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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom, Campus Based/Physical Classroom	Video and Text Based	Weekdays, Weekends

Course Overview

The course on Business Analytics & Intelligence by the Indian Institute of Management Bangalore offers students an opportunity to learn from the experts of the industry. During the Business Analytics & Intelligence certification syllabus, the candidates will cover the key concepts of Business analytics. The registered students will be provided with multiple case studies that will help them study the course in a better way. The students will be covering the chapters of Introduction to data science, Data summarization and Data visualization methods, Different types and scales of data (ratio, interval, nominal, and ordinal), Chebyshev's Inequality, Box Plot, Coefficient of determination, Confidence, Residual analysis, Significance tests for predictor variables and Prediction intervals. The certificate issued by IIM Bangalore will help applicants secure better career opportunities.

The Highlights

Online session and in-campus session available
Certificate issued by IIM Bangalore
Government recognized certificate

Important dates

Course Commencement Date

Start Date : 15 Jul, 2024

End Date : 05 Apr, 2025

Programme Offerings

assignments
Case Studies
online programme
Lectures
Recorded videos available
Group Projects

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIM Bangalore

The Business Analytics & Intelligence fee structure has been provided below-

The total course fee is Rs. 7,75,000 +GST
The confirmation fee that the students have to pay is Rs.1,00,00 +GST
The 1st installment that the students have to pay is Rs. 2,45,000/-
The 2nd and 3rd installment that needs to be paid by students is Rs. 2,15,000 +GST

Particulars	Amount in INR
Total fee	Rs. 7,75,000/- + GST
Confirmation fee	Rs. 1,00,000/- + GST
1st installment	Rs. 2,45,000/- + GST
2nd installment	Rs. 2,15,000/- + GST
3rd installment	Rs. 2,15,000/- + GST

Eligibility Criteria

Work Experience

Three years of working experience is required to apply in the course.

Education

The students can apply for the Business Analytics & Intelligence online course if they have studied one of the subjects of Commerce, Engineering, Science, Mathematics, or Bachelor of Arts degree.

Certification Qualification Details

Students will receive the Business Analytics & Intelligence certification after successfully completing the programme as well as meeting all the requirements of the programme. On completion of the course, the candidates will also be a part of the IIMB EEP Alumni Association.

What you will learn

Statistical skills

After completion of the Business Analytics & Intelligence certification syllabus, students will learn the following concepts:

Students will be learning about the application of the Bayes theorem
The various regression model-building framework will be covered in the course curriculum
Candidates will study conditional probability
The basic concepts of probability will also be taught to the students
Applicants will learn about Simple linear regression

Who it is for

The Business Analytics & Intelligence certification course will help the following professionals

Candidates who are currently working as senior professionals will highly benefit from the course

Admission Details

To apply to the Business Analytics & Intelligence online course, students have to follow the listed steps:

Step 1: In the first place, students have to click the URL to generate the application form, https://www.iimb.ac.in/eep/product/259/Business-Analytics-Intelligence-BAI.

Step 2: The applicants then have to provide all the necessary details that have been listed.

Step 3: The candidates who already have an existing account can log in back to the account.

Step 4: The applicants have to download the Proforma invoice.

Step 5: Students then have to pay the course fee.

Step 6: The candidates need to download the original invoice.

Before submitting the application form, candidates have to give their consent to the course terms and conditions.

Application Details

The students who are filling the Business Analytics & Intelligence certification course form will have to provide the given details:

Full Name
Designation
City
Full Address
Organization Name
Contact Number

Note: In case the students have an existing account in LinkedIn, then they can auto-fill the application form of Business Analytics & Intelligence training.

