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
Section 1: Wednesdays 9–9:50 am, Chen 100
Instructor office hours: Tuesdays 2:00-3:00 pm, Kerckhoff B123
TA recitation: Thursdays 7–8:30 pm, Chen 130
TA homework help: Wednesdays 2:30–4 pm and Thursdays 8:30–10 pm, Chen 130
Homework due dates
Homework 1: due 5 pm, October 6
Homework 2: due 5 pm, October 13
Homework 3: due 5 pm, October 20
Homework 4: due 5 pm, October 27
Homework 5: due 5 pm, November 3
Homework 6: due 5 pm, November 10
Homework 7: due 5 pm, November 17
Homework 8: due 11:59 pm, December 1
Homework 9: due 5 pm, December 7
Homework 10: due 5 pm, December 8
Lesson exercise due dates
Lesson exercise 1: due noon, October 1
Lesson exercise 2: due noon, October 8
Lesson exercise 3: due noon, October 15
Lesson exercise 4: due noon, October 22
Lesson exercise 5: due noon, October 29
Lesson exercise 6: due noon, November 5
Lesson exercise 7: due noon, November 12
Lesson exercise 8: due noon, November 19
Lesson exercise 9: due noon, November 26
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
Lesson 00: Preparing for the course
- Week 2
M 10/02: Lesson 03: Introduction to Python
M 10/02: Lesson 04: Style
M 10/02: Lesson 05: Test-driven development
M 10/02: Lesson 06: Exploratory data analysis, part 1
W 10/04: Lesson 09: Good data storage and sharing practices (guest lecture by Tom Morrell)
Th 10/05: Recitation 02: Homework help for beginning programmers
- Week 3
M 10/09: Lesson 07: Exploratory data analysis, part 2
M 10/09: Lesson 08: File formats
M 10/09: Lesson 10: Data wrangling
W 10/11: Lesson 11: Introduction to probability (lecture)
Th 10/12: Recitation 03: Manipulating data frames
- Week 4
M 10/16: Lesson 12: Random number generation
M 10/16: Lesson 13: Probability distributions
W 10/18: Lesson 14: Plug-in estimates and confidence intervals (lecture)
Th 10/19: Recitation 04: Probability review
- Week 5
M 10/23: Lesson 15: Nonparametric inference with hacker stats
W 10/25: Lesson 16: Null hypothesis significance testing (lecture)
Th 10/26: Recitation 05: Overplotting
- Week 6
M 10/30: Lesson 17: NHST with hacker stats
W 11/01: Lesson 18: Parametric inference (lecture)
Th 11/02: Recitation 06: Dashboards
- Week 8
M 11/13: Lesson 21: Confidence intervals of MLEs
M 11/13: Lesson 22: Implementation of variate-covariate models
W 11/15: Lesson 24: Model assessment and information criteria (lecture)
Th 11/16: Recitation 07: Review of MLE
- Week 9
M 11/20: No reading, but complete Lesson exercise 8
W 11/22: Lesson 27: Reproducible workflows (guest lecture by Griffin Chure, 9 AM PST)
W 11/22: Lesson 28: The paper of the future (guest lecture by Griffin Chure, 10 AM PST)
Th 11/23: No recitation, Thanksgiving holiday
- Week 10
M 11/27: Lesson 23: Mixture models
M 11/27: Lesson 25: Implementation of model assessment
W 11/29: Lesson 26: Statistical watchouts (lecture)
Th 11/30: Recitation 08: Topics in bootstrapping