/datascibiol

Website for FRS417, Winter 2019

Primary LanguageHTML

Welcome!

This is the course page for FRS 417, Introduction to Data Science for Biologists at UC Davis.

Class Times and Contact Information

  • Fridays, 1:10-3:00p, 2103 SCC
  • Joel Ledford
  • Office: 2220 Life Sciences, Department of Plant Biology

Course Summary

In FRS 417, you will be introduced to the fundamentals of data science with emphasis on data frequently used by biologists. We will use the R software environment to develop and practice key skills including data management, transformation, analysis, and visualization. Practical examples will span a range of disciplines including social science, ecology, evolution, and genetics. Labs will use a problem-solving approach where students build on previously learned skills culminating in a small, group-based project presented at the end of the quarter.

This class is designed for students with no background in computer programming, R, or statistics. My assumption is that you know how to turn a computer on and use a mouse- that's it!

Please read the course syllabus here.

Lab Schedule and Homework

We have eight total labs. The last two labs (9 and 10) will be devoted to getting final projects complete and preparing presentations.

  1. Lab 1 | part 1 | part 2 | HW 1
  2. Lab 2 | part 1 | part 2 | HW 2
  3. Lab 3 | part 1 | part 2 | HW 3
  4. Lab 4 | part 1 | part 2 | HW 4
  5. Lab 5 | part 1 | part 2 | HW 5
  6. Lab 6 | part 1 | part 2 | HW 6
  7. Lab 7 | part 1 | part 2 | HW 7
  8. Lab 8 | part 1 | part 2 | HW 8

Reading

R for Data Science, Grolemund and Wickham (2017).

Course Learning Goals

At the end of this course, you will be able to:

  1. Use R and RStudio to perform basic analyses including arithmetic and basic statistics.
  2. Work with multiple types of data in R and RStudio including vectors, data frames, and nucleotide sequences.
  3. Import and transform complex, messy data for analysis in R using dplyr, tidyr, and purrr.
  4. Produce a variety of plots and charts to visualize results of data analysis using ggplot2.
  5. Build an exploratory analysis pipeline that can be applied to a variety of data types and structures.
  6. Use Shiny to build an interactive application.
  7. Use geospacial data to produce distribution maps.

Let's Get Started lab 1