Following is a tentative course schedule, which is subject to change as the course progresses. Links to all tutorials will be posted when available. Lab sessions marked with an asterisk feature lecture material.
Weekly activities
- Lab
- Section 1: Mondays, 9:00 am-noon, 200 Broad
Section 2: Mondays, 1:00-4:00 pm, 200 Broad
Section 3: Mondays, 7:00-10:00 pm, 200 Broad - Lecture
- Section 1: Wednesdays, 9:00-9:50 am, 100 Broad
Section 2: Wednesdays, 10:00-10:50 am, 100 Broad - TA sessions
- Recitation: Thursdays, 7:00-8:30 pm, 100 Broad, except Thanksgiving
- Homework help: Thursdays, 8:30-10:00 pm, 200 Broad, except Thanksgiving; dial 626-395-5947 if you need to be let in the building.
- Instructor office hours
Wednesdays, 2:30-4:00 pm, 100 Broad, except Nov. 27
Homework due dates
- HW 1
- due 11:59 pm, Oct. 11
- HW 2
- due 11:59 pm, Oct. 18
- HW 3
- due 11:59 pm, Oct. 25
- HW 4
- due 11:59 pm, Nov. 1
- HW 5
- due 11:59 pm, Nov. 8
- HW 6
- due 11:59 pm, Nov. 15
- HW 7
- due 11:59 pm, Nov. 24
- HW 8
- due 11:59 pm, Dec. 6
- HW 9
- due 5 pm, Dec. 11
- HW 10
- due 5 pm, Dec. 13
Lesson and lecture schedule
- Lab 0
- (before first
lab) Setting up a Python distribution for scientific
computing
(before first lab) Introduction to Jupyter notebooks
(as needed) Introduction to LaTeX
- Lecture 1
- (W 10/02) Welcome / data pipelines / version control
- Lab 1
- (M 10/07) Basics of programming in Python I
- Lecture 2
- (W 10/09) Style and test-driven development
- Lab 2
- (M 10/14) Basics of programming in Python II
- Lecture 3
- (W 10/16) Good data storage and sharing practices (guest lecture by Tom Morrell)
- Lab 3
- (M 10/21) Exploratory data analysis I
- Lecture 4
- (W 10/23) Methods for conducting reproducible research (guest lecture by Griffin Chure)
- Lab 4
- (M 10/28) Exploratory data analysis II
- Lecture 5
- (W 10/30) Introduction to image processing
- Lab 5
- (M 11/04) Extracting data from images
- Lecture 6
- (W 11/06) Introduction to probability and statistical inference
- Lab 6
- (M 11/11) Probability distribution and random number generation
- Lecture 7
- (W 11/12) Confidence intervals, hypothesis testing, and nonparametric methods
- Lab 7
- (M 11/18) Nonparametric hacker stats
- Lecture 8
- (W 11/20) Parametric inference
- Lab 8
- (M 11/25) Parametric inference with hacker stats
- Lecture 9
- (W 11/27) Publishing an open paper (guest lecture by Griffin Chure)
- Lab 9
- (M 12/02) Mixture models and model comparison
- Lecture 10
- (W 12/04) Statistical watchouts / Course recap and wrap-up
Recitation lessons
- Rec 1
- (Th 10/03) Navigating the command line (SB)
- Rec 2
- (Th 10/10) Tips and traps for Git/GitHub (MM)
- Rec 3
- (Th 10/17) Package management (PA)
- Rec 4
- (Th 10/24) Tips for manipulating data frames (SS)
- Rec 5
- (Th 10/31) Dashboarding (CA) and handling over plotting (SB)
- Rec 6
- (Th 11/07) Time series analysis (JC)
- Rec 7
- (Th 11/14) Colocalization analysis of images (JC)
- Rec 8
- (Th 11/21) More on the bootstrap (CA)
- Rec 9
- (Th 12/05) Pairs, residual, and wild bootstrap (SB)