If you have useful links to share, please post them on The Whiteboard and I will add them here.
MSU Courses:
Statistics
- Statistics for Biologists (STT464): This course is a step1 for individuals with no background in statistics. The course covers the basics of the importance of statistical analysis, experimental design, ttests, ANOVAs, chi-squared tests and the like. I found this course particularly helpful when it came to the broad introduction. There is a BRIEF introduction to R/Rstudio. It is setup very much like an undergrad course in statistics would be but is ~90% graduate students. ~ information from Shawna Rowe
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Advanced Statistics for Biologists (STT814): This course is largely an introduction to experimental design and SAS. The course runs on the assumption that 464 was completed previously, but doesn’t require many tedious by hand calculations and (in my opinion) serves as a sufficient introduction to many statistical methods and covers more detailed types of tests that may be useful. The caveat is that it is done THROUGH the use of SAS, which is often times frustrating since many have moved to R (rightfully so, in my opinion). ~ information from Shawna Rowe
Computer Science
- Computer Science for Evolutionary Biologists (CSE801): Don’t be fooled by the course name! This is a small class that is required for students receiving a particular type of BEACON funding. Evolution is mentioned and appreciated but not the focus of the course in the slightest. It is able to be audited and served as a phenomenal introduction to python and the usefulness of knowing a language. The class is very small and the students sit around and learn python as a group— taught by a computer scientist (often times one that studies artificial intelligence or evolution using digital organisms and the like). The end project requires the development of some sort of model or writing a program that is capable of performing a tedious task (i.e. maze generation and solving or automated sequence alignments). Those who audit the course do not complete the final project and can skedaddle with fresh python skills and no additional gray hairs sprouted as a consequence of completing the final project. ~ information from Shawna Rowe
Tutorials:
UNIX
Python
R
- Cyclismo R tutorial
- An R Introduction to Statistics
- Quick-R
- Introduction to using R: The Statistical Programming Language
- Code School: R
Resources:
Statistics
- New! MSU CANR Biometry Group Statistical Consulting Center – Get expert help on your experimental design and data analysis. This resource was shared by TPC member, Amy Baetsen-Young, who reported getting great help from Chun-Lung Lee at the Consulting Center.
- Points of Significance (Nature)
