Following is a tentative course schedule, which is subject to change as the course progresses. Links to all tutorials will be posted when available.

Note that the Tuesday lab sessions are in 102 Spaulding on 09/29, 10/27, and 11/24.

Weekly activities

Lab: Section 1: Mondays, 1:00-4:00 pm, 328 SFL
Section 2: Mondays, 7:00-10:00 pm, 328 SFL
Section 3: Tuesdays, 9:00 am-noon, 328 SFL or 102 Spaudling
Lecture: Wednesdays, 10:00-10:50 am, 100 Broad
Recitation (optional): Thursdays, either 7:00-8:30 pm or 8:30-10:00 pm, 328 SFL
TA OHs: Thursdays, either 7:00-8:30 pm or 8:30-10:00 pm, 328 SFL

Sundays, 1:00-2:30 pm, 328 SFL
JB's OHs: Fridays, 2:30-4:00 pm, 100 Broad

Tutorial and lecture schedule

Lab 0: (before first class) Setting up a Python distribution for scientific computing

(before first homework) Introduction to Jupyter notebooks

(before first homework) Introduction to LaTeX

(before first homework) A sample homework problem and solution
Lab 1: (M 09/28, Tu 09/30) Introduction to Python / Exploratory data analysis (data set)
Lecture 1: (W 09/30) Bayes's theorem and the logic of science
Lab 2: (M 10/05, Tu 10/06) Managing data sets / Defining parameters and estimating them (data set)
Lecture 2: (W 10/07) Parameter estimation
Lab 3: (M 10/12, Tu 10/13) Regression (data set) / Boolean data (data set)
Lecture 3: (W 10/14) Probability distributions and their meanings
Lab 4: (M 10/19, Tu 10/20) Parameter estimation and MLE (data set) / Parameter estimation with MCMC
Lecture 4: (W 10/21) Model selection
Lab 5: (M 10/26, Tu 10/27) Model selection I (data set 1 / data set 2) / Outlier detection (data set)
Lecture 5: (W 10/28) Theoretical basis of Markov chain Monte Carlo
Lab 6: (M 11/02, Tu 11/03) Frequentist parameter estimation / Frequentist hypothesis testing (data set 1 / data set2)
Lecture 6: (W 11/04) Credible regions / Parallel tempering MCMC
Lab 7: (M 11/09, Tu 11/10) Time series and data smoothing / Model selection II (data set 1 / data set 2)
Lecture 7: (W 11/11) Introduction to images
Lab 8: (M 11/16, Tu 11/17) Extracting information from images (data sent via Dropbox)
Lecture 8: (W 11/18) Hierarchical models (Jupyter notebook)
Lab 9: (M 11/23, Tu 11/24) Basic filtering and thresholding / Segmentation (data sent via Dropbox)
Lecture 9: (W 11/25) Guest speakers Titus Brown and Tracy Teal: Good data storage and sharing practices / Data validation
Lab 10: (M 11/30, Tu 12/01) Colocalization (data set)
Lecture 10: (W 12/03) Course recap and wrap-up

Auxiliary recitation schedule (tentative)

Recitations are on Thursdays in 328 SFL. They cover auxiliary topics and are completely optional. The schedule below is tentative and may change based on student and instructor/TA interest.

Rec 1: (Th 10/08) Interactive data analysis with Bokeh (JB)
Rec 2: (Th 10/15) Review of concepts in probability (MR)
Rec 3: (Th 10/22) Kernel density estimation (MM)
Rec 4: (Th 10/29) LASSO and ridge regression (JB) (data set)
Rec 5: (Th 11/05) Principle component analysis (MR) (data set)
Rec 6: (Th 11/12) Introduction to SymPy (MR)
Rec 7: (Th 11/19) Intro to PyMC3 (JB)
Rec 8: (Th 12/03) Advanced segmentation: watershed algorithms and cell lineage tracking (GC)
Rec 9: (Th 12/10) Introduction to R (Axel Müller)