Data Mining and Analytics
The goal of Data Mining and Analytics is to introduce students to the practical fundamentals of data mining and machine learning with just enough theory to aid intuition building. The course is project-oriented, with a project beginning in class every Thursday and to be completed outside of class by the following week, or two weeks for longer assignments. The in class portion of the project is meant to be collaborative and a time for the instructor to work closely with groups to understand the learning objectives and help them work through any logistics that may be slowing them down. Tuesdays are lecture days which introduce the concepts and algorithms which will be used in the upcoming project. The primary objective is for everyone to leave the class with hands-on data mining and data engineering skills they can confidently apply. Knowledge of basic python programming is a strong prerequisite for this course.
Foster critical thinking about real world actionability from machine learned analytics.
Develop intuition in various machine learning classification algorithms (e.g. decision trees, neural networks / deep representation learning, support vector machines), clustering techniques (e.g. kmeans, spectral), as well as big data processing tools (e.g. map reduce).
Develop data engineering and High Performance Computing systems skills
Provide a preview of trends that will shape the need for data mining and analytics across a variety of disciplines.
(Previously offered as Info 290.)