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Certification Program on Business Analytics, Data Mining and Operations Research





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Delhi Centre


Statistical Quality Control &

Operations Research Unit (SQC & OR Unit)




Certification Program on Business Analytics, Data Mining and Operations Research

Venue: Indian Statistical Institute Delhi Centre

Results for Certification Program on Business Analytics, Data Mining and Operations Research Examination 2019

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 SPSS, Minitab, Matlab & R and management skills to improve efficiency, financial performance and implement strategic solutions.

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

Course fee: Rs. 50,000/- + (GST 18%)=Total Rs 59000/- per participant which include course material, lunch and refreshment.

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

Dates: September 16-19, 2019, October 21-24, 2019, November 19-22, 2019, December 9- 12, 2019 (16 days).

Examination: December 13, 2019 (Friday)

Programme Coordinator: Professor S. K. Neogy (ph. No. 011-41493966, 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 and Registration form (download the form as pdf file) on or before August 26, 2019 to the Progammme Coordinator in the mailing address given 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 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).

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

e.mail: sqc@isid.ac.in, skn@isid.ac.in


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: April 5,2019