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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”
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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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