New Biomedical Data Science Course

Big Data Health

New Biomedical Data Science Course

Biomedical Data Science is a new 500/600 level course, cross listed between the Departments of Biology and Computer Science.  The goal of this course is to teach students how to turn data into information. Students will learn the necessary computational skills for the analysis of genomic data sets.

Starting with the basics of using a command line interface (text editor, shell), and how to get started on the U’s Supercomputer Pegasus, the course will teach Python, and how to write scripts for downloading, manipulating, and analyzing data. File sharing and version control using github will be introduced (including RCR training). Using an experiential learning model, students will choose a Next Generation Sequencing data set (RNAseq, exome, or whole genome; public or user), and will learn how to analyze the data set, and how to interpret the results and present them.

Science Level: BIL575 and BIL675 / CSC598
Format: Tue/Thurs 9:30-10:45 AM  |  Cox 213
Instructors: Sawsan Khuri (coordinator) and Stefan Wuchty (with B. Kirkpatrick)

Student Learning Objectives
By the end of this course, students will be able to:
1. Manipulate data files and software in the Unix/Linux/OSX operating system
2. Work within a High Performance Computing environment
3. Write scripts in Python that are relevant to molecular data analysis
4. Gain a deeper knowledge of the bioinformatics algorithms that are commonly used for biomedical data analysis
5. Apply the above skill set for the analysis of genomics data
6. Interpret and present the results of genomics data analysis

Prerequisite BIL250 or by instructor permission

Recommended (but not required) Textbooks

  • Exploring Bioinformatics, St. Clair & Visick, Jones and Bartlett Learning, 2015
  • Practical Computing for Biologists, Haddock & Dunn, Sinauer Associates, 2011

Course Assessment

  • Graded Homework (45%) Three graded homework assignments—15% each
  • Project (40%)  A project assignment graded on data wrangling, data analysis, interpretation, and presentation—10% each
  • Final Exam (15%)  To test on the theory