DescriptionProgram StructureEligibilityFacultyContact

In this online course, you will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction.

In both cases, predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features.

In classification, the target variable is categorical (“purchased something” vs. “has not purchased anything”).

In prediction, the target variable is continuous (“dollars spent”).

You will learn how to explore and vizualize the data, to get a preliminary idea of what variables are important, and how they relate to one another.  Four modeling techniques will be used:

  • k-nearest neighbors,
  • classification and regression trees (CART), and
  • Bayesian classifiers.
  • Then you will learn how to combine different models to obtain results that are better than any of the individual models produce on their own.

The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model). The course includes hands-on work with XLMiner, a data-mining add-in for Excel.

Course Program:

  • Week 1: Preparation
  • Week 2: Classification and Prediction
  • Week 3: Bayesian Classifiers; CART
  • Week 4: Ensembles

Important Date:

May 27, 2016 to June 24, 2016


4 weeks

Time Requirement:

About 15 hours per week, at times of your choosing


INR 32,940 (assuming $ = INR 60)

Part time/Full Time:

Part Time

These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

  • Statistics 1 – Probability and Study Design
  • Statistics 2 – Inference and Association


Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Mr. Anthony Babinec

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