• Introduction to Data Types

• Numerical parameters to represent data

          a. Mean  

          b. Mode 

          c. Median 

          d. Sensitivity 

          e. Information Gain 

          f.  Entropy

• Statistical parameters to represent data

Objectives: At the end of this Module, you should be able to:

• Understand rules of probability

• Learn about dependent and independent events

• Implement conditional, marginal and joint probability using Bayes Theorem

• Discuss probability distribution

• Explain Central Limit Theorem

Topics:

• Uses of probability

• Need of probability

• Bayesian Inference

• Density Concepts

• Normal Distribution Curve

Objectives: At the end of this Module, you should be able to:

• Understand concept of point estimation using confidence margin

• Draw meaningful inferences using margin of error

• Explore hypothesis testing and its different levels

Topics:

• Point Estimation

• Confidence Margin

• Hypothesis Testing

• Levels of Hypothesis Testing

Objectives: At the end of this module, you should be able to:

• Understand concept of association and dependence

• Explain causation and correlation

• Learn the concept of covariance

• Discuss Simpson’s paradox

• Illustrate Clustering Techniques

Topics:

• Association and Dependence

• Causation and Correlation

• Covariance

• Simpson’s Paradox

• Clustering Techniques

Objectives: At the end of this module, you should be able to:

• Understand Parametric and Non-parametric Testing

• Learn various types of parametric testing

• Discuss experimental designing

• Explain a/b testing

Topics:

• Parametric Test

• Parametric Test Types

• Non- Parametric Test

• Experimental Designing

• A/B testing

Objectives: At the end of this module, you should be able to:

• Understand the concept of Linear Regression

• Explain Logistic Regression

• Implement WOE

• Differentiate between heteroskedasticity and homoscedasticity

• Learn concept of residual analysis

Topics:

• Logistic and Regression Techniques

• Problem of Collinearity

• WOE and IV

• Residual Analysis

• Heteroscedasticity

• Homoscedasticity