The Syllabus

Foundations of Data Science Module Contents

Hypothesis testing: Constructing a hypothesis test; Null and alternative hypotheses; Test Statistic; Type I and Type II Error; Z test, t-test, two-sample t-tests; Level of significance, Power of a test, ANOVA
Data visualization and storytelling with data
Introduction to data science; Different types and scales of data (ratio, interval, nominal and ordinal); Data summarization and visualization methods; Tables, Graphs, Charts, Histograms, Frequency distributions, Relative frequency measures of central tendency and dispersion; Box Plot; Chebyshev's Inequality
Sampling and estimation: Estimation problems, Point and interval estimates, Confidence Intervals
Basic probability concepts, Conditional probability, Bayes Theorem, Probability distributions, Continuous and discrete distributions, Binomial Distribution, Uniform Distribution, Exponential Distribution, Normal distribution, Central Limit Theorem, Sequential decision making, Decision tree
Introduction to R and Python
Test for the goodness of fit, Non-parametric tests

Case Studies

A Dean’s Dilemma: Selection of Students for the MBA program to Admit or Not to Admit (IIMB Case)
Central Parking Solutions Private Limited (IIMB Case)

Data quality check, data cleaning, and imputation
K Nearest Neighbours (KNN) algorithm for data imputation

Case Study

Analytics in HR— Predicting Job Acceptance (IIMB Case)

Predictive Analytics Module Contents

Simple linear regression: Coefficient of determination, Significance tests for predictor variables, Residual analysis, Confidence, and Prediction intervals
Regression model building framework: Problem definition, Data pre-processing; model building; Diagnostics and Validation
Application of predictive analytics in retail, direct marketing, health care, financial services, insurance, supply chain, etc.
Logistic and Multinomial Regression: Logistic function, Estimation of probability using logistic regression, Deviance, Wald Test, Hosmer Lemeshow Test, Classification table, Gini coefficient; Multinomial logistic regression
Multiple linear regression: Coefficient of multiple coefficients of determination, Interpretation of regression coefficients, Categorical variables, heteroscedasticity, Multicollinearity, outliers, Autoregression and Transformation of variables, Regression Model Building
Forecasting: Moving average, Exponential smoothing, Causal models

Case Studies

Pedigree vs Grit: Predicting Mutual Fund Manager Performance (Kellogg Case)
Package Pricing at Mission Hospital (IIMB Case)
Pricing of players in the Indian Premier League (IIMB Case)
Colonial Broadcasting Company (HBS Case)
A Game of Two Halves: In-Play Betting in Football (IIMB Case)
Breaking Barriers – Micro-Mortgage Analytics (IIMB Case)
Predicting Demand for Food at Apollo Hospital (IIMB Case)
HR Analytics – Predicting Probability of Renege (IIMB Case)
Predicting Earnings Manipulations by Indian firms using machine learning algorithms (IIMB Case)

Optimization Analytics Module Contents

Introduction to Operations Research (OR), linear programming (LP), formulating decision problems using linear programming, interpreting the results, and sensitivity analysis. Concepts of shadow price and reduced cost
Integer Programming (IP) problems, mixed-integer and zero-one programming. Applications of IP in capital budgeting, location decisions, contracts
Multi-period LP models. Applications of linear programming in product mix, blending, cutting stock, transportation, transhipment, assignment, scheduling, planning, and revenue management problems. Network models and project planning
Nonlinear programming, portfolio theory, gradient descent algorithm technique
Multi-criteria decision making (MCDM) techniques: Goal Programming (GP) and analytic hierarchy process (AHP) and applications of GP and AHP in solving problems with multiple objectives

Case Studies

Merton Truck Company (HBS Case)
Red Brand Canners (Stanford Case)
Supply Chain Optimization at Madurai Aavin Milk Dairy (IIMB Case)
Case on Airline Operations (IIMB Case)
Managing Linen at Apollo Hospitals (IIMB Case)

Stochastic Models Module Contents

Reinforcement Learning Algorithms: Dynamic Programming; Markov decision process, Applications of Markov decision process in sequential decision making
Poisson process, Cumulative Poisson process, Applications of Poisson, and cumulative Poisson in operations, marketing, and insurance. Measuring the effectiveness of retail promotions, warranty analytics
Introduction to stochastic models, Markov models, Classification of states, Steady-state probability estimation, Brand switching, and loyalty modelling, Market share estimation in the short and long run. Google’s ranking algorithm
Monte Carlo simulation

