DSI-604 Predictive Analytics 1 - Machine Learning Tools - with R
In this course, students 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. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. 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 R, a free software environment for statistical computing.
Preview the Online Syllabus
(Please visit the University bookstore to view the correct materials for each course by semester as the contents of the actual online syllabus may differ from the preview due to updates or revisions)