This course is designed for the person who is new to the science of data analytics, who has completed at least one college-level math class, and is comfortable with basic statistics. The course explains the core methods used in data analytics and how to apply those methods in conjunction with RapidMiner, a free and easy-to-use (no programming knowledge required) data analytics platform.

You'll first learn about the features of RapidMiner, configuring it, and how to connect to a variety of data sets, and then move into a detailed survey of the analytical methods incorporated within the software. Topics covered include correlation, association rules, k-means clustering, k-nearest neighbors, discriminant analysis, Naive Bayes, linear and logistic regression, neural networks, decision trees, and text analysis.

• Learn how to use RapidMiner as a data analytics tool
• Gain a practical hands-on understanding of the core methods used in data analytics
• Explore correlational methods, affinity analysis methods, and predictive methods
• Discover which analytical method works best for a specific type of data
• Learn how to apply a selected method to build a model in RapidMiner and interpret its results

Professor Matt North teaches data analytics and data mining at Utah Valley University. He is a Fulbright alumnus, a recipient of a Gamma Sigma Alpha Outstanding Professor Award, and the author of the book "Data Mining for the Masses". He holds a Doctor of Education degree from West Virginia University and a Master of Science from Utah State University.