Schedule overview

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


Homework due dates

  • Homework 1: due 5 pm, October 3
  • Homework 2: due 5 pm, October 10
  • Homework 3: due 5 pm, October 17
  • Homework 4: due 5 pm, October 24
  • Homework 5: due 5 pm, October 31
  • Homework 6: due 5 pm, November 7
  • Homework 7: due 5 pm, November 14
  • Homework 8: due 5 pm, November 21
  • Homework 9: due 5 pm, December 5
  • Homework 10: not graded

Exam dates

  • Midterm: In class, November 3
  • Final: 9 am–noon, December 10

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 lesson exercises about exploratory data analysis must be submitted by noon on Sunday, October 5.

If one were reading through the lessons, the numbering of the lessons represents the most logical order. However, due to the constraints 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 09/29: Course welcome and team set-up
  • M 09/29: Lessons 2–4: Exploratory data analysis I
  • W 10/01: Lesson 1: What are we doing? (lecture)

Week 2

  • M 10/06: Lessons 6–16: Exploratory data analysis II
  • W 10/08: Lessons 18 and 19: Introduction to probability (lecture)

Week 3

  • M 10/13: Lessons 20–22: Random number generation and its uses
  • W 10/15: Guest lecture by Tom Morrell: Good data storage and sharing practices

Week 4

  • M 10/20: No reading
  • W 10/22: Lessons 24–26: Plug-in estimates and confidence intervals (lecture)

Week 5

  • M 10/27: Lessons ###: Nonparametric inference with hacker stats
  • W 10/29: Lessons ###: Null hypothesis significance testing (lecture)

Week 6

  • M 11/03: Midterm exam
  • M 11/03: Lessons ###: NHST with hacker stats
  • W 11/05: Lessons ###: Parametric inference (lecture)

Week 7

  • M 11/10: Lessons ###: Numerical maximum likelihood estimation
  • W 11/12: Lessons ###: Variate-covariate models (lecture)

Week 8

  • M 11/17: Lessons ###: Confidence intervals of MLEs
  • M 11/17: Lessons ###: Implementation of variate-covariate models
  • W 11/19: Lessons ###: Model assessment and information criteria (lecture)

Week 9

  • M 11/24: Lessons ###: Mixture models
  • M 11/24: Lessons ###: Implementation of model assessment
  • W 11/26: Lessons ###: Principal component analysis

Week 10

  • M 12/01: No reading
  • W 12/03: Lessons ###: Statistical watchouts (lecture)

Week 11

  • W 12/10: Final exam, 9 am - noon