Computer Aided Decision Systems - Industrial practices using Big Analytics

Overview

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.

Syllabus

Week 1: Introduction to Systems
System Analysis and Design
Decision Support Systems (DSS)
Design of Decision Support SystemsWeek 2:Rational Decisions using DSS
Introduction to Relational Database
Relating Multiple Databases
Case Study on DSS
Assignment: Practice on DSS DatabasesWeek 3:Basics of Data Modelling
Models for DSS
Selecting a Right Model
Assignment: Practice on Model Selection
Developing Models for DSS applicationsWeek 4:Introduction to Big Data
General Applications and Uses
Big Data Analytics (BDA)
Assignment: Additional reading material
Case Study on Retail IndustryWeek 5:Credit Modeling
Web Analytics
BDA in Engineering and Manufacturing
Assignment: Additional reading material
Enhancing Quality and Cost ControlWeek 6:Improving Forecast Accuracy
Anticipating Demand Changes
Inventory Management
Pricing, Market Basket AnalysisWeek 7:Cost Management
Medical Monitors, Targeted Drug Delivery
US BRAIN Initiative
Alzheimer’s and Parkinson’s models
Assignment: Case study on Healthcare BDAWeek 8:Population Health Strategies
BDA in transportation
ATC Management
Flat Tracking, Tyre & Fuel Usage
Assignment: Demonstration using soft toolsWeek 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 IndustryWeek 10:Introduction to Simulation
Discrete Event Simulation
Simulation for Descriptive Analytics
Simulation for Prescriptive AnalyticsWeek 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 softwareWeek 12:BDA and Healthcare
Reducing reaction time to critical clinical events
Back Testing Analytical Models
Recapitulating the CADSS BDA concept