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Lessons

  • 0. Setting up your computer
  • 1. Intro to Python I
  • 2. Intro to Python II
  • 3. Exploratory data analysis
  • 4. Data wrangling
  • 5. Intro to image processing
  • 6. Probability distributions
  • 7. Nonparametric inference with hacker stats
  • 8. Maximum likelihood estimation
  • 9. Model assessment

Lecture notes

  • 1. Cycle of science/Version control
    • Lecture 1a: Welcome to BE/Bi 103 a
    • Lecture 1b: Version control with Git
  • 2. Style and TDD
  • 3. Data storage and sharing
  • 4. Reproducible workflows
  • 5. Intro to images
  • 6. Intro to probability
  • 7. Nonparameteric inference
  • 8. Parametric inference
  • 9. The paper of the future
  • 10. Statistical watchouts

Homework

  • 1. Practice with Python
  • 2. File I/O and validation
  • 3. Exploratory data analysis I
  • 4. Exploratory data analysis II
  • 5. Image processing
  • 6. Random number generation and time series
  • 7. Nonparametric hacker stats
  • 8. Maximum likelihood estimation
  • 9. Model comparison
  • 10. Course feedback

Recitations

  • 1. The command line
  • 2. Using Git and GitHub
  • 3. Package management
  • 4. Reshaping data
  • 5. Overplotting and dashboards
  • 6. Time series and data smoothing
  • 7. Colocalization
  • 8. Bootstrapping
  • 9. Pairs, residual, and wild bootstrap
BE/Bi 103 a
  • BE/Bi 103 a main page
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1. Cycle of science/Version control¶

  • Lecture 1a: Welcome to BE/Bi 103 a
  • Lecture 1b: Version control with Git
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Last updated on Dec 18, 2019.

© 2019 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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