Course Overview

Course Outline

Modules

Assignments

Policies

Insurmountable Coding Problems

Use These Resources to Hone Your Data Science Skills



Software Tools

Programming Languages

  • R (MacOS, Windows, Linux)
  • Python (MacOS, Windows, Linux)

Text Editors

Markdown Editors

IDEs

  • RStudio for R (current version is 1.0.136) (MacOS, Windows)
  • y-hat Rodeo for Python (current version is 2.4.10) (MacOS, Windows, Linux)

Version Control Tools

Web Resources

Books

Statistical Modeling in Biology

  • Bolker BM. 2008. Ecological Models and Data in R. Princeton, NJ: Princeton University Press.
  • Irizarry RA, Love MI. 2015. Data Analysis for the Life Sciences. Lean Publishing.
  • Quinn GP, Keough MJ. 2002. Experimental Design and Data Analysis for Biologists. Cambridge, UK: Cambridge University Press.

R and Basic Statistics

  • Caffo B. 2015. Statistical Inference for Data Science. Lean Publishing.
  • Caffo B. 2016. Regression Models for Data Science in R. Lean Publishing.
  • Dalgaard P. 2008. Introductory Statistics with R (Second Edition). New York: Springer.
  • Navarro DJ. 2024. Learning Statistics with R (v. 0.6). CC BY-SA 4.0
  • Shahbaba B. 2012. Biostatistics with R. New York: Springer.
  • Thulin M. 2024. Modern Statistics with R (Second Edition). New York: Springer.

R Programming

  • Adler J. 2009. R in a Nutshell. Sebastopol, CA: O’Reilly Media.
  • Kabacoff RI. 2011. R in Action: Data Analysis and Graphics with R. Shelter Island, NY: Manning Publications Co.
  • Matloff N. 2011. The Art of R Programming: A Tour of Statistical Software Design. San Francisco: No Starch Press.
  • Peng R. 2016. R Programming for Data Science. Lean Publishing.
  • Peng R. 2016. Exploratory Data Analysis with R. Lean Publishing.
  • Zuur AF, Ieno EN, Meesters EHWG. 2009. A Beginner’s Guide to R. New York: Springer.

R Reference

  • Crawley MJ. 2012. The R Book (Second Edition). Chichester, UK: John Wiley & Sons Ltd.
  • Gardener M. 2012. The Essential R Reference. Indianapolis, IN: John Wiley & Sons, Inc.
  • Teetor P. 2011. R Cookbook. Sebastopol, CA: O’Reilly Media.

R Graphics

  • Chang W. 2013. R Graphics Cookbook. Sebastopol, CA: O’Reilly Media.
  • Wickham H. 2016. ggplot2: Elegant Graphics for Data Analysis (Second Edition). Springer International Publishing.

Data Science and Visualization

  • Dale K. 2016. Data Visualization with Python and JavaScript. Sebastopol, CA: O’Reilly Media.
  • Grus J. 2015. Data Science from Scratch. Sebastopol, CA: O’Reilly.
  • Kazil J, Jarmul K. 2016. Data Wrangling with Python. Sebastopol, CA: O’Reilly.
  • Peng RD, Matsui E. 2015. The Art of Data Science. Lean Publishing.
  • Zumel N, Mount J. 2014. Practical Data Science with R. Shelter Island, NY: Manning Publications Co.

Spatial Analysis

  • Bivand RS, Pebesma E, Gómez-Rubio V. 2013. Applied Spatial Data Analysis with R (Second Edition). New York: Springer.
  • Brundson C, Comber L. 2015. An Introduction to R for Spatial Analysis and Mapping. London: SAGE Publications, Ltd.

Mixed Effects Models

  • Garamszegi LZ. 2014. Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Berlin: Springer.
  • Pinheiro JC, Bates DM. 2000. Mixed-Effects Models in S and S-Plus. New York: Springer.
  • Zuur AF, Ieno EN, Walker NJ, Savaliev AA, Smith GM. 2009. Mixed Effects Models and Extensions in Ecology with R. New York: Springer.

Web Scraping and Text Mining

  • Friedl JEF. 2000. Mastering Regular Expressions (Third Edition). Sebastopol, CA: O’Reilly Media.
  • Mitchell R. 2015. Web Scraping with Python. Sebastopol, CA: O’Reilly Media.
  • Nolan D, Temple Lang D. 2014. XML and Web Technologies for Data Sciences with R. New York: Springer.

Statistics and Programming in Python

  • Downey A. 2014. Think Stats (Second Edition). Sebastopol, CA: O’Reilly Media.
  • Lubanovic B. 2014. Introducing Python. Sebastopol, CA: O’Reilly Media.
  • McKinney W. 2013. Python for Data Analysis. Sebastopol, CA: O’Reilly.