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

  • Lecture

    • Wednesdays 9–9:50 am, Chen 100

  • Instructor office hours: Wednesdays 2:00-3:00 pm, Kerckhoff B123

  • TA session: Thursdays 7–10 pm, Chen 130

Homework due dates

Exam dates

  • Midterm: In class, November 4

  • Final: 9 am–noon, December 11

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 10 must be submitted by noon on Sunday, October 6.

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

    • M 09/30: Course welcome and team set-up

    • M 09/30: Lesson 03: Introduction to Python

    • M 09/30: Lesson 04: Style

    • M 09/30: Lesson 05: Test-driven development

    • M 09/30: Lesson 06: Exploratory data analysis, part 1

    • W 10/02: Lesson 01: Data analysis pipelines (lecture)

    • W 10/02: Lesson 02: Version control with Git

  • Week 2

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

    • M 10/07: Lesson 08: File formats

    • M 10/07: Lesson 10: Data wrangling

    • W 10/09: Lesson 11: Introduction to probability (lecture)

  • Week 3

    • M 10/14: Lesson 12: Random number generation

    • M 10/14: Lesson 13: Probability distributions

    • W 10/16: Lesson 09: Good data storage and sharing practices (guest lecture by Tom Morrell)

  • Week 4

    • M 10/21: No reading

    • W 10/23: Lesson 14: Plug-in estimates and confidence intervals (lecture)

  • Week 5

    • M 10/28: Lesson 15: Nonparametric inference with hacker stats

    • W 10/30: Lesson 16: Null hypothesis significance testing (lecture)

  • Week 6

    • M 11/04: Midterm exam

    • M 11/04: Lesson 17: NHST with hacker stats

    • W 11/06: Lesson 18: Parametric inference (lecture)

  • Week 7

    • M 11/11: Lesson 19: Numerical maximum likelihood estimation

    • W 11/13: Lesson 20: Variate-covariate models

  • Week 8

    • M 11/18: Lesson 21: Confidence intervals of MLEs

    • M 11/18: Lesson 22: Implementation of variate-covariate models

    • W 11/20: Lesson 24: Model assessment and information criteria (lecture)

  • Week 9
  • Week 10

    • M 12/02: No reading

    • W 12/04: Lesson 26: Statistical watchouts (lecture)

  • Week 11

    • W 12/11: Final exam, 9 am - noon