Course Details

  • This class will meet in-person (Lectures on Tuesdays from 5:30 – 6:45 p.m.; lab on Thursdays from 5:30 – 7:50 p.m.)
  • 8 weeks | May 28 to July 19, 2024
  • 3 credits | $1,875
  • Last day to register: May 21, 2024
  • Prerequisites: BIO 101 or BIO 102

Course Overview

Computers are essential for many aspects of biology. Basic programming is required for everything from accessing and managing data to statistical analysis and modeling. This course will provide an introduction to data management, manipulation, and analysis, with an emphasis on biological problems. The course will be taught using R, but the concepts learned will easily apply to other programming languages that are common in the field of Bioinformatics.

Course Advantages

Regardless of your academic specialty, computer literacy is both extremely useful and highly sought after in virtually all career paths. Data science in particular is valuable as it sits at the intersection of computer skills and some other subject area. Data scientists then are a vital component of any discipline-specific work as they empower teams to rigorously and reproducibly uncover patterns and test hypotheses. This course aims to give you a primer on data management best practices, analytical processes, and reproducible coding strategies. There will be opportunities to practice communication of technical concepts to a non-specialist audience. Students will complete this course with a public-facing portfolio of the coding products they produce and will get advice on how best to leverage this in job or graduate school applications.

Additional Information

Faculty will contact all students after the Tuesday, May 21, registration deadline.

About the Instructor

Nicholas Lyon

Nick Lyon started their career as a community ecologist specializing in pollinator and flowering plant communities and went on to study insect pest and predator communities on organic farms and their interactions with local weed communities. While doing this work, they emphasized strengthening data management and coding skills due to the complexity inherent in ecological data. These data science qualifications led to collaborations with other scientists that allowed Nick to focus exclusively on the data side of exciting research questions. Nick then pivoted from a career as an ecologist to one as a data scientist and continues to leverage their background as a broadly-trained ecologist in their current role as a Data Analyst for the Long Term Ecological Research (LTER) Network Office. Nick aims to help other early career ecologists gain confidence with data science tools by designing intuitive workshops and pedagogical materials.

Questions? Contact Us

Duffy Academic Center – 112

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