Logo

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
    • Exploratory data analysis
    • Introduction to Numpy and Scipy
    • Introduction to data frames
    • Tidy data and split-apply-combine
    • Introduction to plotting with Bokeh
    • Plotting smooth curves
    • Plots with categorical axes with Bokeh
    • Introduction to high-level plotting with HoloViews
    • Categorical axes and HoloViews
    • Visualizing distributions
    • iqplot
    • Exercise
  • 7. Data storage and sharing
  • 8. Data wrangling
  • 9. Intro to probability
  • 10. Overplotting
  • 11. Dashboards
  • 12. Plug-in estimates and confidence intervals
  • 13. Random number generation
  • 14. Probability distributions
  • 15. Null hypothesis significance testing
  • 16. Nonparametric inference with hacker stats
  • 17. Parametric inference
  • 18. Maximum likelihood estimation
  • 19. Model assessment
  • 20. Regression
  • 21. Reproducible workflows
  • 22. The paper of the future
  • 23. Mixture models
  • 24. Implementation of model assessment
  • 25. Statistical watchouts

Recitations

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

Homework

  • 0. Configuring your team
  • 1. Practice with Python
  • 2. Exploratory data analysis I
  • 3. Exploratory data analysis II
  • 4. Dashboards
  • 5. Random number generation and probability distributions
  • 6. Nonparametric hacker stats
  • 7. Parametric inference
  • 8. Maximum likelihood estimation
  • 9. Model comparison
  • 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
BE/Bi 103 a
  • »
  • 6. Exploratory data analysis
  • View page source

6. Exploratory data analysis¶

  • Exploratory data analysis
  • Introduction to Numpy and Scipy
  • Introduction to data frames
  • Tidy data and split-apply-combine
  • Introduction to plotting with Bokeh
  • Plotting smooth curves
  • Plots with categorical axes with Bokeh
  • Introduction to high-level plotting with HoloViews
  • Categorical axes and HoloViews
  • Visualizing distributions
  • iqplot
  • Exercise
Next Previous

Last updated on Aug 29, 2021.

© 2020 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.



Built with Sphinx using a theme provided by Read the Docs.