Specialization Basics and Practice of Data Mining

The Data Mining Specialization is designed for data scientists and data experts who want to learn fundamental concepts and basic techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the fundamental steps of data understanding, data preprocessing, data warehousing, data modeling, and interpretation/evaluation; (2) Data Mining Techniques, which covers basic techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience in designing and implementing a real-world data mining project. The Data Mining course can be counted toward CU Boulder’s Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that includes faculty from CU Boulder’s Applied Mathematics, Computer Science, Information Science, and other departments. Based on learning outcomes and requiring no application, the MS-DS program is ideal for individuals with broad educational and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available on Unsplash: https://unsplash.com/photos/QG93DR4I0NE Applied learning project There are programming assignments that cover specific aspects of the data mining pipeline and methods. In addition, the Data Mining Project course provides step-by-step guidance and hands-on experience in formulating, designing, implementing, and reporting on a real data mining project.