A Primer on Predictive Analytics

Predictive analytics (PA) is on everyone’s lips. But what is it really all about? Discover its principle, implementation, typical pitfalls and good practices. Learn about data wrangling and munging, a crucial step in predictive analytics. An overview of the most commonly used models is also presented.



  • Goals and context of use of PA
  • Principle underlying PA
  • Model-building
  • Measuring the predictive ability of a model
  • Setting Up a PA Study
    • Defining the goal
    • Selecting and preparing the data
    • Choosing and testing models
    • Overwiew of the most commonly used models
    • Summary
  • Mistakes to avoid
  • Good practices
This applied training session is aimed at people who need to understand the steps involved in the development of a predictive model.This module introduces key concepts in modelling. It assumes that participants either have no previous knowledge of statistics or that they have not used statistics for a long time.
Upon completion of this module, participants will be able:
    • To understand what predictive analytics (PA) is about
    • To master the general principles underlying the realization of a PA study (data munging, data selection, etc.)
    • To understand the key issues to address in a PA study and common pitfalls
    • To know about the most commonly used modelling techniques in PA

Recommended Duration: 1 day(s)

Course Materials:


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