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Quick Facts

Medium Of InstructionsMode Of LearningMode Of DeliveryFrequency Of Classes
EnglishSelf Study, Virtual ClassroomVideo and Text BasedWeekends

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesIIT 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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