ECOL_596W_2024

Practical and Reproducible Data Science for EEB

Fall 2024

Professor: Dr. Sabrina McNew (she/her/ella)

Email: [email protected]
Office: BSW 326
Office Hours: By appointment

Course Information and Communication:

1. The course website is https://github.com/smcnew/ECOL_596W_2024.
2. If you have questions or concerns feel free to email the instructor ([email protected]).
3. Tuesdays will be mostly lecture, Thursdays will be mostly in-class practice. Bring a computer with R and R studio to Thursdays, and some Tuesdays TBD.

Course Description

Data management and analysis skills are essential for graduate students in the biological sciences. However, these skills can be challenging to learn because they sit at the intersection of three fields: biology, math, and computer science. Here we will pull from each of these disciplines to build your toolkit as a researcher. In this course, we will develop general best analytical practices to ensure that your data are safely stored, that your results are reproducible, and that your research leads to meaningful insights. We will not cover every statistical approach that you may need to complete your graduate degree, and we will not deeply delve into the math behind most analyses. However, by the time you leave the course you should have a foundation that will help you tackle many common types of data analyses, and you should have the confidence to gain new skills to answer your specific research questions.

General Learning Outcomes:

By the end of the course you should be able to:

• Manage your data: You will learn how to “wrangle” your data into an analysis-ready format. You will learn best practices for storing and archiving your data to ensure reproducibility and easy collaboration with colleagues and future you.
• Visualize your results: Plot your data early and often. Visualizing your data is an important first step when organizing your analysis and sharing results with collaborators.
• Use common statistical approaches: Know what kind of data you have and what family of statistical approaches to use. You will gain a strong foundation in linear regression and its variations. You will also become acquainted with other techniques including PCA, Bayesian analysis, and more.
• Learn to google: Professional scientists face new data challenges with every project. Your goal is not to know every statistical approach but rather how to gain skills to tackle new problems.

Classroom Philosophy:

I’m excited you want to learn data science! This class is here to serve you as you work towards your PhD. As a group, our goal is to ensure that everyone feels included and free to engage fully with the content. We welcome:

• Diverse identities and backgrounds – all members of this class deserve to be here. We recognize and welcome the fact that our identities and experiences shape our perspectives and what we contribute to the classroom.
• Respectful interactions – that show value for others’ opinions, identities, and time.
• Commitment to shared learning – recognizing that we each have different ways of demonstrating what we learn.

Prerequisites:

This course will be taught in R with R Studio. R is currently the predominant statistical software in academic biology. Prior knowledge of R is not a prerequisite; however, there is a learning curve to the language and the more comfortable you are with the code the easier this class will be.

Course texts/sources:

Irizarry, Introduction to Data Science: Data Analysis and Prediction Algorithms with R
Whitlock and Schluter, The Analysis of Biological Data
McElreath, Statistical Rethinking

Prior to the first week of class:

1. Download R and R Studio. If you need detailed instructions click here.
   1A. Already downloaded but it’s been a while? Time to update R and R studio
   https://www.r-bloggers.com/2022/01/how-to-install-and-update-r-and-rstudio/
2. If you are totally new to the R language please try one or both of these tutorials to get acquainted with how it works:
   https://swirlstats.com/
   https://moderndive.com/1-getting-started.html#nycflights13

How do I actually download stuff from Github?

We'll get to working with Git in Week 3. Until then here are two methods:

1. Navigate to the file in the github repo. In the top right corner there is a a little download icon underneath the word "History"
2. Download a whole directory using its url here https://download-directory.github.io/

Github resources:

https://happygitwithr.com/

Download the full syllabus here

This is Natalie Morris!!! How I feel right now: https://xkcd.com/1597/ Please work :'(