Curriculum
Understanding the Data
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 Types
- Numerical parameters to represent data
a. Mean
- b. Mode
- c. Median
- d. Sensitivity
- e. Information Gain
- f. Entropy
- Statistical parameters to represent data
Probability and its uses
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
Statistical Inference
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
Data Clustering
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
Testing the Data
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
Regression Modelling
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 concept of residual analysis
Topics:
- Logistic and Regression Techniques
- Problem of Collinearity
- WOE and IV
- Residual Analysis
- Heteroscedasticity
- Homoscedasticity