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
	+ Section 3: Mondays, 7–10 pm, Chen 130
- Lecture
	+ Section 1: Wednesdays, 9–9:50 am, Chen 100
	+ Section 2: Wednesdays, 10–10:50 am, Chen 100
- Instructor office hours: Fridays, 2:30-3:30 pm, Broad 100
- TA recitation: Thursdays, 7–8:30 pm, Chen 130 
- TA homework help: Thursdays, 8:30–10 pm, Chen 130


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

- :ref:`Homework 0<0. Configuring your team>`: due as soon as you can, September 27
- :ref:`Homework 1<1. Practice with Python>`: due 5 pm, October 2
- :ref:`Homework 2<2. Practice with Numpy and plotting>`: due 5 pm, October 9
- :ref:`Homework 3<3. Exploratory data analysis I>`: due 5 pm, October 16
- :ref:`Homework 4<4. Exploratory data analysis II>`: due 5 pm, October 23
- :ref:`Homework 5<5. Dashboards>`: due 5 pm, October 30
- :ref:`Homework 6<6. Random number generation and probability distributions>`: due 5 pm, November 6
- :ref:`Homework 7<7. Nonparametric hacker stats>`: due 5 pm, November 13
- :ref:`Homework 8<8. Parametric inference>`: due 5 pm, November 20
- :ref:`Homework 9<9. Maximum likelihood estimation>`: due 11:59 pm, December 6
- :ref:`Homework 10<10. Model comparison>`: due 5 pm, December 8
- :ref:`Homework 11<Homework 11: Course feedback (20 pts)>`: due 5 pm, December 10


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

- :ref:`Lesson exercise 1<E1. To be completed after lesson 3>`: due 5 pm, October 3
- :ref:`Lesson exercise 2<E2. To be completed after lesson 6>`: due 5 pm, October 3
- :ref:`Lesson exercise 3<E3. To be completed after lesson 7>`: due 5 pm, October 10
- :ref:`Lesson exercise 4<E4. To be completed after lesson 10>`: due 5 pm, October 17
- :ref:`Lesson exercise 5<E5. To be completed after lesson 11>`: due 5 pm, October 24
- :ref:`Lesson exercise 6<E6. To be completed after lesson 16>`: due 5 pm, October 31
- :ref:`Lesson exercise 7<E7. To be completed after lesson 18>`: due 5 pm, November 7
- :ref:`Lesson exercise 8<E8. To be completed after lesson 20>`: due 5 pm, November 14
- :ref:`Lesson exercise 9<E9. To be completed after lesson 22>`: due 5 pm, November 21
- :ref:`Lesson exercise 10<E10. To be completed after lesson 26>`: due 5 pm, November 28


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Weekly schedule
---------------

The notes for each Monday lesson must be read ahead of time and associated lesson exercises submitted by 5 pm on the Sunday before the lesson. For example, the exercises to be completed after lesson 6 must be submitted by 5 pm on Sunday, October 3.

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 the lessons 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 09/27: Course welcome and team set-up
	+ M 09/27: :ref:`Lesson 02<2. Version control with Git>`: Version control with Git
	+ M 09/29: :ref:`Lesson 03<3. Introduction to Python>`: Introduction to Python
	+ W 09/29: :ref:`Lesson 01<1. The cycle of science>`: Data analysis pipelines (lecture)
	+ Th 09/30: :ref:`Recitation 01<R1. The command line>`: Command line
- **Week 2**
	+ M 10/04: :ref:`Lesson 06<6. Exploratory data analysis, part 1>`: Exploratory data analysis, part 1
	+ W 10/06: :ref:`Lesson 04<4. Style>`: Style (lecture)
	+ W 10/06: :ref:`Lesson 05<5. Test-driven development>`: Test-driven development (lecture)
	+ Th 10/07: :ref:`Recitation 02<R2. Git/Github tips and traps>`: Git/GitHub tips and traps
- **Week 3**
	+ M 10/11: :ref:`Lesson 07<7. Exploratory data analysis, part 2>`: Exploratory data analysis, part 2 
	+ W 10/13: :ref:`Lesson 09<9. Data storage and sharing>`: Good data storage and sharing practices (guest lecture by Tom Morrell)
	+ Th 10/14: :ref:`Recitation 03<R3. Time series and data smoothing>`: Time series and data smoothing
- **Week 4**
	+ M 10/18: :ref:`Lesson 08<8. Data file formats>`: File formats
	+ M 10/18: :ref:`Lesson 10<10. Data wrangling>`: Data wrangling
	+ W 10/20: :ref:`Lesson 11<11. Intro to probability>`: Introduction to probability (lecture)
	+ Th 10/21: :ref:`Recitation 04<R4. Manipulating data frames>`: Manipulating data frames
- **Week 5**
	+ M 10/25: :ref:`Lesson 12<12. Overplotting>`: Overplotting
	+ M 10/25: :ref:`Lesson 13<13. Dashboards>`: Dashboards
	+ W 10/27: :ref:`Lesson 14<14. Plug-in estimates and confidence intervals>`: Plug-in estimates and confidence intervals (lecture)
	+ Th 10/28: :ref:`Recitation 05<R5. Probability review>`: Probability review
- **Week 6**
	+ M 11/01: :ref:`Lesson 15<15. Random number generation>`: Random number generation
	+ M 11/01: :ref:`Lesson 16<16. Probability distributions>`: Probability distributions
	+ W 11/03: :ref:`Lesson 17<17. Null hypothesis significance testing>`: Null hypothesis significance testing (lecture)
	+ Th 11/04: :ref:`Recitation 06<R6. Intro to image processing>`: Introduction to image processing
- **Week 7**
	+ M 11/08: :ref:`Lesson 18<18. Nonparametric inference with hacker stats>`: Nonparametric inference with hacker stats
	+ W 11/10: :ref:`Lesson 19<19. Parametric inference>`: Parametric inference (lecture)
	+ Th 11/11: :ref:`Recitation 07<R7. Topics in bootstrapping>`: Topics in bootstrapping
- **Week 8**
	+ M 11/15: :ref:`Lesson 20<20. Maximum likelihood estimation>`: Maximum likelihood estimation
	+ W 11/17: :ref:`Lesson 21<21. Model assessment>`: Model assessment and information criteria (lecture)
	+ Th 11/18: :ref:`Recitation 08<R8. Review of maximum likelihood estimation>`: Review of maximum likelihood estimation
- **Week 9**
	+ M 11/22: :ref:`Lesson 22<22. Regression>`: Regression
	+ W 11/24: :ref:`Lesson 23<23. Reproducible workflows>`: Reproducible workflows (guest lecture by `Griffin Chure <https://gchure.github.io>`_, **9 AM PST**)
	+ W 11/24: :ref:`Lesson 24<24. The paper of the future>`: The paper of the future (guest lecture by `Griffin Chure <https://gchure.github.io>`_, **10 AM PST**)
	+ Th 11/25: No recitation, Thanksgiving Day
- **Week 10**
	+ M 11/29: :ref:`Lesson 25<25. Mixture models>`: Mixture models
	+ M 11/29: :ref:`Lesson 26<26. Implementation of model assessment>`: Implementation of model assessment
	+ W 12/01: :ref:`Lesson 27<27. Statistical watchouts>`: Statistical watchouts (lecture)
	+ Th 12/02: :ref:`Recitation 09<R9. Wild and residual bootstrap>`: Wild and residual bootstrap
	+ Th 12/02: :ref:`Recitation 10<R10. Packaging and package management>`: Packaging and package management