Foundations in data science course
Gain knowledge and learn how to solve problems with data.
Now available through the ASU Library’s Unit for Data Science and Analytics, a free, new digital learning experience offers credentialing to students, faculty and staff of all levels and disciplines in need of an introductory course in data science.
Foundations of Data Science is a six-module ASU Canvas course, designed by data science experts, aimed at enhancing one’s understanding of using data as a research tool – everything from data visualization and machine learning to natural language processing and model selection.
No programming is involved. At the end of each module will be a quiz of multiple choice questions and one or more essay questions to apply critical analysis skills.
The course is available to self-enroll for ASU students, faculty and staff only (at this time).
Podcasts
Aimed at late high school and beyond, Misinfo Weekly is an educational podcast dedicated to understanding misinformation in our time. Providing perspective on current events in misinformation, the show breaks down basic and advanced concepts in mis- and disinformation, and helps track and trace how misinformation events come to be.
The show is a collaboration between the Information Competition Lab, co-led by Michael Simeone and Shawn Walker, and the Unit for Data Science and Analytics at Arizona State University Library.
Misinfo Weekly transcripts
Resource sheets
Video series
Data Visualization
This series covers techniques and approaches to statistical data visualization, and explores ways to use graphics in both research and data presentation.
Produced by: Laura Davis and the Unit for Data Science and Analytics at Arizona State University Library.
Model selection
This series assists in the ability to identify a suitable algorithm and/or modeling technique that is appropriate to the data and problem statement. Pros and cons of various methods will be reviewed and appropriate applications to use will be explored.
Produced by: Laura Davis and the Unit for Data Science and Analytics at Arizona State University Library.
Model Selection Part 1