Developing a Hands-on Data Science Curriculum for Non-Computing Majors

为非计算专业开发实践数据科学课程

• 批准号: 2021287
• 负责人: Xumin Liu
• 金额: $ 29.99万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021287&HistoricalAwards=false
• 关键词: Developing; Hands; Data; Science; Curriculum

This project aims to serve the national interest by addressing the particularly high national demand for data scientists, which is estimated to grow much faster than the average for all occupations between 2018 to 2028. Moreover, recent national challenges have highlighted the nation’s ongoing need for data scientists who can rapidly create actionable results from unprecedented amounts of data being generated in various fields. Computing and mathematics departments in colleges and universities across the U.S. have increased their efforts to provide data science content for their own undergraduate majors, but typically in their senior year. This project aims to develop a data science curriculum that can attract non-computing majors. Such an option does not require a long prerequisite chain of courses in programming, data structures, and introductory databases, as well as relevant mathematics. By directly addressing this challenge of broadening the early engagement of all students in data science, this project will create a hands-on curriculum that will make learning more readily-accessible to non-computing majors.The project’s overarching goal is to introduce a data science curriculum appropriate for non-computing disciplines by strengthening data science components in existing Computer Science Principles (CSP) courses and providing a follow-on Data Science Principles (DSP) course. The project’s intellectual merit stems from its two major deliverables. First is a web-based Data Science Learning Platform (DSLP) for students to obtain hands-on practice with processing and analyzing data without needing to write code. Second is a Data Science Curricular Module (DSCM) to teach data science concepts both in the DSP and CSP courses. These deliverables will be developed and refined over the course of the project. As part of the project’s broader impacts, the proposed DSCM will be taught to non-computing majors in CSP and DSP classes during the second and third year of the project. Additionally, to increase the project’s impact, results will be disseminated through publications and presentations delivered at conferences attended by both college and high school teachers. In particular, workshops will be organized for instructors at other institutions to facilitate the adoption of the project-developed curriculum to benefit students across the nation, thus helping to address the national demand for data scientists. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过解决国家对数据科学家的特别高的需求来服务于国家利益，据估计，在2018年至2028年期间，数据科学家的增长速度远远快于所有职业的平均水平。 此外，最近的国家挑战凸显了国家对数据科学家的持续需求，他们可以从各个领域产生的前所未有的数据量中快速创建可操作的结果。 美国高校的计算和数学系已经加大了为本科专业提供数据科学内容的力度，但通常是在大四。 该项目旨在开发一个可以吸引非计算专业学生的数据科学课程。这样的选择不需要在编程，数据结构和介绍性数据库以及相关数学方面的一系列课程。通过直接解决扩大所有学生早期参与数据科学的挑战，该项目将创建一个实践课程，使非计算专业的学生更容易学习。该项目的总体目标是通过加强现有计算机科学原理（CSP）课程中的数据科学部分，并提供以下内容，引入适合非计算学科的数据科学课程-数据科学原理（DSP）该项目的智力价值源于其两个主要的可交付成果。首先是一个基于Web的数据科学学习平台（DSLP），让学生在无需编写代码的情况下获得处理和分析数据的实践。第二个是数据科学课程模块（DSCM），用于在DSP和CSP课程中教授数据科学概念。这些交付品将在项目过程中开发和完善。 作为该项目更广泛影响的一部分，拟议的DSCM将在项目的第二年和第三年教授CSP和DSP课程的非计算专业学生。 此外，为了提高项目的影响力，将通过大学和高中教师参加的会议上发表的出版物和演讲来传播成果。 特别是，将为其他机构的教师组织讲习班，以促进采用项目开发的课程，使全国各地的学生受益，从而帮助满足全国对数据科学家的需求。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Introducing Data Science Topics to Non-Computing Majors

向非计算机专业介绍数据科学主题

• DOI: 10.1145/3478432.3499156
• 发表时间: 2022
• 期刊: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
• 作者: Liu, Xumin;Golen, Erik;Raj, Rajendra K.
• 通讯作者: Raj, Rajendra K.

Developing a Hands-on Data Science Curriculum for Non-Computing Majors

为非计算专业开发实践数据科学课程

• 发表时间: 2022
• 期刊: ASEE annual conference exposition proceedings
• 作者: Liu, X.;Golen, E.;Raj, R.
• 通讯作者: Raj, R.

Offering Data Science Coursework to Non-Computing Majors

为非计算机专业提供数据科学课程

• DOI: 10.1145/3596673.3596971
• 发表时间: 2023
• 期刊: Proceedings of the 2nd International Workshop on Data Systems Education: Bridging education practice with education research
• 作者: Liu, Xumin;Golen, Erik;Raj, Rajendra K.;Fluet, Kimberly
• 通讯作者: Fluet, Kimberly

Hands-on Assignments for Practical Data Science Education to Non-Computing Majors

非计算专业实用数据科学教育的实践作业

• 发表时间: 2023
• 期刊: Proceedings ASEE annual conference
• 作者: Xumin Liu;Erik Golen
• 通讯作者: Erik Golen

DSLP: A Web-based Data Science Learning Platform to Support DS Education for Non-Computing Majors

DSLP：基于网络的数据科学学习平台，支持非计算专业的 DS 教育

• DOI: 10.1145/3478432.3499255
• 发表时间: 2022
• 期刊: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
• 作者: Liu, Xumin;Golen, Erik;Raj, Rajendra K.
• 通讯作者: Raj, Rajendra K.