A flipped-classroom introduction to Python inspired by and for life sciences¶
From multi-omics sequencing to precision health medicine, from geographical information systems to digital agriculture, life sciences in general face mountains of data that must be efficiently analyzed and summarized. With recent technological advances, we are now able to collect precise information of gene expression levels across tissues, molecular structure of proteins, minute soil and climate variations across time and space, detailed canopy and pasture drone imagery, spatial organization of cells, swarms, herds, and people at different scales, etc. As science and technology transition into a data-driven era, meaningful interpretation of these large datasets is a limiting factor. The solutions to grand societal challenges we face lie in data science.
Over the course of this semester, we will explore how foundational aspects of data science intersect and can be applied directly to many areas within life sciences. These lessons will be example-driven, with problems and data-sets drawn specifically from research carried out by CAFNR faculty. Working with these datasets will provide hands-on experience on how data science can be applied to various life science contexts, and it will also showcase exciting research being done at our own university. The course will be based in Python as a programming language but assumes no prior coding experience. Students from all majors are welcome. Although the data analysis examples will be mostly inspired in life sciences, the data analysis skills and techniques can be applied to all kinds of domains. The course will be conducted as a flipped classroom, where students will be required to complete assignments before every class period and then participate actively in groups during class time.
Course Objectives¶
The main goal of the course is for students to have agency to explore, wrangle, model, analyze, and visualize datasets of their interest. By the end of the semester, the students will:
Describe and summarize examples of various biological systems through the use of computational algorithms and tools.
Write programs to solve common problems in life sciences.
Identify salient features of a system that can be codified into a model.
Manipulate, analyze, and visualize datasets and use this data to evaluate models.
Present the results from a scientific computing problem both verbally and in writing.
Recognize and articulate the validity and reliability of data-based results based on its original biological context.
Important Course Information¶
Course Syllabus. You’ll need to access it using your MU Google account.
Installing miniconda (Make sure to go through this before the first day of class!)
Installing Jupyter. You must install miniconda first.
Instructor Information¶
Lead Instructor¶
Erik Amézquita
eah4d@missouri.edu
Assistant Professor, Division of Plant Science & Technology
Adjunct Professor, Department of Mathematics