The field of data science is a multidisciplinary field, with mathematics, statistics and computer science forming the core topics. In my studies, I have been examined using research methodologies including citations analysis, examination of the syllabi, and student peer review.
Here are my publications on the subject:
2024 Friedman, A. and Beasley, Z. “Using Textual Analysis to Examine Student Engagement in Online Undergraduate Science Education.” Journal of Statistics and Data Science Education.
2020 Friedman, A. “Visualizing protein data sets in R through a student peer-review rubric. Biochemistry and Molecular Biology Education.
2020 Friedman, A., Beasley, Z.J. “Teaching R with Peer Review and a New Rubric.” UseR! 2020.
2019 Friedman, A. “Data Science syllabi measuring its content.” Education and Information Technologies. 24(6).
2017 Friedman, A. “Measuring the Promise of Big Data Syllabi.” Technology, Pedagogy andEducation 26(5).
2016 Friedman, A. “Statistics for Library and Information Services: A Primer for Using Open Source R Software for Accessibility and Visualization.”Published by Rowman & Littlefield.
