What will you learn?

• Understand the difference between Cross sectional and Longitudinal data.
• Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.
• Understand Parametric and Non Parametric modelling approach towards addressing the key tradeoff between Predictive accuracy and Explain- ability of models.
• Use LPP towards building multiple “What if “ scenarios which are widely used in business decision making.
• Conceptualize Gradient Descent Algorithm which is a key foundation for most of the widely used Machine learning algorithms to be introduced subsequently

Pre-requisites

• Understanding data types.
• Key statistical concepts namely Hypothesis testing, Confidence Intervals, Significance.
• Python programming.

Course curriculum

• 1

• 2

Chapter 2 - Simple Linear Regression and Multiple Linear Regression

• Regression Fundamentals
• The linear regression equation
• Linear Regression explained
• Linear Regression with independent variable
• Interpreting R -Squared
• Evaluating Model Performance
• Key assumptions of Linear Regression
• Residual Analysis
• Statistical tests to validate assumptions
• Correlation and Causation
• Heat map and Scatter plots
• Multiple Linear Regression use case
• Interpreting regression outputs
• Regression use cases
• Chapter 2 Quiz
• 3

Chapter 3 - Time Series Forecasting

• Time Series Fundamentals
• Visualizing time series data using plots
• Components of Time series
• Stationary time series
• Forecasting fundamentals
• Forecasting techniques
• Forecasting techniques : Exponential Smoothing
• Forecasting techniques : Holt’s method
• Forecasting techniques : Holt’s Winter method
• Forecasting techniques : ACF & PACF
• Forecasting techniques : ARIMA
• Forecasting techniques : ARIMA models in Python
• Applications of Time Series
• Chapter 3 -Quiz
• 4

Chapter 4 - Prescriptive Analytics -Gradient Descent

• Introduction to Prescriptive Analytics
• Chapter 4 Quiz
• 5

Chapter 5-Prescriptive Analytics-Linear Programming Problems

• Linear Programming fundamentals
• Components of LPP
• Formulating the LPP model
• Solving linear models-Graphical method
• Solving linear models -Simplex method
• Assumptions of LPP
• Chapter 5 -Quiz
• 6