Data Science II

Catalog description

3 Lecture Hour(s) 0 Lab Hour(s) This second course in data science will cover data wrangling, web scraping, text mining, and an introduction to predictive modeling. Topics covered will include reshaping data, joining data tables, string processing, processing dates/times, topic modeling, sentiment analysis, cross-validation, various examples of predictive modeling algorithms, and techniques for handling large datasets such as dimension reduction and regularization. Students will gain hands-on experience using the Python or R programming environment, and through empirical data science case studies and class projects. Prerequisites: DASC 240. (F;S;SS)