Schedule


Day 1

Registration & Welcome Tea

Inauguration & Keynote Address

Tea Break & Networking

Panel-1  Demystifying Business Analytics; 2018 and Beyond

Lunch

An overview on R-Programming in Data Science

 Introduction to R
 Understanding of Data Structure
 Import and exporting data from external source
 Understanding data and its type
 Summary of data set
 Aggregating functions
 Frequency analysis
 Missing value analysis and Missing value imputation
 Identifying Duplicate
 Merging
 Visualization

8.30 AM – 9.30 AM

9.30 AM – 10.30 AM

10.30 AM – 11.00 AM

11.00 AM – 1.00 PM 

01.00 AM – 2.00 PM

02.00 AM – 5.00PM

Day 2

Panel- II  Perpetual Talent opportunities

Lunch & Networking

Machine Learning Techniques with Case Study (Supervised Learning)

 Basic understanding of testing of Hypothesis
o T test
o Chi Square test
o ANOVA
 Correlation and Regression (Case study: Salary prediction)
o Introduction
o Application
o Types of regression
o Steps to build model
o Interpretation of model
o Prediction
o Implementation
 Logistic regression (Case study: Risk Analysis / HR attrition analysis)
o Basic concept
o Importance
o How logistic regression different from regression analysis
o Assumption
o Steps to build model
o Interpretation of model
o Prediction
o Implementation

10.00 AM – 12.00  PM

12.00 AM – 1.00 PM

01.00 AM – 5.00 PM

Day 3

Panel- III Choice of Business Analytics- Functional Area

Lunch & Net working

Machine Learning Techniques with Case Study (Unsupervised learning)

 Cluster analysis (Case Study: Customer Segmentation based on purchase behavior)
o Introduction
o Types of cluster analysis
o Application
o Steps to develop cluster analysis
o Profiling and interpretation
 Recommendation engine (Market Basket analysis: E-commerce product recommendation)
o Introduction
o Application
o Support , confidence and Lift
o Develop Recommendation engine
o Interpretation
 Time Series Analysis
o Understanding of Time series data
o Application
o Time series component
o Forecasting technique
 Moving average
 Auto regression
 ARIMA model

10.00 AM – 12.00  PM

12.00 AM – 1.00 PM

01.00 AM – 5.00 PM

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