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 3–4 pm, Kerckhoff B123
TA session: Thursdays 7–10 pm, Chen 130
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, Chen 130
- Final: 9 am–noon, December 10, Chen 130
Lesson exercise due dates
- EDA lesson exercise: due noon, October 5
- Probability and sampling lesson exercise: due noon, October 12
- Nonparametric stats lesson exercise: due noon, October 26
- NHST lesson exercise: due noon, November 9
- MLE lesson exercise: due noon, November 9
- Variate-covariate models lesson exercise: due noon, November 16
- Model assessment lesson exercise: due noon, November 23
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
- Appendix B: Configuring your computer
Week 1
- M 09/29: Course welcome and team set-up
- M 09/29: Lessons 2–9: Exploratory data analysis I
- W 10/01: Lesson 1: What are we doing? (lecture)
Week 2
- M 10/06: Lessons 10–16: 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 27 and 28: Nonparametric inference with hacker stats
- W 10/29: Lessons 30 and 31: Null hypothesis significance testing (lecture)
Week 6
- M 11/03: Midterm exam
- M 11/03: Lesson 32: NHST with hacker stats
- W 11/05: Lessons 34–37: Parametric inference (lecture)
Week 7
- M 11/10: Lessons 38–40: 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: Lesson ###: Mixture models
- M 11/24: Lessons ###: Implementation of model assessment
- W 11/26: Lessons ###: Principal components 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