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.

    • Description



    Course curriculum

    • 1

      Chapter 1 - Overview of Predictive Analytics

    • 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
      • Gradient Descent (& code)
      • Gradient descent fundamentals
      • Stochastic Gradient descent regression
      • 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
      • Business applications of LPP
      • Chapter 5 -Quiz
    • 6

      Chapter 6 -Business Decisions I

      • Parametric & Non Parametric Methods -Model building
      • Tradeoffs -Accuracy vs Explainability
      • Chapter - 6 Quiz
    • 7

      Chapter 7 -Business Decisions ..II

      • Framework to choose the right model to address business problems
      • Chapter 7 -Quiz