Lessons

0. Setting up your computer
1. Intro to Python I
2. Intro to Python II
3. Exploratory data analysis
4. Data wrangling
5. Intro to image processing
6. Probability distributions
7. Nonparametric inference with hacker stats
8. Maximum likelihood estimation
9. Model assessment

Lecture notes

1. Cycle of science/Version control
2. Style and TDD
3. Data storage and sharing
4. Reproducible workflows
5. Intro to images
6. Intro to probability
7. Nonparameteric inference
8. Parametric inference
9. The paper of the future
10. Statistical watchouts

Homework

1. Practice with Python
2. File I/O and validation
3. Exploratory data analysis I
4. Exploratory data analysis II
5. Image processing
6. Random number generation and time series
7. Nonparametric hacker stats
8. Maximum likelihood estimation
9. Model comparison
10. Course feedback

Recitations

1. The command line
2. Using Git and GitHub
3. Package management
4. Reshaping data
5. Overplotting and dashboards
6. Time series and data smoothing
7. Colocalization
8. Bootstrapping
9. Pairs, residual, and wild bootstrap

BE/Bi 103 a

BE/Bi 103 a main page
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3. Data storage and sharing¶

Slides from Tom Morrell’s guest lecture on data storage and sharing best practices may be found here.

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Last updated on Dec 18, 2019.

© 2019 Justin Bois and BE/Bi 103 a course staff. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.

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