BE/Bi/NB 203--schedule

Below is the class schedule. A few notes:

All regular meetings except Friday are in the Gates-Thomas 135. This includes the evening exercises, when students work together under the guidance of the course staff. On Friday, we will meet in 115 Gates-Thomas.
To allow attendance at International Student Orientation, incoming international grad students will skip sessions colored in purple and will have make-up sessions marked in green. The bootcamp for non-incoming international students ends Friday at noon.
The lessons are labeled with the initials of their primary author/presenter from the course staff.
There will be a special pre-bootcamp session on Saturday afternoon (Sept. 10) from 2-3:30 pm in room 115 of the Beckman Institute for students who are unable to complete Lesson 0.

Saturday, September 10

9 am - noon: Lesson session 1; Lesson 1 (JB): Welcome, .py files, and IPython; Lesson 2 (JB): Basic command line skills; Lesson 3 (JB): Variables, operators, and types; Lesson 4 (JB): More operators and conditionals
noon - 1:30 pm: Lunch
1:30 - 4:30 pm: Lesson session 2; Lesson 5 (DA): Lists and tuples; Lesson 6 (NN): Iteration; Lesson 7 (JB): Introduction to functions; Lesson 8 (JB): String methods
4:45 - 5:45 pm: Grad student lectures: Griffin, James, and Shyam talk about the importance of computation in their graduate work.
5:45 - 7:15 pm: Dinner
7:15 - 10:15 pm: Exercise 1

9 am - noon: Lesson session 3; Lesson 9 (JB): Review of exercise 1; Lesson 10 (JB): Packages and modules; Lesson 11 (AM): Version control with Git; Lesson 12 (JB): Forking and practice with Git
noon - 1 pm: Lunch
1 - 4 pm: Lesson session 4; Lesson 13 (JB): Dictionaries; Lesson 14 (JB): File I/O; Lesson 15 (JB): Exceptions and error handling; Lesson 16 (JB): Python style (PEP 8)
4:15 - 5:15 pm: Faculty lecture: Michael Elowitz, Dynamics and Design Logic of Cellular Systems
5:15 - 7 pm: Dinner
7 - 10 pm: Exercise 2

9 am - noon: Lesson session 5; Lesson 17 (JB): Review of exercise 2; Lesson 18 (JB): Introduction to NumPy and the SciPy stack; Lesson 19 (JB): NumPy arrays and operations with them; Lesson 20 (JB): Practice with NumPy arrays [solution]
noon - 1 pm: Lunch
1 - 4 pm: Lesson session 6; Lesson 21 (JB): Introduction to Matplotlib; Lesson 22 (JB): More plotting with Matplotlib; Lessons 23 and 24 (JB): Practice with NumPy arrays and Matplotlib [solution]
4:15 - 5:15 pm: Faculty lecture: Michael Dickinson, Exposing the fly brain with data bombs
5:15 - 7 pm: Dinner
7 - 10 pm: Exercise 3

9 am - 11:20 am: Lesson session 7; Lesson 25 (JB): Review of exercise 3; Lesson 26 (JB): Random number generation; Lesson 27 (JB): Hacker statistics; Lesson 28 (JB): Practice with hacker stats [solution]
noon - 1 pm: Lunch
1 - 4 pm: Lesson session 8; Lesson 29 (JB): Case study: computing the Luria-Delbrück distribution; Lesson 30 (JB): Introduction to Pandas; Lesson 31 (JB): Case study: extracting data of interest from frog tongue adhesion; Lesson 32 (JB): Practice with Pandas [solution]
4:15 - 5:15 pm: Faculty lecture: Markus Meister, Neural Computations [slides]
5:15 - 7 pm: Dinner
7-10 pm: Exercise 4

9 am - noon: Lesson session 9; Lesson 33 (JB): Review of exercise 4; Lesson 34 (JB): Seaborn and data display; Lesson 35 (JB): Testing and test-driven development; Lesson 36 (JB): Practice TDD [solution]
noon - 1 pm: Lunch
1 - 4 pm: Lesson session 10; Lesson 37 (JB): Performing regressions; Lesson 38 (GC): Introduction to image processing; Lesson 39 (GC): Case study: Basic image quantification; Lesson 40 (JB): Practice image processing [solution]
4:15-5:15 pm: Faculty lecture: Mitch Guttman and Noah Ollikainen, Predicting and designing 3D genome structures
5:15-7 pm: Dinner
7-10 pm: Exercise 5

9 am - noon: Lesson session 11; Lesson 41 (JB): Review of exercise 5; Lesson 42 (JB): The Jupyter notebook; Lesson 43 (JB/AM): Survey of other packages and languages; Lesson 44 (JB): Bootcamp recap
noon - 1 pm: Lunch
1 - 4 pm: Lesson session 9; Lesson 33 (JB): Review of exercise 3; Lesson 34 (JB): Seaborn and data display; Lesson 35 (JB): Testing and test-driven development; Lesson 36 (JB): Practice TDD
5:15-7 pm: Dinner
7 - 10 pm: Lesson session 10; Lesson 37 (JB): Performing regressions; Lesson 38 (GC): Introduction to image processing; Lesson 39 (GC): Case study: Basic image quantification; Lesson 40 (JB): Practice image processing

9 am - noon: Exercise 5
noon - 1:30 pm: Lunch
1:30 - 4:30 pm: Lesson session 11; Lesson 41 (JB): Review of exercise 5; Lesson 42 (JB): The Jupyter notebook; Lesson 43 (JB/AM): Survey of other packages and languages; Lesson 44 (JB): Bootcamp recap