A flipped classroom introduction to Python inspired by and for life sciences

Course Materials
ejamezquita.github.io/plnt_sci2500

University of Missouri logo

PLNT_SCI 2500: Data Science for Life Sciences

orcid logo Erik J. Amézquita

Division of Plant Sciences & Technology, University of Missouri


Setting and Setup
Python and Jupyter—license-free tools

Logo for Python, the programming language Logo for Jupyter, the coding platform

  • Course open to all undergrads across all majors.
  • College Algebra (MAT 1100) is the only pre-requisite.
  • No prior statistics or coding experience is assumed.
  • Two 75-minute sessions per week through 16 weeks.
  • Course fully based in Python with Jupyter Notebooks.

Open-access, example-driven lessons

Screenshot of the research paper How shrub encroachment under climate change could threaten pollination services for alpine wildflowers
Relationship between Julian days when flowering specimens of Salix and Polemonium were collected in each of 25 historic contact zones in the Colorado Rocky Mountains (USA).
screenshot of the data availability statement of the referenced paper.

  • Python and data science as means to an end.
  • Class discussion driven by biology-oriented open-access papers with publicly available data.
  • Students associate content to real-life situations.
  • Expose students to data literacy and visualization.
  • Papers co-authored by Mizzou's faculty: promote the exciting research done on campus.

Acknowledgements

The class materials are heavily inspired by those of CMSE 201 - Introduction to Computational Modeling at Michigan State University and by Plants & Python.

References

Kettenbach JA, Miller-Struttmann N, Moffett Z, Galen C (2017). How shrub encroachment under climate change could threaten pollination services for alpine wildflowers. Ecol Evol. 7: 6963–6971.


Active Learning: Pre-Class Assignments

Screenshot of the pre-class assignment on correlations. The screenshot highlights a YouTube video explaining the Pearson Correlation Coefficient.

  • Students watch videos and comment working code:
     — Python programming and Python packages
     — Data wrangling, analysis, and visualization
     — Mathematical modeling and statistics
     — Common statistical misconceptions and pitfalls

Active Learning: In-Class Assignments

Screenshot of the in-class assignment on correlations. The screenshot hightlights Python code to plot correlated data and compute its Pearson coefficient.

  • Students apply the concepts to various datasets to reproduce open-access published results.
  • Encourage them to work in groups and peer-learning.

Conclusions and future directions

Screenshot of the in-class assignment on correlations. The screenshot hightlights Python code to plot correlated data and compute its Pearson coefficient.

  • No prior experience in biology, data science, or coding assumed, welcoming students from every background.
  • Python and Jupyter are open-source technologies, with no licensing barriers and plenty of free support.
  • Lessons are drawn from open-access papers, avoiding thus any paywalls and promoting open science.