Data science curriculum in the iField

Allison S. Theobold

29 Juni 2023

The COVID-19 pandemic caused the flaws of traditional grading systems to become even more apparent. In response, a growing number of educators are transitioning their classrooms to focus on alternative methods of assessment. These subversive methods promote more equitable assessments, as they provide a more accurate picture of what a student has learned, cultivate students' intrinsic motivation, and do not privilege students from certain backgrounds. This article details how alternative grading, specifically "ungrading," was integrated into an introductory data science course. I detail how the course components align with the principles of alternative grading, students' responses to the course structure, and the lessons I learned along the way. Finally, I close with a discussion of how infusing alternative methods of assessment into the classroom stands to cultivate the diversity continually lacking in computer science and data science.

Michael Overton Stephen W. Kleinschmit

1 April 2021

Public administration is struggling to contend with a substantial shift in practice fueled by the accelerating adoption of information technology. New skills, competencies and pedagogies are required by the field to help overcome the data-skills gap. As a means to address these deficiencies, we introduce the Data Science Literacy Framework, a heuristic for incorporating data science principles into public administration programs. The framework suggests that data literacy is the dominant principle underlying a shift in professional practice, accentuated by an understanding of computational science, statistical methodology, and data-adjacent domain knowledge. A combination of new and existing skills meshed into public administration curriculums help implement these principles and advance public administration education.

Michael L. Brodie

20 Januari 2023

The Communications website, https://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications, we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM https://cacm.acm.org/blogs/blog-cacm Koby Mike and Orit Hazzan consider why multiple definitions are needed to pin down data science.

Paul M. Leidig M. Doyle A. Danyluk + 5 lainnya

3 Maret 2021

In this session, members of the ACM Data Science (DS) Task Force will present the final draft of Computing Competencies for Undergraduate Data Science Programs. Drafting this document has been a three-year process, in which the task force has released preliminary drafts, sought input from the community, and responded to the community's helpful feedback. Our intent is that the session be an exchange that will clarify the contents of the report and provide participants with ways to put the report into practice at their own institutions. This session should be of interest to all SIGCSE attendees, but especially to faculty developing college-level curricula in Data Science.

John DeNero Michael I. Jordan A. Adhikari

13 Februari 2021

The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.