CSE 351 is a multidisciplinary course teaches both the theory as well as the practice of extracting useful knowledge from data. The topics include linear algebra, probability, statistics, machine learning, and programming. Using large and messy data sets collected from real-world problems in areas of science, technology, and medicine, we introduce how to preprocess the data, identify the best model that describes the data, perform prediction and analyze the data, evaluate the results, and finally report the results using proper visualization methods. The course also teaches state-of-the-art tools for data analysis such as Python and its scientific libraries.
Lecture Meetings: Tuesday, Thursday 5:00 PM - 6:20 PM, JAVITS LECTR 102 WESTCAMPUS
Textbook and Required Course Materials:
Textbook: The Data Science Design Manual by Steven Skiena, Springer, 2017