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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
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