2021Unpublished venueRequires access

Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone Program

Genevera I. Allen

Open publisher page 41 citations

Abstract

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.

About this research paper

What this paper is about

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.

Why it matters

OpenAlex reports 41 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

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.

Key concepts: Capstone, Curriculum, Experiential learning, Medical education, Capstone course, Computer science, Degree program, Set (abstract data type)

Related papers

Back to paper searchBrowse research topicsOriginal source
Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone Program — Research Paper | ScholarLens