1. Descriptive Statistics and Probability Distributions:

Introduction about Statistics

Different Types of Variables

Measures of Central Tendency with examples

Measures of Dispersion

Probability & Distributions

Probability Basics

Binomial Distribution and its properties

Poisson distribution and its properties

Normal distribution and its properties

2.Inferential Statistics and Testing of Hypothesis

Sampling methods

Different methods of estimation

Testing of Hypothesis & Tests

Analysis of Variance

3.Covariance & Correlation

Data Preparation

Exploratory Data analysis

Model Development

Model Validation

Model Implementation

4.Supervised Techniques:

Linear Regression – Introduction – Applications

Assumptions of Linear Regression

Building Linear Regression Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)

Validation of Linear Regression Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc)

Interpretation of Results – Business Validation – Implementation on new data

Real time case study of Manufacturing and Telecom Industry to estimate the future revenue using the models

->> Logistic Regression – Introduction – Applications

Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

Building Logistic Regression Model

Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)

Validation of Logistic Regression Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, ROC Curve)

Probability Cut-offs, Lift charts, Model equation, drivers etc)

Interpretation of Results – Business Validation – Implementation on new data

Real time case study to Predict the Churn customers in the Banking and Retail industry

->> Partial Least Square Regression

Partial Least square Regression – Introduction – Applications

Difference between Linear Regression and Partial Least Square Regression

Building PLS Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)

Interpretation of Results – Business Validation – Implementation on new data

Sharing the real time example to identify the key factors which are driving the Revenue

5.Variable Reduction Techniques

*Factor Analysis

*Principle component analysis

Assumptions of PCA

Working Mechanism of PCA

Types of Rotations

Standardization

Positives and Negatives of PCA

6.Supervised Techniques Classification:

->> CHAID

->> CART

->> Difference between CHAID and CART

->> Random Forest

Decision tree vs. Random Forest

Data Preparation

Missing data imputation

Outlier detection

Handling imbalance data

Random Record selection

Random Forest R parameters

Random Variable selection

Optimal number of variables selection

Calculating Out Of Bag (OOB) error rate

Calculating Out of Bag Predictions

->> Couple of Real time use cases which are related to Telecom and Retail Industry. Identification of the Churn.

7.Unsupervised Techniques:

->> Segmentation for Marketing Analysis

Need for segmentation

Criterion of segmentation

Types of distances

Clustering algorithms

Hierarchical clustering

K-means clustering

Deciding number of clusters

Case study

->> Business Rules Criteria

->> Real time use case to identify the Most Valuable revenue generating Customers.

8.Time series Analysis:

 Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

*Basic Techniques

 Averages,

 Smoothening etc

 Advanced Techniques

 AR Models,

 ARIMA

 UCM

*Hybrid Model

*Understanding Forecasting Accuracy – MAPE, MAD, MSE etc

*Couple of use cases, To forecast the future sales of products

9.Text Analytics

*Gathering text data from web and other sources

Processing raw web data

Collecting twitter data with Twitter API

*Naive Bayes Algorithm

Assumptions and of Naïve Bayes

Processing of Text data

Handling Standard and Text data

Building Naïve Bayes Model

Understanding standard model metrics

Validation of the Models (Re running Vs. Scoring)

*Sentiment analysis

 Goal Setting

 Text Preprocessing

 Parsing the content

 Text refinement

 Analysis and Scoring