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

data102@berkeley.edu

If you are requesting extensions or if you have personal concerns, please contact us via the email address above.

Announcements

All course announcements will be made on Ed.

Important Information

Lecture: Tuesdays and Thursdays from 2:00 PM to 3:30 PM remote on Zoom. See Ed posts (link coming soon) for the zoom link. Lecture videos will be recorded and links will be available on the course website within a few hours after lecture.

Discussion session: Wednesdays in-person (with virtual sessions for those unable to be on campus: more info TBA). See Ed posts (link coming soon) for more information on this. Attendance is highly encouraged but not mandatory.

Lab: Mondays in-person (with virtual sessions for those unable to be on campus: more info TBA). 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 [data102@berkeley.edu], and you should get a response within a few days. Please avoid emailing professors or GSIs directly!

Office Hours Schedules

Please see Ed posts for Zoom links for remote OHs.

For official holidays see the academic calendar.

Prerequisites

While we are working to make this class widely accessible we currently require the following (or equivalent) prerequisites :

  1. Principles and Techniques of Data Science: DS100 covers important computational and statistical skills that will be necessary for DS102.

  2. 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.

  3. 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.

Main Instructors

Ramesh Sridharan
Ramesh Sridharan

Thursdays 11:00am-12:00 pm @ Virtual, Fridays 2:00pm-3:00pm @ Virtual

(email)

Jacob Steinhardt
Jacob Steinhardt

OH: Tuesdays 3:30pm-4:30pm @ Evans 325

(email)

TAs

Alice Cima
Alice Cima

Disc: Wednesdays 12pm-1pm @ Virtual, 1pm-2pm @ Evans 9, 7pm-8pm @ Virtual

Lab: Mondays 12pm-1pm @ Hearst Field Annex B5, 1pm-2pm @ Etcheverry 3108

OH: Mondays 2pm-3pm @ Moffitt 145, Wednesdays 6pm-7pm @ Virtual

(email)

Ewen Dai
Ewen Dai

Disc: Wednesdays 3pm-4pm @ Evans 3, 4pm-5pm @ Wheeler 202

Lab: Mondays 3pm-4pm @ Virtual, 4pm-5pm @ Virtual

OH: Fridays 3pm-5pm @ Moffitt 145

(email)

Ruhi Doshi
Ruhi Doshi

Disc: Wednesdays 2pm-3pm @ Evans 70

Lab: Mondays 4pm-5pm @ Etcheverry 3111

OH: Mondays 5pm-6pm @ Virtual, Thursdays 10am-11am @ Virtual

(email)

Ritvik Iyer
Ritvik Iyer

Disc: Wednesdays 9am-10am @ Hearst Field Annex B5

Lab: Mondays 9am-10am @ Hearst Field Annex B5

OH: Tuesdays 9am-11am @ Virtual, Fridays 12pm-2pm @ Moffitt 145

(email)

Yimeng(Kobe) Wang
Yimeng(Kobe) Wang

Disc: Wednesdays 10am-11am @ Evans 9, 12-1pm @ Virtual

Lab: Mondays 10am-12pm @ Evans B6

OH: Thursdays 7-9pm PT @ Virtual

(email)

Readers

Victor Shi
Victor Shi

OH: TBA

(email)

Nabeel Hingun
Nabeel Hingun

OH: TBA

(email)

Christina Steinmeier
Christina Steinmeier

OH: TBA

(email)