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: Tuesdays, 2:30-3:30 pm, Broad 200
- TA recitation: Wednesdays, 7–8:30 pm, Chen 130 
- TA homework help: Wednesdays, 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, not later than September 27
- :ref:`Homework 1<1. Practice with Python tools and EDA I>`: due 5 pm, October 7
- :ref:`Homework 2<2. Exploratory data analysis II>`: due 5 pm, October 14
- :ref:`Homework 3<3. Wrangling, EDA III, and Normal approximations>`: due 5 pm, October 21
- :ref:`Homework 4<4. Working with probability distributions>`: due 5 pm, October 28
- :ref:`Homework 5<5. Nonparametric hacker stats>`: due 5 pm, November 4
- :ref:`Homework 6<6. Maximum likelihood estimation I>`: due 5 pm, November 11
- :ref:`Homework 7<7. Maximum likelihood estimation II>`: due 5 pm, November 18
- :ref:`Homework 8<8. Maximum likelihood estimation III>`: due 11:59 pm, December 2
- :ref:`Homework 9<9. Model assessment>`: due 5 pm, December 8
- :ref:`Homework 10<Homework 10: Course feedback (20 pts)>`: due 5 pm, December 9


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

- :ref:`Lesson exercise 1<E1. To be completed after lesson 6>`: due noon, October 2
- :ref:`Lesson exercise 2<E2. To be completed after lesson 10>`: due noon, October 9
- :ref:`Lesson exercise 3<E3. To be completed after lesson 13>`: due noon, October 16
- :ref:`Lesson exercise 4<E4. To be completed after lesson 15>`: due noon, October 23
- :ref:`Lesson exercise 5<E5. To be completed after lesson 17>`: due noon, October 30
- :ref:`Lesson exercise 6<E6. To be completed after lesson 19>`: due noon, November 6
- :ref:`Lesson exercise 7<E7. To be completed after lesson 21>`: due noon, November 13
- :ref:`Lesson exercise 8<E8. To be completed after lesson 24>`: due noon, November 20
- :ref:`Lesson exercise 9<E9. To be completed after lesson 27>`: due noon, November 27


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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 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**
	+ :ref:`Lesson 00<0. Preparing computing resources for the course>`: Preparing for the course
- **Week 1**
	+ W 09/28: Course welcome and team set-up
	+ W 09/28: :ref:`Lesson 01<1. The cycle of science>`: Data analysis pipelines (lecture)
	+ W 09/28: :ref:`Lesson 02<2. Version control with Git>`: Version control with Git
	+ Th 09/29: :ref:`Recitation 01<R1. The command line and Git>`: The command line and Git
- **Week 2**
	+ M 10/03: :ref:`Lesson 03<3. Introduction to Python>`: Introduction to Python
	+ M 10/03: :ref:`Lesson 04<4. Style>`: Style
	+ M 10/03: :ref:`Lesson 05<5. Test-driven development>`: Test-driven development
	+ M 10/03: :ref:`Lesson 06<6. Exploratory data analysis, part 1>`: Exploratory data analysis, part 1
	+ W 10/05: :ref:`Lesson 09<9. Data storage and sharing>`: Good data storage and sharing practices (guest lecture by Kristin Briney)
	+ Th 10/06: :ref:`Recitation 02<R2. Intro to image processing>`: Introduction to image processing
- **Week 3**
	+ M 10/10: :ref:`Lesson 07<7. Exploratory data analysis, part 2>`: Exploratory data analysis, part 2 
	+ M 10/10: :ref:`Lesson 08<8. Data file formats>`: File formats
	+ M 10/10: :ref:`Lesson 10<10. Data wrangling>`: Data wrangling
	+ W 10/12: :ref:`Lesson 11<11. Intro to probability>`: Introduction to probability (lecture)
	+ Th 10/13: :ref:`Recitation 03<R3. Manipulating data frames>`: Manipulating data frames
- **Week 4**
	+ M 10/17: :ref:`Lesson 12<12. Random number generation>`: Random number generation
	+ M 10/17: :ref:`Lesson 13<13. Probability distributions>`: Probability distributions
	+ W 10/19: :ref:`Lesson 14<14. Plug-in estimates and confidence intervals>`: Plug-in estimates and confidence intervals (lecture)
	+ Th 10/20: :ref:`Recitation 04<R4. Probability review>`: Probability review
- **Week 5**
	+ M 10/24: :ref:`Lesson 15<15. Nonparametric inference with hacker stats>`: Nonparametric inference with hacker stats
	+ W 10/26: :ref:`Lesson 16<16. Null hypothesis significance testing>`: Null hypothesis significance testing (lecture)
	+ Th 10/27: :ref:`Recitation 05<R5. Overplotting>`: Overplotting
- **Week 6**
	+ M 10/31: :ref:`Lesson 17<17. Hacker’s approach to NHST>`: NHST with hacker stats
	+ W 11/02: :ref:`Lesson 18<18. Parametric inference>`: Parametric inference (lecture)
	+ W 11/02: :ref:`Recitation 06 <R6. Dashboards>`: Dashboarding
- **Week 7**
	+ M 11/07: :ref:`Lesson 19<19. Numerical MLE>`: Numerical maximum likelihood estimation
	+ W 11/09: :ref:`Lesson 20<20. Variate-covariate modeling>`: Variate-covariate models
	+ W 11/09: :ref:`Recitation 07 <R7. Topics in bootstrapping>`: Topics in bootstrapping
- **Week 8**
	+ M 11/14: :ref:`Lesson 21<21. Confidence intervals of MLEs>`: Confidence intervals of MLEs
	+ W 11/16: :ref:`Lesson 22<22. Reproducible workflows>`: Reproducible workflows (guest lecture by `Griffin Chure <https://gchure.github.io>`_, **9 AM PST**)
	+ W 11/16: :ref:`Lesson 23<23. The paper of the future>`: The paper of the future (guest lecture by `Griffin Chure <https://gchure.github.io>`_, **10 AM PST**)
	+ W 11/16: Recitation 08: MLE review
- **Week 9**
	+ M 11/21: :ref:`Lesson 24<24. Implementation of MLE for variate-covariate models>`: Implementation of variate-covariate models
	+ W 11/23: :ref:`Lesson 26<26. Model assessment>`: Model assessment and information criteria (lecture)
	+ W 11/23: No recitation, Thanksgiving holiday
- **Week 10**
	+ M 11/28: :ref:`Lesson 25<25. Mixture models>`: Mixture models
	+ M 11/28: :ref:`Lesson 27<27. Implementation of model assessment>`: Implementation of model assessment
	+ W 11/30: :ref:`Lesson 28<28. Statistical watchouts>`: Statistical watchouts (lecture)
	+ W 11/30: :ref:`Recitation 09<R9. Best Practices when using the Resnick High Performance Computing Center and other related topics>`: High-performance computing