GS11 1053 Data Mining Methodology
Yamal, Jose-Miguel; Wu, Hulin. Three semester hours. Spring, annually. Grading System: Letter Grade. Prerequisites: Introductory statistics and inference, basic math and algebra skills, linear regression, and statistical programming.
In this course we will introduce new concepts of Data Science and Big Data analytics. We will cover application of various novel statistical and machine learning, data mining and artificial intelligence methods to the analysis, integration and predictions of large complex data from health sciences, industries and other disciplines. The emphasis will be on creative thinking, problem-solving skills, and a hands-on data exploration to generate and address important scientific and business questions from a variety of complex data. Among other methods, sparse regression, feature construction and feature set reduction, classification, clustering, tree-based approaches and dependency modeling will be detailed. This course is cross-listed at UTHealth School of Public Health (PH 1998). The venue of the course will be at the SPH.