About this Course
Data, Inference, and Decisions
This course develops the probabilistic foundations of inference in data science. It builds a comprehensive view of the decision-making and modeling life cycle in data science, including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, robustness, Thompson sampling, optimal control, Q-learning, differential privacy, fairness in classification, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
This class is listed as Data 102.
Course email address
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All course announcements will be made on Ed.
Lectures: Lectures will be held in-person Tuesdays and Thursdays from 12:30 - 2 in Li Ka Shing 245. Recordings will be made available on bCourses within 24 hours.
Discussion sessions: Discussion section will be held on Wednesdays, led by your GSIs. These sections will cover important problem-solving skills that bridge the concepts in the lecture with the skills you’ll need to apply the ideas on the homework and beyond.
Lab: Labs will be held on Mondays in-person. You can complete lab assignments on your own time, but you are highly encouraged to attend lab sessions to work with your classmates and get help from the staff.
Contacting Course Staff: The best way to contact course staff is using Ed, using a private post as needed—we’ll be checking it regularly and should respond to most questions within a day or less. If you need to reach out to course staff and Ed isn’t suitable, you can email [email@example.com], and you should get a response within a few days. Please avoid emailing professors or GSIs directly! You are much less likely to get a timely response.
While we are working to make this class widely accessible we currently require the following (or equivalent) prerequisites :
Principles and Techniques of Data Science: DS100 covers important computational and statistical skills that will be necessary for DS102.
Probability: Probability and Random Processes EECS126, or Concepts of Probability STAT134, or Probability for Data Science STAT140, or Probability and Risk Analysis for Engineers IEOR172. EECS126 and STAT140 are prefered. These courses cover the probabilistic tools that will form the underpinning for the concepts covered in DS102.
Math: Linear Algebra & Differential Equations Math54, or Linear Algebra MATH110, or both Designing Information Devices and Systems I EE16A and Designing Information Devices and Systems II EE16B, or Linear Algebra for Data Science Stat89a, or Introduction to Mathematical Physics PHYSICS89. We will need some basic concepts like linear operators, eigenvectors, derivatives, and integrals to enable statistical inference and derive new prediction algorithms.