Foundation Course
Predictive Modeling

using Python

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Program Objective

This program is designed for familiarizing and providing hands-on experience to participants on various statistical and machine learning techniques for predictive modelling using python

Program Benefits

The participants will acquire the knowledge required for

  • Data summarization, visualization and aggregation
  • Statistical techniques for decision making
  • Supervised (predictive modeling) learning techniques
  • Methods for model significance, accuracy, adequacy and generalizability checks
  • Hands-on experience on the usage of open-source package Python


  • Any science or engineering graduate working in the analytics field or aspiring to get into analytics can participate in the program.
  • The knowledge of a programming language is desirable but not necessary
  • The examination will be conducted at the end of the course and successful candidates will be issued the certificate

Course Content

  1. Introduction to Predictive Modeling
  2. Introduction to Python
  3. Test of Hypothesis
  4. Normality Test
  5. Analysis of Variance
  6. Cross Tabulation and Chi-Square Test
  7. Linear Regression
  8. Dummy Variable Regression
  9. Logistic Regression
  10. Classification and regression tree (CART)
  11. Random Forest
  12. Bagging
  13. Naïve Bayes Classifier
  14. K-Nearest Neighbor
  15. Support Vector Machine (SVM)