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Introductory data science courses are appearing at colleges, universities, and high schools around the country and the world. What topics do we cover in these courses, and how and why are these decisions made? How do we consider the background knowledge of our students and how they hope to utilize their skills after this course (whether professionally, additional courses, or as an engaged citizen)? In addition, the course is being taught by computer scientists, statisticians, business analysts, mathematicians, journalists, etc. Each of these disciplines approaches the topics differently. What upskilling is required of instructors to prepare them to integrate material from academic disciplines in which they were not trained into the course? How much, if any, cross-disciplinary collaboration, and discussion occurs or should occur in designing this course? Participants in this birds-of-a-feather will share their decision processes and choices about introductory data science courses that they teach or are designing. This includes choices made about the content as well as whether and how upskilling occurs. They will review and refine a list of current data science topics created based on national surveys of data science instructors as well as a review of curriculum guidelines. Close attention will be paid to differing language between data science instructors from different academic backgrounds. We welcome new and experienced data science instructors, educators planning on or interested in teaching such a course.
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A Cross-disciplinary Review of Introductory Undergraduate Data Science Course Content
Data Science is one of the fastest growing fields with unmet demand from employers. Many academic institutions have taken on the task of creating programs to meet both current and future needs and demands. Data science, as a field, integrates aspects of computer science, statistics, and subject matter expertise which encourages cross-disciplinary conversations and collaboration. In this talk, we present results from a broad survey of instructors of introductory college-level data science courses for undergraduates. In addition, we explore the alignment of these findings with the recommendations of various professional organizations. We conducted a national survey on topics covered in introductory, college-level data science courses. With responses from computer scientists, statisticians, and allied fields, these results represent a wide array of instructors of data science. The survey identifies topics commonly covered, the amount of time spent on each, common and divergent definitions of data science, and course materials used. These results will be presented. We will then discuss the alignment of these results through a rigorous review and synthesis of recommendations from various professional organizations. These include Association for Computing Machinery's Computing Competencies for Undergraduate Data Science Curricula[1], the National Academies of Science, Engineering, and Medicine’s Data Science for Undergraduates: Opportunities and Options[2], the Park City Math Institute's report Curriculum Guidelines for Undergraduate Programs in Data Science[3], and the American Statistical Association’s Two-Year College Data Science Summit Final Report[4] and Curriculum Guidelines for Undergraduate Programs in Statistical Science[5]. We will also explore alignment with ABET’s accreditation of data science.[6]
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- Award ID(s):
- 2013392
- PAR ID:
- 10561966
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704246
- Page Range / eLocation ID:
- 1937 to 1937
- Subject(s) / Keyword(s):
- Data Science Undergraduate Curriculum
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3626253.3635352
