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Predictive Modeling & Data Analytics

Part-I : Basic Analytic s
Descriptive Statistics Introduction to Advanced Data Analytic
Statistical inferences for various Business problems
Types of Variables, measures of central tendency and dispersion
Variable Distributions and Probability Distributions
Normal Distribution and Properties
Case Studies discussion for

Part-I Session 1
Test of Hypothesis Null/Alternative Hypothesis formulation
One Sample, two sample (Paired and Independent) T/Z Test
P Value Interpretation
Analysis of Variance (ANOVA)
Chi Square Test
Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
Case Studies discussion for

Part-I Session 2
Multivariate Regression Introduction to Correlation - Karl Pearson and Graphical Methods
Spearman Rank Correlation
OLS Regression - Simple and Multiple
Case Studies discussion for

Part-II : Advanced Analytics
Logistic Regression Non Linear Regressions
Binomial Propensity Modeling
Training-Validation approach
Case Studies discussion for

Part-II Session 1
Factor Analysis Introduction to Factor Analysis – PCA
Reliability Test
KMO MSA tests, Eigen Value Interpretation
Factor Rotation and Extraction
Part-II Session 2
Cluster Analysis Introduction to Cluster Techniques
Distance Methodologies
Hierarchical and Non-Hierarchical Procedures
K-Means clustering
Wards Method

Part-III : Time Series Analysis
Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis
Decomposition of Time Series
Trend and Seasonality detection and forecasting
Introduction to Auto Regression and Moving Averages, ACF,
Logistic Regression
Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data
Opening and Saving models using Rattle