Here’s an outline of what to expect from the course:
Note: Throughout the course we will work with real world examples in class, and will be challenging you to make inferences about the real world in your labs and other assignments. While we will open the semester with a focus on what it means to think scientifically, and we’ll close the semester with an explicit discussion of ethics in data science, both themes will be woven strongly through the presentation of all the material, as they’re really important.
Weeks 1, 2, 3
Introduction to data science, introduction to database-related programming in Python, and fundamentals of cause and effect.
Weeks 4, 5, 6, 7
Statistics fundamentals, including sampling, distributions, A/B testing, and uncertainty.
Weeks 9, 10, 11
Learning and making discoveries from data, including testing hypotheses, the normal distribution, and introduction to the power (and limitations and assumptions) of regression analysis.
Weeks 12, 13, 14
Advanced topics in learning from data, including classifiers and text as data, and ethics (including privacy, security, and bias), and a review.
Cumulative final exam.