Trained, competent professionals in Analytics, Research, and Management are very much in demand. Analytics has become a major driving force and is closely aligned with the core competency for most of the industries today. Its impact on management has created a demand for an entirely new array of leaders. These emerging knowledge leaders must be able to effectively and efficiently guide their organizations into the future, uninhibited by continuing technical advancements and the increasing reliance on analytics and research to remain competitive in business.

Jointly designed by the Shailesh J Mehta School Of Management at IIT Bombay, one of the country’s finest and foremost business school, and HughesNet Global Education, Indian’s premier corporate management development institution, the program is spread over one year and covers both the theoretical and practical aspects of the discipline. Industry experts also contribute to the course by giving lectures and presenting real-life consultancy projects. Completing the program will ensure that the participant demonstrates to their potential or current employer that they understand and appreciate the various approaches of analytics to deliver real benefits.

Part 1: Introduction to Analytics - 22 sessions

• IOL: 17 sessions (51 hours)
• Assessments: 5 sessions
• Campus: 3 days

Introduction to Statistics & Econometrics – 4 sessions (12) + 1 session assessment

• Basic Probability Theory
• Probability Theory
• Conditional Probability And Bayes’ Theorem
• Random Variables
• Density And Distribution Function
• Joint, Marginal And Conditional Distribution
• Moment Generating Function
• Law Of Large Numbers And Central Limit Theorem
• Theory Of Estimation And Statistical Inference
• Point And Interval Estimation
• Maximum Likelihood And Method Of Moment Estimation
• Hypothesis Testing
• Types And Size Of Error
• Power Of A Test

Regression – 3 sessions (9) + 1 session assessment

• Regression Analysis
• Econometric Models
• Simple Linear Regression Model
• General And Confidence Approach To Hypothesis Testing

• Logistic Regression
• What to look for in Logistic Regression
• Comparison of Discriminant and Logistic Regression
  • Extending The Regression Analysis
  • Goodness Of Fit
  • Model Evaluation
  • Relaxation Of Assumptions In Regression Model
  • Consequences And Remedial Measures
  Time Series Analysis – 4 sessions (12) + 1 session assessment
  • Introduction to Time Series Analysis
  • Autocorrelation and partial autocorrelation
  • Conditions for stationary and invertible process
  • Box-Jenkins approach
  • Models for Stationary Time Series
  • Auto Regressive Processes
  • Moving average models
  • ARMA Models
  • Modeling & Forecasting with ARMA Models
  Panel Data Econometrics – 2 sessions (6) + 1 session assessment
  • Static Panel Data Models
  • Fixed effects, Random effects, Random coefficients
  • Dynamic Panel Data Models
  • Models with lagged dependent variable, Estimation,
  • Simultaneous equation panel data models,
  • Generalised method of moment estimation
  Segmentation Methods– 4 sessions (12) + 1 session assessment
  • Cluster Analysis
  • Factor Analysis
  • Discriminant Methods
  • What is Response-Based Segmentation?
  • Comparison of Discriminant and Logistic Regression
  • Recommendations
  • What to look for in Discriminant Analysis
  • Stepwise Results
  • Gains Tables and Charts
  • CHAID
  • What is CHAID?
  • What to look for When Running CHAID?
  • Principles and Considerations
  • Exhaustive CHAID
  • Bonferroni Adjustments
  • Variable Types
  • Recommendations
  Computational Languages and Techniques – 2 days at Campus
  
  PART2: Advanced Analytics – 18 sessions
  • IOL: 14 sessions (42 hours)
  • Assessments: 4 sessions
  • Campus: -
  
  Artificial Neural Networks – 4 sessions (12) + 1 session assessment
  • Architecture - Introduction to neural networks & their history, Biological neurons and neural networks; Artificial neurons, Networks of artificial neurons.
  • Data Processing - Ordinal models, Multinomial models, and Nested models
  • Back Propagation - Back propagation, Learning in multi-layer perceptrons, Learning with momentum, Conjugate gradient learning.
  • Performance Management - Bias and variance, under-fitting and over-fitting, Improving generalization
  
  Advances in Time-Series – 4 sessions (12) + 1 session assessment
  • Non stationary Time Series
  • Auto Regressive Integrated Moving Average Process
  • Random Walk
  • Unit Root, Testing For Unit Root
  • Co-integration
  • Error Correction Model
  • Forecasting
  • Models of Volatility
  • Volatility Clustering
  • Leverage Effect
  • ARCH Model
  • GARCH Model & Its Various Extension
  • Multivariate GARCH Modeling
  • Forecasting
  
  Advances in Applied Econometrics – 4 sessions (12) + 1 session assessment
  • Discrete Choice Models
  • Introduction to binary variables, Probit and Logit Models, Censored versus truncation
  • Models with More than Two Choices Ordinal models, Multinomial models, and Nested models
  • Count Data Models and Financial Data
  • Poisson distribution, Binomial and negative binomial distribution
  • Applications: Number of takeover bids, credit history of individual card applicants, state of holding a mortgage, default probability calculation
  • Markov Models
  
  Advanced Techniques – Analysis, Boosting, Bagging, Bootstrapping – 2 sessions (6) + 1 session assessment
  • Introduction - Bootstrap, Estimation of standard error, Parametric bootstraps, Number of bootstrap replications
  • Application - Application of bootstrap, bootstrap pairs, bootstrap residuals, examples, confidence intervals
  • based on bootstrap

The program has been designed for those who are new to business analysis and who work with business analysts, including application developers, systems analysts, business managers, and anyone involved in solution development. For analysts who need to develop the skills and competencies and desire an in-depth understanding and grasp of key techniques such as behavior and data modeling, this program takes the practitioners to the next level.