Business analytics is a discipline that focuses on understanding business performance and developing new strategies based on data and statistics. It uses specialized techniques and tools to gather information, organize data and interpret it to support business decision making. Business intelligence professionals make use of programming software like Minitab, SPSS, Matlab & R and management skills to improve efficiency, financial performance and implement strategic solutions.

Objective: The online program offers rigorous training that will prepare you to use data and analytics to identify business opportunities, generate business insights and create business solutions.

Course fee: Rs. 45,000/- + (GST 18%)=Total Rs 53100/- per participant(Course fee once deposited is non-refundable/non-transferable)..

Participants Profile: Bachelor's degree with Mathematics/Statistics/ Physics or Bachelor's degree in Economics with Statistics as subject or BE/B Tech degree or MBA, or Six Sigma black belt

Tentative Dates: 1st Module 12 - 15 September 2023, 2nd Module 10 - 13 October 2023
3rd Module 20 - 23 November 2023, 4th Module 11 - 14 December 2023
Examination :- 15 Dec. 2023

Venue: Indian Statistical Institute Delhi Centre

Programme Coordinator: Professor S. K. Neogy (ph. No. 011-414939 68/66, e.mail:skn@isid.ac.in, sknisid@gmail.com)

For Certification Program on Business Analytics, Data Mining and Operations Research please send your participant details along with bio-data, on line transaction details/draft and Registration form (download the form as pdf file) on or before September 6, 2023 to the Progammme Coordinator in the mailing address given below.

All payments regarding the programs need to be done electronically i.e.by NEFT/RTGS/etc. by the participants. Details are attached below.

Please ensure that you satisfy the eligibility criteria. If you do not satisfy eligibility criteria/ qualification criteria, then your application will be rejected and draft/paid amount will be sent back to your address. For further details, visit http://www.isid.ac.in/~sqc/badmor.html or write to programme coordinator, Professor S. K. Neogy (e.mail: skn@isid.ac.in, sknisid@gmail.com).

Mailing address: Professor S. K. Neogy

Programme Coordinator

Room no. 320/318 Faculty Block

SQC & OR unit, Indian Statistical Institute,

7, S. J. S. Sansanwal Marg,

New Delhi: 110016, India

Ph. No: 41493968, 41493966/Mob: 8800170139

e.mail: skn@isid.ac.in, sknisid@gmail.com, sqc@isid.ac.in,

Syllabus

Module 1: Introduction, Basic Concepts and Visual Analytics using R

-Introduction to Analytics and concepts of statistical / machine learning / analytics problems, Developing a Predictive Business Analytics Function

-Introduction to random experiments and random variable, concepts of parameters, concepts of visual analytics using random variables and their parameters, different summary measures and presentations, examples and exercises

-Graphs and Charts for data visualization

-Sampling Concept and Methods

Module 2: Probability distributions, Estimation and Hypothesis Testing

-Distribution as a model of a business process; usage of distribution for decision making.

-Probability and Distributions, understanding normal distribution, brief introduction to some useful distributions, probability computations

-Estimation and hypothesis Testing

-Estimation of parameters like means, variances, proportions and model parameters in different circumstances and their usages in analytics;

-Concepts of standard errors and confidence intervals

-Theory of estimates; concept of likelihood, sufficiency and information criteria.

-Formulating hypotheses in real life scenarios

-Test for means, variances, proportions, odds ratios and relative risks

Module 3: Prediction Modeling through Regression

-Simple and multiple linear regressions.

-Concepts of cross validation, usage of validation set, k fold cross validation and concepts of bootstrap.

-Logistic regression

-Multivariate data analysis

Module 4a: Tree based Method, Forecasting and Segmentation

-Classification and Regression Tree including concepts of bagging, random forests and boosting, fitting and validating tree based models

-Forecasting by using AR, MA and ARMA models. Moving average and Exponential smoothing for forecasting, Measure of forecasting accuracy.

-Cluster analysis, carrying out non-hierarchical clustering, choosing the right solution for non-hierarchical clustering

Module 4b: Operations Research, Neural Network and Game Theory

- Text as Data: Text Mining and Sentiment Analysis, Market Basket Analysis: Association Rules and Lift

-Introduction to deep learning including project and presentation

-Wrap up and examination

After each module assignments will be given. Participants need to carry out the assignments in team and present the finding in first day of next module.

Last Modified: August19, 2023