Schedule overview¶
The schedule information on this page is subject to changes.
- Lab
Section 1: Mondays, 9 am–noon PDT
Section 2: Mondays, 1–4 pm PDT
Section 3: Mondays, 7–10 pm PDT
Lecture: Wednesdays, 9–9:50 am PDT
Instructor office hours: Fridays, 2:30-4 pm PDT
TA recitation: Thursdays, 3-4:30 pm PDT
TA homework help: Thursdays, 4:30–6 pm PDT
Unless given notice otherwise, all sessions are at this Zoom link. However, at some points, the TAs may be in their own Zoom rooms. Here are the links.
Lectures and TA recitations will be recorded and posted at this Google Drive link.
Homework due dates¶
Homework 0: due noon PDT, October 4
Homework 1: due 11:59 pm PDT, October 9
Homework 2: due 11:59 pm PDT, October 16
Homework 3: due 11:59 pm PDT, October 23
Homework 4: due 11:59 pm PDT, October 30
Homework 5: due 11:59 pm PDT, November 6
Homework 6: due 11:59 pm PDT, November 13
Homework 7: due 11:59 pm PDT, November 20
Homework 8: due 11:59 pm PDT, December 4
Homework 9: due 5 pm PDT, December 9
Homework 10: due 5 pm PDT, December 11
Lesson exercise due dates¶
Lesson exercise 3: due noon PDT, October 4 (submit via email)
Lesson exercise 6: due noon PDT, October 11
Lesson exercise 8: due noon PDT, October 18
Lesson exercise 9: due noon PDT, October 25
Lesson exercise 14: due noon PDT, November 1
Lesson exercise 16: due noon PDT, November 8
Lesson exercise 18: due noon PDT, November 15
Lesson exercise 20: due noon PDT, November 22
Lesson exercise 24: due noon PDT, November 29
Weekly schedule¶
The notes for each Monday lesson must be read ahead of time and associated lesson exercises submitted by noon PDT on the Sunday before the lesson. For example, the lesson exercises for lesson 03 must be submitted by noon on Sunday, October 4.
- Week 0
Lesson 00: Preparing for the course
- Week 1
W 09/30: Lesson 01: Data analysis pipelines (lecture)
W 09/30: Lesson 02: Version control with Git (lecture)
Th 10/01: Recitation 01: Command line (LM)
- Week 2
M 10/05: Lesson 03: Introduction to Python
W 10/07: Lesson 04: Style (lecture)
W 10/07: Lesson 05: Test-driven development (lecture)
Th 10/08: Recitation 02: Git/GitHub tips and traps
- Week 3
M 10/12: Lesson 06: Exploratory data analysis
W 10/14: Lesson 07: Good data storage and sharing practices (guest lecture by Tom Morrell)
Th 10/15: Recitation 03: Time series and data smoothing
- Week 4
M 10/19: Lesson 08: Data wrangling
W 10/21: Lesson 09: Introduction to probability (lecture)
Th 10/22: Recitation 04: Manipulating data frames
- Week 5
M 10/26: Lesson 10: Overplotting
M 10/26: Lesson 11: Dashboards
W 10/28: Lesson 12: Plug-in estimates and confidence intervals (lecture)
Th 10/29: Recitation 05: Introduction to image processing
- Week 6
M 11/02: Lesson 13: Random number generation
M 11/02: Lesson 14: Probability distributions
W 11/04: Lesson 15: Null hypothesis significance testing (lecture)
Th 11/05: Recitation 06: Probability review
- Week 7
M 11/09: Lesson 16: Nonparametric inference with hacker stats
W 11/11: Lesson 17: Parametric inference (lecture)
Th 11/12: Recitation 07: Topics in bootstrapping
- Week 8
M 11/16: Lesson 18: Maximum likelihood estimation
W 11/18: Lesson 19: Model assessment and information criteria (lecture)
Th 11/19: Recitation 08: Wild and residual bootstrap
- Week 9
M 11/23: Lesson 20: Regression
W 11/25: Lesson 21: Reproducible workflows (guest lecture by Griffin Chure)
W 11/25: Lesson 22: The paper of the future (guest lecture by Griffin Chure, special time, 10 AM PST)
Th 11/26: No recitation, Thanksgiving Day
- Week 10
M 11/30: Lesson 23: Mixture models
M 11/30: Lesson 24: Implementation of model assessment
W 12/02: Lesson 25: Statistical watchouts (lecture)
Th 12/03: Recitation 09: Packaging and package management