Lessons

0. Preparing computing resources for the course
1. The cycle of science
2. Version control with Git
3. Introduction to Python
4. Style
5. Test-driven development
6. Exploratory data analysis, part 1
E1. To be completed after lesson 6
7. Exploratory data analysis, part 2
8. Data file formats
9. Data storage and sharing
10. Data wrangling
E2. To be completed after lesson 10
11. Intro to probability
12. Random number generation
13. Probability distributions
E3. To be completed after lesson 13
14. Plug-in estimates and confidence intervals
15. Nonparametric inference with hacker stats
E4. To be completed after lesson 15
16. Null hypothesis significance testing
17. Hacker’s approach to NHST
E5. To be completed after lesson 17
18. Parametric inference
19. Numerical MLE
E6. To be completed after lesson 19
20. Variate-covariate modeling
21. Confidence intervals of MLEs
E7. To be completed after lesson 21
22. Implementation of MLE for variate-covariate models
E8. To be completed after lesson 22
23. Mixture models
24. Model assessment
25. Implementation of model assessment
E9. To be completed after lesson 27
26. Statistical watchouts
27. Reproducible workflows
28. The paper of the future

Recitations

R1. The command line and Git
R2. Extra help for new programmers
R3. Manipulating data frames
R4. Probability review
R5. Overplotting
R6. Dashboards
R7. Review of maximum likelihood estimation
R8. Topics in bootstrapping

Homework

1. Practice with Python tools and EDA I
2. Exploratory data analysis II
3. Wrangling, EDA III, and probability distributions
4. Working with probability distributions
5. Nonparametric hacker stats and Boolean data
6. Maximum likelihood estimation I
7. Maximum likelihood estimation II
8. Maximum likelihood estimation III
9. Model assessment
10. Course feedback

Schedule

Schedule overview
Homework due dates
Lesson exercise due dates
Weekly schedule

Policies

Meetings
Lab sessions
Submission of assignments
Lessons and lesson exercises
Homework
Grading
Collaboration policy and Honor Code
Excused absences and extensions
Course communications
“Ediquette”

BE/Bi 103 a

9. Data storage and sharing
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9. Data storage and sharing

Tom Morrell gave a guest lecture on data storage and sharing best practices. You can view the slides at this link: https://doi.org/10.5281/zenodo.8404493.

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Last updated on Nov 29, 2023.

© 2022 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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