Week 9 | Predictive risk analytics |
| Week 10 | Predictive risk analytics (cont.,) |
| Week 11 | Prescriptive safety analytics (cont.,) |
| Week 12 | Behavioral safety analytics and injury epidemiology |
Essential Reading and Reference Materials
The course is supported by a robust reading list from leading texts in risk assessment, statistical learning, and data mining:
- Probabilistic Risk Assessment and Management for Engineers and Scientists by H Kumamoto and E J Henley
- An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani
- Pattern Recognition and Machine Learning by Christopher M Bishop
- Introduction to Data Mining by Tan, Steinbach, & Kumar
- Text Mining: Predictive Methods for Analysing Unstructured Information by Weiss et al.
Industry Recognition and Support
The practical relevance of this course is underscored by the strong industry support it receives. The methodologies taught are directly applicable in major organizations, including:
- Manufacturing: GM, Tata Motors, Tata Steel
- Process Industries: ONGC
- Mining: Coal India Limited
- Construction: L&T
- Conglomerates & R&D: General Electric, DRDO
This endorsement highlights the course's value in addressing real-world safety challenges across sectors.
Why Choose This Course?
Choosing this course means learning from an institution and instructor of the highest caliber. Prof. Maiti's 42-lecture series on "Applied Multivariate Statistical Modeling" is already available on YouTube via NPTEL, a testament to his teaching excellence and commitment to accessible education.
In a world where safety is paramount, equipping yourself with the skills to analyze risk, predict incidents, and prescribe preventive actions is an invaluable investment. This course offers a unique blend of theoretical depth and practical application, making it an essential step for anyone serious about advancing safety through analytics.
Embrace the future of safety management. Enroll in the Safety and Risk Analytics course and become a catalyst for creating safer, smarter, and more sustainable work environments.
Enroll Now →