Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley

Rinat B. Rosenberg-Kima Noa Ragonis Koby Mike + 1 lainnya

21 Juli 2022

Incorporating data thinking into computer science education.

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.

Genevera I. Allen

3 Maret 2021

Interest in data science education and degree programs has rapidly expanded over the past several years. An integral part of many degree programs is a capstone experience, where students complete a major research or real-world project at the culmination of their educational program. In engineering and computer science, many have shown that client-sponsored projects lead to better student engagement and improved training. In this paper, we discuss experiences with developing an interdisciplinary, client-sponsored capstone program in data science and machine learning. We show how we set up the capstone program, including how the program is structured, how projects are set up, how the course is managed, how students are assessed, and outline the newly developed capstone curriculum. Finally, we report results from a cohort of students participating in this capstone program and discuss lessons learned as well as best practices when developing data science capstone programs.

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.

B. Bishop Chirag Shah I. Song + 10 lainnya

30 Juli 2022

Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate‐level and undergraduate‐level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.