Practical Data Science I

A flipped-classroom, exercise and project-focused course. Requires zero prior programming experience and begins with an introduction to Python, computational thinking, and the principles of good programming using the 7 Steps method. Class focus then shifts to data analysis with an emphasis on the type of analyses of interest to social science, public policy, and natural scientists with human behavior/society overlap (biostats, ecology, etc.). The course provides students with experience manipulating and analyzing real (often messy, error-ridden, and poorly documented) data using the full range of Python tools (e.g., command line, git, VS Code, numpy, pandas, matplotlib, statsmodels, etc.).