Case Studies

Customer Analytics at Flipkart (IIMB Case)
Consumer Choices between House Brands and National Brands in Detergent Purchase at Reliance Retail (IIMB Case)
Browser Wars: Microsoft Vs Netscape (Darden Case)
MNB ONE Credit Card Portfolio (Darden Case)

Principal component analysis, Factor analysis, Conjoint analysis, Discriminant analysis
Supply chain analytics
Auto-Regressive Integrated Moving Average (ARIMA) models, ARIMAX
Lean thinking: Lean manufacturing, Value stream mapping
Six Sigma as a problem-solving methodology, DMAIC, and DMADV methodology, Six Sigma Tool Box: Seven quality tools, Quality function deployment (QFD), SIPOC, Statistical process control, Value stream mapping, TRIZa

Case Studies

Apollo Hospitals: Differentiation through Hospitality (IIMB Case)
Dosa King – A Standardized Masala Dosa for Every Indian (IIMB Case)
Dean’s Dilemma: To Admit or Not to Admit (IIMB Case)
Delivering Doors in a Window – Supply Chain Management at Hindustan Aeronautics Limited (IIMB Case)

Introduction to Big Data; sources of Big Data
Employing Hadoop MapReduce
Big Data technologies: Hadoop distributed file system
Statistical Analysis of Big Data

Support vector machine and Neural Network
Recommender Systems, Collaborative Filtering: Cosine Similarity, Jaccard Coefficient. Advanced recommender system
Introduction to machine learning, different types of machine learning algorithms
Bootstrap Aggregating (Bagging), Random forest, Adaptive boosting, gradient boosting

Case Study

Predicting Earnings Manipulation by Indian Companies Using Machine Learning Algorithms (IIMB Case)

Introduction to neural networks; rule-based expert systems
Deep learning algorithms: Convolutional networks; Recurrent nets; Auto-encoders
Introduction to artificial neural networks (ANN); Neuron as computing element; Perceptron: McCullogh-Pitts model; Back-propagation algorithm; Multi-layer Neural Networks
Deep Learning Platform: H2O.ai; Dato GraphLab; Tensor Flow

Dynamic pricing and revenue management, high-dimensional data analysis, financial data analysis, and prediction
Analytics in finance, Discounted cash flows (DCF), Profitability analysis. Asset performance: Sharpe ratio, Calmar ratio, Value at Risk (VaR), Brownian motion process, Pricing options, and Black–Scholes formula
Survival analysis and its applications: Life tables, Kaplan Meier estimates, Proportional hazards, Predictive hazard modelling using customer history data
Analysis of unstructured data: text mining and sentiment analysis, analysis of machine-generated data
Game theory: Two-person zero-sum game, dynamic games

Case Studies

1920 Evil Returns – Bollywood and Social Media Marketing (IIMB Case)
Markdown optimization at Indian Retail Store (IIMB Case)

Evaluation process

The candidates will have to take up project work and home assignments for evaluation purposes. As every module ends, they will be provided with a home assignment. It is essential for candidates to complete it and then submit it within a time frame of 5 weeks.

Instructors

IIM Bangalore Frequently Asked Questions (FAQ's)

1: Who is the course offered by?

The Indian Institute of Management Bangalore is offering the Business Analytics & Intelligence programme.

2: Where will the classes be organized?

The classes for the Business Analytics & Intelligence training will be held on the online platform. There is also an option for the candidates to learn at the campus as well.

3: How can the students pay the course fee?

The students can pay the Business Analytics & Intelligence fee through the means of online payment.

4: Who will be the course instructor?

The Business Analytics & Intelligence programme instructors will be Professor U Dinesh Kumar and Professor Rajluxmi V Murthy.

5: By whom will the certificate be issued?

The Business Analytics & Intelligence certification benefits the students by providing them with a certificate from IIM Bangalore.