Investigating evolutionary relationships through cluster analysis: A teaching science with big data workshop session

Biochemistry is a data‐heavy discipline, yet teaching students to work with large datasets is absent from many undergraduate Biochemistry programs. Ensuring that future generations of students arevbv confident in tackling problems using big data first requires that educators become comfortable teach...

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Bibliographic Details
Published in:Biochemistry and molecular biology education 2022-09, Vol.50 (5), p.440-445
Main Author: Novak, Walter R.P.
Format: Article
Language:English
Subjects:
Big Data
Biochemistry
biochemistry and molecular biology
Cluster analysis
conference report
Conferences
Data
education
Evolutionary biology
evolutionary relationships
Programming Languages
Science education
Science Instruction
Teaching Methods
Undergraduate Students
Workshops
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Summary:Biochemistry is a data‐heavy discipline, yet teaching students to work with large datasets is absent from many undergraduate Biochemistry programs. Ensuring that future generations of students arevbv confident in tackling problems using big data first requires that educators become comfortable teaching big data skills. The activity described herein introduces educators to working with big data and a framework for generating sequence similarity networks using JupyterLab and Python. This article reports a session from the virtual international 2021 IUBMB/ASBMB workshop, “Teaching Science with Big Data.”
ISSN:1470-8175
1539-3429
DOI:10.1002/bmb.21645