Data Science in the Wild

3.0 credits · Graduate

This course focuses on the practical aspects of data science by providing tools to identify data patterns, evaluate the strength and significance of relationships, and generate predictions using data. In addition to covering the core principles of statistical programming (data frames, Python packages, reproducible workflows, and version control), students will learn how to use data to make effective arguments by operationalizing the data science pipeline: problem formulation (domain understanding), data preparation (collection, sampling, integration, cleaning), exploratory data analysis (univariate and multivariate statistical analysis of small and medium-size datasets), data modeling (regression methods, hypothesis testing, probability models, basic supervised and unsupervised machine learning), and communication (data presentation, visualization). Students who complete the course will be able to produce meaningful, data-driven analyses of real-world problems.