Fundamentals of Health Data Science

This 9-week class covers the basics of programming in Python for data science projects in health sciences. It includes a general look at data science and algorithmic concepts as well as specific topics in coding, namely the understanding and tools needed to clean data, create data visualizations, and share reproducible results. 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, though learners will be expected to meet online a few times during the course to discuss class materials and their final project ideas. 

Objectives

Upon completion of the Fundamentals of Health Data Science, learners will be able to do the following:

  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.

Class Owner(s)

Contact the NNLM Training Office for more information.

Instructor(s)

Contact the NNLM Training Office for more information.

Continuing Education

This class qualifies for Medical Library Association (MLA) Data Services Specialization (DSS).

This class has been approved for 32 Medical Library Association (MLA) continuing education credits.

Classes

Event Title Event Start Date Summary Continuing Education Credits CE Categories Is Online Registration

No classes.

Event Title Event Start Date Summary Continuing Education Credits CE Categories Is Online Registration
Fundamentals of Health Data Science 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 also meet a handful of times with community experts, experienced data science librarians, to discuss the field and class assignments. 32.00 DSS Level 2 Off