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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Delhi |
The Syllabus
Module 1: Governance of Enterprises Analytics Systems
- Introduction to management of systems in enterprises and industries
- Types and Levels of Analytics Systems
- Understanding the evolution of the SMAC era for enterprise systems
- Challenges of managing Artificial Intelligence and Machine Learning Projects
- Technology Alignment and Governance issues in Digital Transformation using Machine Learning.
Module 2: inferential Analytics
- Statistics 101 and Descriptive Analytics using MS Excel and SPSS/PSPP
- Data Visualisation
- Python programming for data management
- Python for descriptive, diagnostic, and inferential statistics
- Prescriptive Analytics using MCDM/AHP
Module 3: Predictive Analytics and Machine Learning
- Data Mining approaches for predictive analytics
- Supervised and Unsupervised learning
- Regression and Multivariate analysis using SPSS/PSPP
- Data Multidimensionality
- Data model building for Big Data applications
- Machine Learning using Artificial Neural Networks
- Deep learning
- Fuzzy set theory
- Machine learning using KNN, Kmeans, Random Forest, Support Vector Machine, etc.
Modile 4: Cognitive Science and Big Data Analytics
- Big Data Applications
- Understanding Natural Language Processing Applications (e.g. Search Engines and Social Media)
- Web Analytics (Google)
- Machine Learning Applications and Chatbots
- Social Media Analytics
- Advanced Text Mining like sentiment analysis, topic modelling, and text summarisation
- Advanced Network Science and Applications
Module 5: Tools for Data Science
- Hands-on exercises with Machine Learning for Supervised and Unsupervised learning
- User Interface drove Python applications (Orange)
- Python programming for big data and machine learning applications
- Text mining using Orange
Module 1: Overview on Analytics for business decisions
- Understanding of main pillars of business decision analytics
- Introduction to Heuristics/Meta-Heuristics/Hyper-Heuristics/AI
- Application of decision-making models
Module 2: Prescriptive Analytics
- Understanding Quantitative Data Analysis and Prescriptive Analytics
- Linear Programming (Single Objective) using Excel/LINGO
- Non-Linear Programming (Single Objective) using Excel / LINGO
- Linear Programming (Multiple Conflicting Objectives)
- Goal Programming using Excel / LINGO
- Applications of Linear Programming/Non-Linear Programming in business
- Predictive Analytics using EXCEL/R
Module 3: Business Simulation
- Introduction to basic statistics such as population and sample
- The measure of central tendency, dispersion, and association
- Simulation modelling and analysis using Excel
- Application of simulation in business decisions
- Demand forecasting in business decisions
- R for predictive analytics (demand forecasting)
- Applying AI (Genetic Algorithm) in business decisions using Excel
Module 4: Descriptive and Qualitative Data Analysis
- Understanding Qualitative Data Analysis and Descriptive Analytics
- Introduction to Multi-Criteria Decision Making
- Group Decision Making
- ISM, MICMAC Analysis, IRP, DEMATEL, TOPSIS, ELECTRE
- Hybridisation of MCDM such as IRP-AHP, ISM-AHP, AHP-TOPSIS
- Qualitative data analysis from Most Likely, Pessimistic, and Optimistic Algorithms
- Aggregation of ranking variations using MILP in Excel/LINGO
Module 5: Decision Science tools and Case Studies
- Case study discussions from several domains of businesses viz. marketing, production, human resource, finance, & strategy using Excel/LINGO.
Instructors
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