The major aim of this course is to provide an introduction to the support of data science and open science with the goal of developing and implementing or enhancing data science training and services at participants’ institutions. The course topics include an overview of data science and open science, data literacy, data wrangling, data visualization, and data storytelling. It will also include practice in using Jupyter Notebooks through an open-source browser-based application (JupyterHub) that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. This course will expand on concepts covered in RDM 101: Biomedical and Health Research Data Management Training for Librarians, and threaded throughout will be the librarian’s role in research reproducibility and research integrity and include practice in using Jupyter notebooks. Course is made up of weekly online modules and assignments as well as a final summative project.
Upcoming Course Offering February 24- April 24, 2020
Participants will be able to
- Describe key concepts and trends in open science
- Articulate roles for librarians in data science and research reproducibility
- Clean a dataset to extract information
- Create a visualization of a dataset according to key principles
Week 1: Introduction to Open Science and Data Science
Week 2: Data Literacy
Weeks 3 - 4: Data Wrangling
Week 5: Data Visualization
Week 6: Data Storytelling
Week 7 - 8: Final Projects and Presentations
Each week will use a variety of readings, videos, and interactive tutorials plus an assignment to achieve learning objectives. Students will apply new skills to a project under the guidance of a project instructor.
MLA CE Credits: 36