Goal: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms of different statistical parameters.

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

• Understand various data types
• Learn Various variable types
• List the uses of variable types
• Explain Population and Sample
• Discuss sampling techniques
• Understand Data representation

Topics:

• Introduction to Data TypesNumerical parameters to represent data
• Mean
• Mode
• Median
• Sensitivity
• Information Gain
• Entropy
• Statistical parameters to represent data

Hands-On/Demo

• Estimating mean, median and mode using python
• Calculating Information Gain and Entropy

Goal: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

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

Hands-On/Demo:

• Calculating probability using python
• Conditional, Joint and Marginal Probability using Python
• Plotting a Normal distribution curve

Goal: Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint).

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

• Understand the 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

Hands-On/Demo:

• Calculating and generalizing point estimates using python
• Estimation of Confidence Intervals and Margin of Error

Goal: In this module, you should learn the different methods of testing the alternative hypothesis.

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

Hands-On/Demo:

• Perform p test and t tests in python
• A/B testing in python

Goal: Get an introduction to Clustering as part of this Module which forms the basis for machine learning.

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

• Understand the 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

Hands-On/Demo:

• Correlation and Covariance in python
• Hierarchical clustering in python
• K means clustering in python

Goal: Learn the roots of Regression Modelling using statistics.

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 heteroscedasticity and homoscedasticity
• Learn the concept of residual analysis

Topics:

• Logistic and Regression Techniques
• Problem of Collinearity
• WOE and IV
• Residual Analysis
• Heteroscedasticity
• Homoscedasticity

Hands-On/Demo:

• Perform Linear and Logistic Regression in python
• Analyze the residuals using python