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. Reproducible workflows
23. The paper of the future
24. Implementation of MLE for variate-covariate models
E8. To be completed after lesson 24
25. Mixture models
26. Model assessment
27. Implementation of model assessment
E9. To be completed after lesson 27
28. Statistical watchouts

Recitations

R1. The command line and Git
R2. Intro to image processing
R3. Manipulating data frames
R4. Probability review
R5. Overplotting
R6. Dashboards
R7. Topics in bootstrapping
R8. Review of maximum likelihood estimation
R9. Best Practices when using the Resnick High Performance Computing Center and other related topics

Homework

0. Configuring your team
1. Practice with Python tools and EDA I
2. Exploratory data analysis II
3. Wrangling, EDA III, and Normal approximations
4. Working with probability distributions
5. Nonparametric hacker stats
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
Lessons and lesson exercises
The BE/Bi 103 GitHub group
Homework
Grading
Collaboration policy and Honor Code
Excused absences and extensions
Course communications
“Ediquette”

BE/Bi 103 a

Archive of the Fall 2022 edition

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9. Data storage and sharing

Kristin Briney gave a guest lecture on data storage and sharing best practices. The slides may be found here.

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Last updated on Sep 22, 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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