Data Science and Analytics

0 or 3 Credits · Level: Graduate, Undergraduate

Introduction of data science concepts (data collection, cleaning, filtering, pre-processing, modeling, knowledge extraction, actionable recommendations). The data science process and its connections to statistical techniques. Elements of database use and of SQL. Algorithms. Data exploration and elements of visualization. Ethical issues and ways to implement them. Examples applications include fraud detection, social networks, and spam filters, among others. Prerequisites: Intermediate coding experience in Python3, and knowledge of introductory statistics, 3 credits.