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Computer Aided Decision Systems - Industrial practices using Big Analytics

By Prof. Deepu Philip, Prof. Amandeep Singh   |   IIT Kanpur
Learners enrolled: 153
ABOUT THE COURSE:
Industry 4.0 has marked the use of Computer Aided Decision Support Systems largely using Big Data Analytics in developing interfaces between the soft and physical systems. With huge numbers of sensors, smartphones, vehicles and systems being connected, the data and information is being generated at an unprecedented pace. Big Data Science has become a prominent tool to conceptually connect and realize fruitful use of this data and information. This has created tremendous opportunities and ventures for the students and practitioners. This course covers the major sectors that utilize the Big Data Analytics vis-à-vis Retail Industry, Engineering and Manufacturing, Healthcare, and Transportation. The predominant tools in the above sectors and use of soft tools are designed to make the course useful for the practitioners. The candidates are expected t take a new leap on taking the Analytics assignments after taking this course.

INTENDED AUDIENCE: Students of all Engineering and Science disciplines.

PREREQUISITES: The student should have completed two semesters of UG Engineering or Science program.

INDUSTRY SUPPORT: TCS, Accenture, Tech Mahindra, Capgemini India Pvt Ltd., Genpact.
Summary
Course Status : Upcoming
Course Type : Elective
Duration : 12 weeks
Start Date : 23 Jan 2023
End Date : 14 Apr 2023
Exam Date : 29 Apr 2023 IST
Enrollment Ends : 30 Jan 2023
Category :
  • Multidisciplinary
Credit Points : 3
Level : Undergraduate/Postgraduate

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Course layout

Week 1: Introduction to Systems
               System Analysis and Design
               Decision Support Systems (DSS)
               Design of Decision Support Systems
Week 2: Rational Decisions using DSS
               Introduction to Relational Database
               Relating Multiple Databases
               Case Study on DSS
               Assignment: Practice on DSS Databases
Week 3: Basics of Data Modelling
               Models for DSS
               Selecting a Right Model
               Assignment: Practice on Model Selection
               Developing Models for DSS applications
Week 4: Introduction to Big Data
               General Applications and Uses
               Big Data Analytics (BDA)
               Assignment: Additional reading material
               Case Study on Retail Industry
Week 5: Credit Modeling
               Web Analytics
               BDA in Engineering and Manufacturing
               Assignment: Additional reading material
               Enhancing Quality and Cost Control
Week 6: Improving Forecast Accuracy
               Anticipating Demand Changes
               Inventory Management
               Pricing, Market Basket Analysis
Week 7: Cost Management
               Medical Monitors, Targeted Drug Delivery
               US BRAIN Initiative
               Alzheimer’s and Parkinson’s models
               Assignment: Case study on Healthcare BDA
Week 8: Population Health Strategies
               BDA in transportation
               ATC Management
               Flat Tracking, Tyre & Fuel Usage
               Assignment: Demonstration using soft tools
Week 9: Buying Power instead of engine (RR Model)
               Assignment: Case study on Transportation BDA models
               Complaint Redressal
               UAVs, Smart Vehicle Integration
               Big Data practices in Industry
Week 10: Introduction to Simulation
               Discrete Event Simulation
               Simulation for Descriptive Analytics
               Simulation for Prescriptive Analytics
Week 11: Product Innovation, and Benchmarking
               Real-Time Performance Monitoring (Mc Laren)
               Assignment: Case study on Manufacturing
               BDA and Industry 4.0
               Product Lifecycle Management, Managing Innovation
               Assignment: Demonstration on PLM software
Week 12: BDA and Healthcare
               Reducing reaction time to critical clinical events
               Back Testing Analytical Models
               Recapitulating the CADSS BDA concept

Books and references

1. Elmasri, R., Navathe, S.B., Elmasri, R. and Navathe, S.B., 2000. Fundamentals of Database Systems. Addison-Wesley Publishing.
2. Marr, Bernard, 2016. Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. John Wiley & Sons.
3. Baesens, Bart, 2014. Analytics in a big data world: The essential guide to data science and its applications. John Wiley & Sons.

Instructor bio

Prof. Deepu Philip

Deepu Philip is a faculty of Industrial & Management Engg. Department and Design Programme of IIT Kanpur. He works in the area of Production and Operations, Systems Simulation, Product Life Cycle Management, Unmanned Aerial Systems, and Systems Engineering. He holds bachelor degree in Industrial Engineering with his doctorate in Industrial & Management Engineering from MSU Bozeman. He has both academic and industrial experience with leading organizations of the world. He has experience in designing and implementing complex system of systems in different fields including defense, aviation, fertilizer, strategic chemical plants, transportation, banking, automation, health care, energy, and communication.


Prof. Amandeep Singh

IIT Kanpur
Dr. Amandeep Singh is working asResearch Scientist in the Mechanical Engineering Department, and Design Program, Indian Institute of Technology, Kanpur, India. He holds PhD degree from Indian Institute of Technology Kanpur, India, and a bachelor degree in Production Engineering. Dr. Singh has ten years of industrial and academic experience. His research interests are Sustainable Manufacturing Processes and Systems, Simulation of Manufacturing Systems, Product Design and Manufacturing, Applied Ergonomics and Engineering Metrology. He has traveled in countries like US, Canada, and Australia to present his research in various international conferences organized by reputed bodies like CIRP and IEOM. His research is also published in various international reputed journals.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 29 April 2023 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kanpur. It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team


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