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The first data-intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data-infused education, research, and collaboration. Students learn Python, R, SQL, and shell-scripting, while working on weekly projects within a high-performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data-intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. 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Data science education has seen rapid growth over the past decade. To provide students with hands-on opportunities to work with real data, many data science programs have advocated for and implemented experiential learning opportunities throughout the curriculum, which has been shown in a wide variety of literature to have many benefits. Most experiential learning opportunities in STEM programs are provided through capstone and engineering design courses; this is becoming increasingly the case in data science programs as well where several universities have developed data science capstone programs in which students work with clients on the client’s real-world data sets. While client-sponsored capstone projects are an exemplar of experiential learning, they may pose major challenges to implement and can be particularly resource-intensive for institutions; this is especially the case in data science where the legalities of data sharing may come with additional hurdles. Because of this, we were motivated to develop a novel client-facing data science consulting course that provides a unique experiential learning scenario to both undergraduate and graduate students while requiring much fewer resources and legalities. In our novel data science consulting course, groups of students work directly with real clients in a consulting clinic setting to provide data science guidance and short-term help with data science challenges. Through this process, students learn about the diversity of real-world problems in data science, how to lead consultations with clients effectively as a team, how to frame data science challenges and research possible solutions, and how to communicate solutions to clients in reports and presentations. We leveraged best practices in consulting courses developed in business school settings to design our course. Additionally, the consulting course serves as a community service initiative whereby researchers, clinicians, non-profit and government workers, and industry professionals benefit from the advice and short-term help provided through consultation. In this paper, we report how our consulting course is set up, how clients from both within and outside the university can seek help at the consulting clinic, and how the structure of the course enables students to have firsthand experience working on many real-world data science problems with clients. 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As degree creation has occurred mainly due to demand, ACM and other professional bodies have recently stepped in to provide curricular guidance. However, no \\em shared global framework for data science as an academic discipline exists, making growth unfocused and driven by employer demands. More recently, the growth of artificial intelligence has also impacted data science programs. This working group builds on prior efforts and participant experiences to develop a global taxonomy of approaches to data science education and expectations for graduates of data science programs to \\em think like data scientists.", "proceedingsTitle": "Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education", "conferenceName": "", "publisher": "Association for Computing Machinery", "place": "New York, NY, USA", "date": "July 2, 2019", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "265–266", "series": "ITiCSE '19", "seriesNumber": "", "DOI": "10.1145/3304221.3325533", "ISBN": "978-1-4503-6895-7", "citationKey": "", "url": "https://dl.acm.org/doi/10.1145/3304221.3325533", "accessDate": "2024-06-02", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Data Science Education", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "accreditation", "type": 1 }, { "tag": "computing education", "type": 1 }, { "tag": "data science education", "type": 1 }, { "tag": "global standards", "type": 1 }, { "tag": "iticse working group", "type": 1 }, { "tag": "multidisciplinary education", "type": 1 } ], "collections": [ "W67SCRQ5" ], "relations": {}, "dateAdded": "2024-06-02T18:06:48Z", "dateModified": "2024-06-02T18:06:48Z" } }, { "key": "3TPWFXXD", "version": 267, "library": { "type": "group", "id": 146983, "name": "LevyKome", "links": { "alternate": { "href": "https://www.zotero.org/groups/levykome", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/146983/items/3TPWFXXD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/levykome/items/3TPWFXXD", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/146983/items/MDHJUK2M", "type": "application/json" } }, "meta": { "createdByUser": { "id": 164985, "username": "samk20012", "name": "Sam Kome", "links": { "alternate": { "href": "https://www.zotero.org/samk20012", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "3TPWFXXD", "version": 267, "parentItem": "MDHJUK2M", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2024-06-02T18:05:36Z", "url": "https://dl.acm.org/doi/pdf/10.1145/3408877.3432536", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Allen - 2021 - Experiential Learning in Data Science Developing .pdf", "md5": "e34a8dfab4ee3108481897b3172e3610", "mtime": 1717351536000, "tags": [], "relations": {}, "dateAdded": "2024-06-02T18:05:36Z", "dateModified": "2024-06-02T18:05:36Z" } }, { "key": "MDHJUK2M", "version": 266, "library": { "type": "group", "id": 146983, "name": "LevyKome", "links": { "alternate": { "href": "https://www.zotero.org/groups/levykome", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/146983/items/MDHJUK2M", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/levykome/items/MDHJUK2M", "type": "text/html" } }, "meta": { "createdByUser": { "id": 164985, "username": "samk20012", "name": "Sam Kome", "links": { "alternate": { "href": "https://www.zotero.org/samk20012", "type": "text/html" } } }, "creatorSummary": "Allen", "parsedDate": "2021-03-05", "numChildren": 1 }, "data": { "key": "MDHJUK2M", "version": 266, "itemType": "conferencePaper", "title": "Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone Program", "creators": [ { "creatorType": "author", "firstName": "Genevera I.", "lastName": "Allen" } ], "abstractNote": "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. 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