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. 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)
Canvas (used for assignment submission/return)
Homework solutions (password protected)
People
Instructor
Justin Bois (bois at caltech dot edu)
TAs
Anwesha Das (
Anwesha.Das AT caltech DOT edu)Kevin Le (
kvnjmle AT caltech DOT edu)Anastasiya Oguienko (
oguienko AT caltech DOT edu)Hunter Richards (
hrichards AT caltech DOT edu)
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
- 7. Exploratory data analysis, part 2
- 8. Data file formats
- 9. Data storage and sharing
- 10. Data wrangling
- E1. To be completed after lesson 10
- 11. Intro to probability
- 12. Random number generation
- 13. Probability distributions
- E2. To be completed after lesson 13
- 14. Plug-in estimates and confidence intervals
- 15. Nonparametric inference with hacker stats
- E3. To be completed after lesson 15
- 16. Null hypothesis significance testing
- 17. Hacker’s approach to NHST
- 18. Parametric inference
- 19. Numerical MLE
- E4. To be completed after lesson 19
- 20. Variate-covariate modeling
- 21. Confidence intervals of MLEs
- 22. Implementation of MLE for variate-covariate models
- E5. To be completed after lesson 22
- 23. Mixture models
- 24. Model assessment
- 25. Implementation of model assessment
- E6. To be completed after lesson 25
- 26. Statistical watchouts
- 27. Reproducible workflows
- 28. The paper of the future
Homework
- 1. Practice with Python tools and EDA I
- 2. Exploratory data analysis II
- 3. Wrangling, EDA III, and probability distributions
- 4. Working with probability distributions
- 5. Nonparametric hacker stats
- 6. Maximum likelihood estimation I
- 7. Maximum likelihood estimation II
- 8. Maximum likelihood estimation III
- 9. Model assessment
- 10. Maximum likelihood estimation IV