# BE/Bi 103 a: Introduction to Data Analysis in the Biological Sciences¶

Modern biology is a quantitative science, and biological scientists need to be equipped with tools to analyze quantitative data. This course takes a hands-on approach to developing these tools. Together, we will analyze real data. We will learn how to organize, preserve, and share data sets, create informative interactive graphical displays of data, process images to extract actionable data, and perform basic resampling-based statistical inferences.

Importantly, biological data is often “messy” and there is no one right way to perform an analysis or make a plot. As we work with data, we will discuss various approaches to get a feel for the art of biological data analysis.

The sequel to this course goes deeper into statistical modeling, mostly from a Bayesian perspective. This course is foundational for that and further studies in analysis of biological data.

If you are enrolled in the course, please read the Course policies below. We will not go over them in detail in class, and it is your responsibility to understand them.

## Useful links¶

Ed (used for course communications)

Course Zoom link (password protected)

Video recordings (password protected)

Google doc for help queue (password protected)

Homework solutions (password protected)

During lab and homework help sessions, we will break out into different Zoom sessions headed by various course staff members. Their individual Zoom links are accessible below.

## People¶

Instructor

Justin Bois (bois at caltech dot edu, Zoom)

TAs

- 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
- 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

- 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