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.

该项目旨在通过满足国家对数据科学家的需求特别高的需求来满足国家利益，据估计，在2018年至2028年之间，所有事件的平均值要快得多。此外，最近的国家挑战强调了该国对数据科学家的持续需求，这些数据科学家可以迅速产生可行的结果，这些数据能够从各个领域产生的数据量产生可行的数据，这些数据是在各个领域产生的。美国各学院的计算和数学系增加了他们为自己的本科专业提供数据科学内容的努力，但通常在大四时。该项目旨在开发一个可以吸引非计算专业的数据科学课程。这种选择不需要长期的编程，数据结构和简介数据库的课程以及相关的数学链。通过直接应对扩大所有学生参与数据科学的早期参与的挑战，该项目将创建一个动手课程，这将使学习更容易被非计算专业的专业访问。该项目的总体目标是引入一个数据科学课程，可通过在现有的数据科学原理（cssp）中（c Carriped of Compline of Comportials of Compartiles of Comperions of total计算机科学）（cssp）介绍（CSSS SPS）原理（CSSP），并介绍耶和华（C）。 课程。该项目的智力优点是其两个主要可交付成果。首先是一个基于Web的数据科学学习平台（DSLP），供学生通过处理和分析数据，而无需编写代码，以获取动手实践。第二是数据科学课程模块（DSCM），用于教授DSP和CSP课程中的数据科学概念。这些可交付成果将在整个项目的过程中开发和完善。作为该项目更广泛影响的一部分，在该项目的第二年和第三年中，将教授拟议的DSCM在CSP和DSP课程中不计算专业。此外，为了增加项目的影响，结果将通过大学和高中教师参加的会议上的出版物和演讲来传播。特别是，将组织研讨会，以供其他机构的讲师组织，以促进通过项目开发的课程，以使全国学生受益，从而有助于满足国家对数据科学家的需求。 NSF IUSE：EHR计划支持研发项目，以提高所有学生STEM教育的有效性。通过参与的学生学习轨道，该计划支持了承诺实践和工具的创建，探索和实施。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。

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

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.

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