Available Until 7/17/2026

Fundamentals of Health Data Science*

For more information or to schedule this course, please contact the NNLM Training Office at nto@utah.edu . 

This 9-week class offered in Moodle covers the basics of programming in Python for data science projects in health sciences. The class includes a general look at data science and algorithmic concepts. In addition, it looks at specific topics in coding, namely the understanding and tools needed to clean data, create data visualizations, and share reproducible results. Each module will contain readings (e.g., articles, book chapters, videos, website content), a discussion board for learners to answer questions about the materials, and an assignment. Learners will be asked to perform these tasks for a final project, which focuses on a provided dataset relating to health research. The class experience is largely asynchronous, meaning you will work on your own, though learners will be expected to meet online a few times during the course to discuss class materials and their final project ideas.

Approved as an elective for Level II of the Data Services Specialization

Resource URLhttps://www.nnlm.gov/training/class-catalog/fundamentals-health-data-science 

Learning Objectives

  1. Employ analytical thinking to solve data science problems with step-by-step procedures.
  2. Prepare data using ethical practices to maintain research integrity and avoid bias.
  3. Use Python programming techniques to clean and analyze datasets.
  4. Communicate results to stakeholders using best practices for visualizing and reporting data.
  5. Explain the importance of reproducibility in working with data and sharing research results.


Course Opens / Pre-Work Module: Introduction & Course Setup

Module 1: Introduction to Data Science & Algorithmic Concepts

Module 2: Introduction to Python

Module 3: Python Advanced Concepts

Module 4: Data Cleaning Python

Module 5: Data Visualization in Python

Module 6: Creating and Sharing Reproducible Data Science Projects

Catch-Up Week

Final Projects / Capstone Due

MLA CE Credits: 32