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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
  • 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
    • Homework 1.1: Plotting an elephant (35 pts)
    • Homework 1.2: Palmer penguins and split-apply-combine (30 pts)
    • Homework 1.3: Microtubule catastrophe and ECDFs (team problem, 35 pts)
  • 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
    Archive of the Fall 2023 edition
  • View page source

1. Practice with Python tools and EDA I

  • Homework 1.1: Plotting an elephant (35 pts)
  • Homework 1.2: Palmer penguins and split-apply-combine (30 pts)
  • Homework 1.3: Microtubule catastrophe and ECDFs (team problem, 35 pts)
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Last updated on Sep 19, 2024.

© 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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