Introduction to Predictive Analytics
Traditional data analysis is about describing the data we have. Some more advanced models are used to explain why we get what we see. However, with advances in technology and statistical theory, most companies now employ (or can employ) a new statistical paradigm: predictive analytics: instead of finding characteristics of customers, predictive models can predict which one is likely to leave, or which lead is most likely to convert.
This change in paradigm benefits decision makers and managers as it provides more precise insights that lead to focused and valuable actions. But it also requires new statistical capabilities. It turns out that the best explanatory models are not always the best predictive models, and analysts now need to develop, evaluate and interpret their models differently.
In this workshop we will get an overview of the world of predictive models and analytics, enter into this new “mindset”, and learn the basic considerations and evaluation techniques. In a “hands on” manner we will learn to classify, estimate, cluster and predict outcomes in real world settings, using the R statistical environment.
The workshop is modular and built as a mix of interactive demonstrations of key topics, in-class exercises based on real business settings and “open audience consultations” in which participants bring their own data and receive advice on how to accomplish their goal. The typical workshop takes 3-4 full days, and specific topics can be tailored to the needs and background of participants. Some background in using R is required.