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 


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Homework due dates
------------------

- :ref:`Homework 1<1. Practice with Python tools and EDA I>`: due 5 pm, October 4
- :ref:`Homework 2<2. Exploratory data analysis II>`: due 5 pm, October 11
- :ref:`Homework 3<3. Wrangling, EDA III, and probability distributions>`: due 5 pm, October 18
- :ref:`Homework 4<4. Working with probability distributions>`: due 5 pm, October 25
- :ref:`Homework 5<5. Nonparametric hacker stats>`: due 5 pm, November 1
- :ref:`Homework 6<6. Maximum likelihood estimation I>`: due 5 pm, November 8
- :ref:`Homework 7<7. Maximum likelihood estimation II>`: due 5 pm, November 15
- :ref:`Homework 8<8. Maximum likelihood estimation III>`: due 5 pm, November 22
- :ref:`Homework 9<9. Model assessment>`: due 5 pm, December 6
- :ref:`Homework 10<10. Maximum likelihood estimation IV>`: not graded
- :ref:`Homework 11<11. Course feedback>`: due 5 pm, December 13

.. - :ref:`Homework 10<Homework 10: Course feedback (20 pts)>`: due 5 pm, December 13


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Exam dates
------------------

- Midterm: In class, November 4
- Final: 9 am–noon, December 11


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Lesson exercise due dates
-------------------------

- :ref:`Lesson exercise 1<E1. To be completed after lesson 10>`: due noon, October 6
- :ref:`Lesson exercise 2<E2. To be completed after lesson 13>`: due noon, October 13
- :ref:`Lesson exercise 3<E3. To be completed after lesson 15>`: due noon, October 27
- :ref:`Lesson exercise 4<E4. To be completed after lesson 19>`: due noon, November 10
- :ref:`Lesson exercise 5<E5. To be completed after lesson 22>`: due noon, November 17
- :ref:`Lesson exercise 6<E6. To be completed after lesson 25>`: due noon, November 24


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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**
	+ :ref:`Lesson 00<0. Preparing computing resources for the course>`: Preparing for the course
- **Week 1**
	+ M 09/30: Course welcome and team set-up
	+ M 09/30: :ref:`Lesson 03<3. Introduction to Python>`: Introduction to Python
	+ M 09/30: :ref:`Lesson 04<4. Style>`: Style
	+ M 09/30: :ref:`Lesson 05<5. Test-driven development>`: Test-driven development
	+ M 09/30: :ref:`Lesson 06<6. Exploratory data analysis, part 1>`: Exploratory data analysis, part 1
	+ W 10/02: :ref:`Lesson 01<1. The cycle of science>`: Data analysis pipelines (lecture)
	+ W 10/02: :ref:`Lesson 02<2. Version control with Git>`: Version control with Git
- **Week 2**
	+ M 10/07: :ref:`Lesson 07<7. Exploratory data analysis, part 2>`: Exploratory data analysis, part 2 
	+ M 10/07: :ref:`Lesson 08<8. Data file formats>`: File formats
	+ M 10/07: :ref:`Lesson 10<10. Data wrangling>`: Data wrangling
	+ W 10/09: :ref:`Lesson 11<11. Intro to probability>`: Introduction to probability (lecture)
- **Week 3**
	+ M 10/14: :ref:`Lesson 12<12. Random number generation>`: Random number generation
	+ M 10/14: :ref:`Lesson 13<13. Probability distributions>`: Probability distributions
	+ W 10/16: :ref:`Lesson 09<9. Data storage and sharing>`: Good data storage and sharing practices (guest lecture by Tom Morrell)
- **Week 4**
	+ M 10/21: No reading
	+ W 10/23: :ref:`Lesson 14<14. Plug-in estimates and confidence intervals>`: Plug-in estimates and confidence intervals (lecture)
- **Week 5**
	+ M 10/28: :ref:`Lesson 15<15. Nonparametric inference with hacker stats>`: Nonparametric inference with hacker stats
	+ W 10/30: :ref:`Lesson 16<16. Null hypothesis significance testing>`: Null hypothesis significance testing (lecture)
- **Week 6**
	+ M 11/04: Midterm exam
	+ M 11/04: :ref:`Lesson 17<17. Hacker's approach to NHST>`: NHST with hacker stats
	+ W 11/06: :ref:`Lesson 18<18. Parametric inference>`: Parametric inference (lecture)
- **Week 7**
	+ M 11/11: :ref:`Lesson 19<19. Numerical MLE>`: Numerical maximum likelihood estimation
	+ W 11/13: :ref:`Lesson 20<20. Variate-covariate modeling>`: Variate-covariate models
- **Week 8**
	+ M 11/18: :ref:`Lesson 21<21. Confidence intervals of MLEs>`: Confidence intervals of MLEs
	+ M 11/18: :ref:`Lesson 22<22. Implementation of MLE for variate-covariate models>`: Implementation of variate-covariate models
	+ W 11/20: :ref:`Lesson 24<24. Model assessment>`: Model assessment and information criteria (lecture)
- **Week 9**
	+ M 11/25: :ref:`Lesson 23<23. Mixture models>`: Mixture models
	+ M 11/25: :ref:`Lesson 25<25. Implementation of model assessment>`: Implementation of model assessment
	+ W 11/27: :ref:`Lesson 27<27. Reproducible workflows>`: Reproducible workflows (guest lecture by `Griffin Chure <https://gchure.github.io>`_, **9 AM PST**)
	+ W 11/27: :ref:`Lesson 28<28. The paper of the future>`: The paper of the future (guest lecture by `Griffin Chure <https://gchure.github.io>`_, **10 AM PST**)
- **Week 10**
	+ M 12/02: No reading
	+ W 12/04: :ref:`Lesson 26<26. Statistical watchouts>`: Statistical watchouts (lecture)
- **Week 11**
	+ W 12/11: Final exam, 9 am - noon