Lessons

0. Preparing computing resources for the course
1. The cycle of science
2. Version control with Git
3. Introduction to Python
E1. To be completed after lesson 3
4. Style
5. Test-driven development
6. Exploratory data analysis, part 1
E2. To be completed after lesson 6
7. Exploratory data analysis, part 2
E3. To be completed after lesson 7
8. Data file formats
9. Data storage and sharing
10. Data wrangling
E4. To be completed after lesson 10
11. Intro to probability
E5. To be completed after lesson 11
12. Overplotting
13. Dashboards
14. Plug-in estimates and confidence intervals
15. Random number generation
16. Probability distributions
E6. To be completed after lesson 16
17. Null hypothesis significance testing
18. Nonparametric inference with hacker stats
E7. To be completed after lesson 18
19. Parametric inference
20. Maximum likelihood estimation
E8. To be completed after lesson 20
21. Model assessment
22. Regression
E9. To be completed after lesson 22
23. Reproducible workflows
24. The paper of the future
25. Mixture models
26. Implementation of model assessment
E10. To be completed after lesson 26
27. Statistical watchouts

Recitations

R1. The command line
R2. Git/Github tips and traps
R3. Time series and data smoothing
R4. Manipulating data frames
R5. Probability review
R6. Intro to image processing
R7. Topics in bootstrapping
R8. Review of maximum likelihood estimation
R9. Wild and residual bootstrap
R10. Packaging and package management

Homework

0. Configuring your team
1. Practice with Python
2. Practice with Numpy and plotting
3. Exploratory data analysis I
4. Exploratory data analysis II
5. Dashboards
6. Random number generation and probability distributions
7. Nonparametric hacker stats
8. Parametric inference
9. Maximum likelihood estimation
10. Model comparison
11. 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

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

Tom Morrell will give a guest lecture on data storage and sharing best practices. The slides may be found here.

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Last updated on Dec 02, 2021.

© 2021 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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