The course will combine three key elements: analytics techniques, business applications, and basic coding/programming (in R, one of the leading open'source tools for analyzing data that you will be able to use in your jobs.) The emphasis will be not on the technicalities or theory, but rather on applications to various business cases. Basic familiarity with R is required, but for most classes you will receive a starter code, by running and modifying which you will learn analytics techniques and coding principles; which you can use in your jobs. Because of that, much of the course will be in a form of a "hands'on" workshop. The course will cover 2 major topics within the domain of predictive analytics: 'predicting quantities' and 'predicting events'. Within the 'quantities' part we will focus on linear models, variable selection and regularizations, as well as on time'series analyses. Within the 'events' part we will focus on generalized linear models (logistic regression) and get an introduction to supervised machine learning (CART, random forest, boosting, and neural networks).
The course will combine three key elements: analytics techniques, business applications, and basic coding/programming (in R, one of the leading open'source tools for analyzing data that you will be able to use in your jobs.) The emphasis will be not on the technicalities or theory, but rather on applications to various business cases. Basic familiarity with R is required, but for most classes you will receive a starter code, by running and modifying which you will learn analytics techniques and coding principles; which you can use in your jobs. Because of that, much of the course will be in a form of a "hands'on" workshop. The course will cover 2 major topics within the domain of predictive analytics: 'predicting quantities' and 'predicting events'. Within the 'quantities' part we will focus on linear models, variable selection and regularizations, as well as on time'series analyses. Within the 'events' part we will focus on generalized linear models (logistic regression) and get an introduction to supervised machine learning (CART, random forest, boosting, and neural networks).
