Software

These might be of interest (for prerequisites, see the syllabus):

• scikit-learn
• statsmodels
• patsy for specifying (linear) models in python
• jupyterhub troubleshooting
• matplotlib - a common way to make plots in python
• seaborn - another way to make plots in python
• plotnine - yet another way to make plots in python
• scipy - many fundamental algorithms (e.g., for optimization)

Other books

If you are looking for more reference or reading material, these are also good. These are in no particular order; browse and find one the speaks to you.

• Python for Data Analysis, by Wes McKinney, free online, covering “the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python”.

• Probability for Data Science, by Ani Adhikari and Jim Pitman, free online.

• Theory meets data, by Ani Adhikari.

• Probability, by Pitman, free ebook via the UO library website.

• Introduction to Probability, by Blitzstein and Hwang.

• Learning Data Science, by Sam Lau, Joey Gonzalez, and Deb Nolan.

• Introduction to Data Science, by Rafael A. Irizarry. Subtitle “Data Analysis and Prediction Algorithms with R”.

• An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani. A less technical (i.e., math-heavy) and more applied treatment of the topics in Elements of Statistical Learning.

• The Elements of Statistical Learning, by Trevor Hastie, Rob Tibshirani, and Jerome Friedman.

• Introduction to Probability for Data Science, by Stanley H. Chan.

• All of Statistics, by Larry Wasserman. Technical, a book on statistical theory.

• Introductory Statistics, by Shafer & Zhang.

• R for Data Science, by Wickham, H. & G. Grolemund. 2016. O’Reilly Publishers. How to do many common data analysis tasks in R, specifically in the tidyverse.

• Fundamentals of Data Visualization, by Wilke, Claus O. O’Reilly Publishers. How to think about visualization (with source code for plots available!).

Other courses:

• Probability for Data Science at UC Berkeley
• Data 88 at UC Berkeley
• Data 100 at UC Berkeley
  • pandas quick reference
  • slides, intro to pandas
• Data 8 at UC Berkeley
  • book: Computational and Inferential Thinking: The Foundations of Data Science, i.e., “Data 8” at UC Berkeley by Ani Adhikari, John DeNero, and David Wagner.

General resources

• linuxcommand.org and bashguide
• Software Carpentry
• Reproducible Research by Karl Broman: talk and course
• Karl Broman’s excellent short tutorials on rmarkdown, git/github, make, perl, and more.
• a visual introduction to git

Probability and statistics

• List of common probability distributions
• Interactive plot of the beta distribution
• Interactive plot of the gamma distribution
• Interactive plot of Student’s t-distribution