Schedule

Below is the class schedule. A few notes:

Sunday, September 13

8-9 pm
Lesson 0 (JB/SS): Configuring your computer

Monday, September 14

9 am - noon
Lesson session 1
Lesson 1 (JB): Welcome and Hello, world.
Lesson 2 (AM): Basic command line skills (command_line_tutorial.zip)
Lesson 3 (JB): Variables, operators, and types
Lesson 4 (JB): More operators and conditionals
noon-1 pm
Lunch
1-4 pm
Lesson session 2
Lesson 5 (JB): Lists and tuples
Lesson 6 (JB): Iteration
Lesson 7 (JB): Introduction to functions
Lesson 8 (JB): String methods
4:15-5:15 pm
Faculty lecture: Niles Pierce, Engineering Programmable Molecular Instruments
5:15-7 pm
Dinner
7-10 pm
Exercise 1

Tuesday, September 15

9 am - noon
Lesson session 3
Lesson 9 (JB): Review of exercise 1
Lesson 10 (JB): Introduction to object-oriented programming
Lesson 11 (JB): Dictionaries (hash tables)
Lesson 12 (JB): Packages and modules
noon-1 pm
Lunch
1-4 pm
Lesson session 4
Lesson 13 (JB): File I/O (1OLG.pdb)
Lesson 14 (JB): Exceptions and error handling
Lesson 15 (JB): Testing and test-driven development
Lesson 16 (AM): Regular expressions (towels.txt, genbank_seq.txt, aligned.fasta)
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 (salmonella_sp1_region.fna)

Wednesday, September 16

9 am - noon
Lesson session 5
Lesson 17 (JB): Review of exercise 2
Lesson 18 (AM): More command line skills
Lesson 19 (JB): Introduction to scripting (leica_tiffs.zip)
Lesson 20 (JB): Introduction to NumPy and the SciPy stack
noon-1 pm
Lunch
1-4 pm
Lesson session 6
Lesson 21 (JB): NumPy arrays and operations with them (xa_high_food.csv, xa_low_food.csv)
Lesson 22 (JB): Introduction to Matplotlib (xa_high_food.csv, xa_low_food.csv)
Lesson 23 (JB): Random number generation
Lesson 24 (JB): Monte Carlo simulation
4:15-5:15 pm
Faculty lecture: Paul Sternberg, Small Worm, Big Data
5:15-7 pm
Dinner
7-10 pm
Exercise 3

Thursday, September 17

9 am - 11:20 am
Lesson session 7
Lesson 25 (JB): Review of exercise 3
Lesson 26 (JB): Python style (PEP 8)
Lesson 27 (JB): Version control with Git
11:20 am-12:20 pm
Lunch
12:20-4 pm
Lesson session 8
Lesson 28 (JB): Case study: Computing the Luria-Delbrück distribution
Lesson 29 (JB): Introduction to Pandas (xa_high_food.csv, xa_low_food.csv)
Lesson 30 (JB): Case study: extracting data of interest from frog tongue adhesion (frog_tongue_adhesion.csv)
Lesson 31 (SS): Introduction to BioPython
Lesson 32 (SS): Case study: scripting BLAST searches
4:15-5:15 pm
Faculty lecture: Mary Kennedy, A Set of Tools for Understanding Biochemical Networks: Computational Reconstitution of Cellular Functions
5:15-7 pm
Dinner
7-10 pm
Exercise 4 (grant.zip)

Friday, September 18

9 am - noon
Lesson session 9
Lesson 33 (JB/SS): Review of exercise 4
Lesson 34 (JB): Introduction to image processing (ecoli_images.zip)
Lesson 35 (JB): Case study: Basic image quantification (HG104_images.zip)
Lesson 36 (JB): Performing regressions (bcd_gradient.csv)
noon-1 pm
Lunch
1-1:40 pm
Lesson session 10a
Lesson 37 (JB): Algorithmic complexity: Finding the longest common subsequence
1:45-2:45 pm
Faculty lecture: Mitch Guttman, lncRNAs: Function and Mechanism in Controlling Cellular Identity
3:55 - 5:15 pm
Lesson session 10b
Lesson 38 (MG): Efficient genomic queries
Lessons 39 and 40 (MG): Quantifiying gene expression (slides)
5:15-7 pm
Dinner
7-10 pm
Exercise 5 (bacterial_growth.zip)

Saturday, September 19

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