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: Tuesdays, 2:30-3:30 pm, Broad 200

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

  • TA homework help: Wednesdays, 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 noon on the Sunday before the lesson. For example, the exercises to be completed after lesson 6 must be submitted by noon on Sunday, October 2.

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 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

    • W 09/28: Course welcome and team set-up

    • W 09/28: Lesson 01: Data analysis pipelines (lecture)

    • W 09/28: Lesson 02: Version control with Git

    • Th 09/29: Recitation 01: The command line and Git

  • Week 2

    • M 10/03: Lesson 03: Introduction to Python

    • M 10/03: Lesson 04: Style

    • M 10/03: Lesson 05: Test-driven development

    • M 10/03: Lesson 06: Exploratory data analysis, part 1

    • W 10/05: Lesson 09: Good data storage and sharing practices (guest lecture by Kristin Briney)

    • Th 10/06: Recitation 02: Introduction to image processing

  • Week 3
  • Week 4

    • M 10/17: Lesson 12: Random number generation

    • M 10/17: Lesson 13: Probability distributions

    • W 10/19: Lesson 14: Plug-in estimates and confidence intervals (lecture)

    • Th 10/20: Recitation 04: Probability review

  • Week 5

    • M 10/24: Lesson 15: Nonparametric inference with hacker stats

    • W 10/26: Lesson 16: Null hypothesis significance testing (lecture)

    • Th 10/27: Recitation 05: Overplotting

  • Week 6

    • M 10/31: Lesson 17: NHST with hacker stats

    • W 11/02: Lesson 18: Parametric inference (lecture)

    • W 11/02: Recitation 06: Dashboarding

  • Week 7
  • Week 8
  • Week 9

    • M 11/21: Lesson 24: Implementation of variate-covariate models

    • W 11/23: Lesson 26: Model assessment and information criteria (lecture)

    • W 11/23: No recitation, Thanksgiving holiday

  • Week 10