Schedule overview

The schedule information on this page is subject to changes. All times are Pacific.

  • Lab

    • Section 1: Mondays, 9 am–noon, Chen 130

    • Section 2: Mondays, 1–4 pm, Chen 130

    • Section 3: Mondays, 7–10 pm, Chen 130

  • Lecture

    • Section 1: Wednesdays, 9–9:50 am, Chen 100

    • Section 2: Wednesdays, 10–10:50 am, Chen 100

  • Instructor office hours: Fridays, 2:30-3:30 pm, Broad 100

  • TA recitation: Thursdays, 7–8:30 pm, Chen 130

  • TA homework help: Thursdays, 8:30–10 pm, Chen 130

Homework due dates

Lesson exercise due dates

Weekly schedule

The notes for each Monday lesson must be read ahead of time and associated lesson exercises submitted by 5 pm on the Sunday before the lesson. For example, the exercises to be completed after lesson 6 must be submitted by 5 pm on Sunday, October 3.

If one were reading through the lessons, the numbering of the lessons represents the most logical order. However, due to the constrains of class meeting times, some of the lessons are presented out of order. This is not a problem, though, as the lessons no lesson that strictly depends on another are presented out of order and the order shown in the schedule below is also a reasonable ordering of the lessons.

  • Week 0
  • Week 1

    • M 09/27: Course welcome and team set-up

    • M 09/27: Lesson 02: Version control with Git

    • M 09/29: Lesson 03: Introduction to Python

    • W 09/29: Lesson 01: Data analysis pipelines (lecture)

    • Th 09/30: Recitation 01: Command line

  • Week 2
  • Week 3

    • M 10/11: Lesson 07: Exploratory data analysis, part 2

    • W 10/13: Lesson 09: Good data storage and sharing practices (guest lecture by Tom Morrell)

    • Th 10/14: Recitation 03: Time series and data smoothing

  • Week 4
  • Week 5
  • Week 6

    • M 11/01: Lesson 15: Random number generation

    • M 11/01: Lesson 16: Probability distributions

    • W 11/03: Lesson 17: Null hypothesis significance testing (lecture)

    • Th 11/04: Recitation 06: Introduction to image processing

  • Week 7

    • M 11/08: Lesson 18: Nonparametric inference with hacker stats

    • W 11/10: Lesson 19: Parametric inference (lecture)

    • Th 11/11: Recitation 07: Topics in bootstrapping

  • Week 8

    • M 11/15: Lesson 20: Maximum likelihood estimation

    • W 11/17: Lesson 21: Model assessment and information criteria (lecture)

    • Th 11/18: Recitation 08: Review of maximum likelihood estimation

  • Week 9
  • Week 10