Syllabus

Course

Data Science for Eco/Evo

BIOE215, 3 Credits, Fall 2023

Instructor

Dr. Max Czapanskiy (he/they)

Email: mczapans@ucsc.edu

Location

CoastBio 115

Times

Mondays, 3:30-5:30 pm

Office Hours

Times: Thursdays 3:30-5:30 pm

Location: CoastBio Otter Conference Room

Or by appointment.

Course Description

This course will aim to be practical and provide structure for learning the computational skills most eco/evo grad students have to pick up on their own. Topics will include programming best practices in R, organizing data and computational projects, and planning for reproducibility. This is a chance to gain knowledge and experience with the nuts and bolts of making your science work in code, without trying to learn generalized linear models at the same time. The course structure will be self-directed for weeks 1-6, followed by a final project in weeks 7-10.

Schedule

Week 1: Computational project organization

Week 2: Computational thinking I

Week 3: Working with data

Week 4: Computational thinking II

Week 5: Troubleshooting

Week 6: Wrap up self-directed portion of class

Weeks 7-9: Final projects

Week 10: Communities of practice

How class works

The class format is self-paced with an emphasis on small-group learning. Each class will begin with a short discussion on the assigned readings, then we’ll break out into groups. I will require everyone to complete some lessons start to finish (for example, the project organization lesson). Other lessons will let you jump in at the point appropriate to your level of experience.

After week 6, class time will be devoted to your final projects. These will be replication studies. You and your group will find a paper that you want to replicate some part of, either a figure or a table. Using the skills you’ve learned in this class, you will create a stand-alone analysis that replicates their work. I will encourage all groups to submit their replication to an appropriate journal such as Re-Science C.

This class covers computational thinking, programming in base R and the tidyverse, project organization and management, and open science practices. You all probably have very different levels of preparation in these areas. For example, if you have more base R programming experience, I’ll suggest you tackle advanced lessons like functional programming. If you feel your foundation is a little patchy, I’ll suggest you deepen your understanding of the core data structures. This means most students will complete somewhat distinct subsets of the available material. That’s by design! You’re not expected to do the same lessons as everyone else. You are expected to do the lessons that best support your growth as a scientist. Speaking of expectations…

Course Expectations

This course offers more material than can be covered in the time allocated and students should not expect to complete all of it! Biology grad students have diverse backgrounds, training, and experience with computational skills. Some of the available material will be too basic or too advanced for you at this time. With help from the instructor, you will identify what content will contribute most to your academic and professional success.

What you can expect from your instructor

The instructor is here to support your learning and provide you with guidance as you develop your understanding of and skills in data science. I strive for an inclusive and collaborative classroom and welcome any suggestions for improvement. I will do my best to provide useful content, rapid feedback, and enthusiastic support, so let me know if I can do anything more. I highly encourage everyone to visit me in office hours or to set up a meeting, even if you don’t feel that you have questions. I want to get to know you and support you in this learning experience!

What your instructor expects from you

I expect you to attend class regularly, complete the self-guided material at the pace that works best for you, and contribute to your group’s final project. Your learning depends on your own preparation and engagement as well as that of your fellow students. I encourage you to rely on each other, as well as the instructor, for problem solving and finding solutions. Teaching reinforces learning, so after you complete lessons be prepared to assist other students with that material. Each member of this class has different ideas and perspectives that will enrich the experience for everyone else, so I ask you to be respectful and thoughtful in your interactions. To get the most out of the class, you should be prepared to share your ideas, ask questions, listen actively and collaborate effectively during small group work. Never hesitate to email your instructors, stop by office hours, or set up a meeting. This class should challenge you, but I believe everyone has the ability to succeed with some effort.

Why isn’t there a remote option?

Remote options have become standard for most classes and I wish I could offer one for this course. However, the structure of the class does not lend itself to hybrid formats because of the emphasis on peer learning and 1-on-1 coaching from the instructor. In the future, I hope to offer online and in-person versions of the course. For now, I only have resources to support the in-person version.

For the same reason, I can’t support auditing the class at this time. However, I am happy to share the educational materials with anyone who’s interested. I will try to connect these students so you can form a study group. If that happens, I will add additional office hours to meet with you.