This study explored the core competencies, technological skills, functional proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding research questions: (1) How do higher education institutions define the professional roles of data science practitioners? (2) What are the working environments of data science practitioners at higher education institutions? (3) What are the core competencies, functional proficiencies, technical knowledge, technological skills, professional experiences, levels of job satisfaction, and working environments of data science practitioners in higher education? (4) What is the career trajectory of data science practitioners in higher education? (5) What has influenced the job satisfaction levels of data science practitioners in higher education? (6) What steps do data science practitioners take to ensure the results of their analysis are equitable? Previous research needs to include more information on the intersection between the use of information technology and best practices for good data stewardship in higher education. The literature needs to be more extensive regarding the competencies, skills, functional proficiencies, and professional experiences necessary for data science practitioners responsible for data stewardship at higher education institutions. This study used a sequential mixed methods design to gather data. Findings from this study supported past studies and their contributions to the overall evolution of the data science field. Specifically, this study explored the roles of data science practitioners in higher education, uncovering a critical balance between technical proficiency and a cognizance of their work's wider impact. This study emphasized the importance of merging technical expertise with an understanding of ethical, societal, and organizational contexts. It also focused attention on a notable gap in how practitioners perceive their role in fostering institutional equity. In addition, the research incorporated a detailed matrix, constructed from job recruitment bulletins, which profiled data scientists, alongside survey data that provided insights into their professional backgrounds, levels of job satisfaction, and their self-identification within the field. The findings advocate for the enrichment of educational programs and professional development initiatives in data science to enhance equity-mindedness among professionals in this sector. Furthermore, this study suggested several growth opportunities related to practitioners of data science. This is especially true for data science practitioners who work at higher education institutions.
","abstract_html":"<p>This study explored the core competencies, technological skills, functional proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding research questions: (1) How do higher education institutions define the professional roles of data science practitioners? (2) What are the working environments of data science practitioners at higher education institutions? (3) What are the core competencies, functional proficiencies, technical knowledge, technological skills, professional experiences, levels of job satisfaction, and working environments of data science practitioners in higher education? (4) What is the career trajectory of data science practitioners in higher education? (5) What has influenced the job satisfaction levels of data science practitioners in higher education? (6) What steps do data science practitioners take to ensure the results of their analysis are equitable? Previous research needs to include more information on the intersection between the use of information technology and best practices for good data stewardship in higher education. The literature needs to be more extensive regarding the competencies, skills, functional proficiencies, and professional experiences necessary for data science practitioners responsible for data stewardship at higher education institutions. This study used a sequential mixed methods design to gather data. Findings from this study supported past studies and their contributions to the overall evolution of the data science field. Specifically, this study explored the roles of data science practitioners in higher education, uncovering a critical balance between technical proficiency and a cognizance of their work's wider impact. This study emphasized the importance of merging technical expertise with an understanding of ethical, societal, and organizational contexts. It also focused attention on a notable gap in how practitioners perceive their role in fostering institutional equity. In addition, the research incorporated a detailed matrix, constructed from job recruitment bulletins, which profiled data scientists, alongside survey data that provided insights into their professional backgrounds, levels of job satisfaction, and their self-identification within the field. The findings advocate for the enrichment of educational programs and professional development initiatives in data science to enhance equity-mindedness among professionals in this sector. Furthermore, this study suggested several growth opportunities related to practitioners of data science. This is especially true for data science practitioners who work at higher education institutions.</p>","abstract_has_math":false,"creators":["Bolden, James LaMar"],"institution":null,"degree_name":"Education PhD, Joint with San Diego State University","degree_level":"Restricted to Claremont Colleges Dissertation","degree_discipline":"School of Educational Studies","degree_department":null,"school":null,"contributors":["David Drew","Jonathan Luke Wood"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T01:41:01Z","subjects":["Data Management","Data Science","Data Scientist","Data Steward","Data Stewardship","Information Technology","Computer Sciences","Educational Technology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/911","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David Drew","Jonathan Luke Wood"]},{"key":"dc:creator","label":"Author","values":["Bolden, James LaMar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-22T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Educational Studies"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Restricted to Claremont Colleges Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Education PhD, Joint with San Diego State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data Management","Data Science","Data Scientist","Data Steward","Data Stewardship","Information Technology","Computer Sciences","Educational Technology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/911"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study explored the core competencies, technological skills, functional proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding research questions: (1) How do higher education institutions define the professional roles of data science practitioners? (2) What are the working environments of data science practitioners at higher education institutions? (3) What are the core competencies, functional proficiencies, technical knowledge, technological skills, professional experiences, levels of job satisfaction, and working environments of data science practitioners in higher education? (4) What is the career trajectory of data science practitioners in higher education? (5) What has influenced the job satisfaction levels of data science practitioners in higher education? (6) What steps do data science practitioners take to ensure the results of their analysis are equitable? Previous research needs to include more information on the intersection between the use of information technology and best practices for good data stewardship in higher education. The literature needs to be more extensive regarding the competencies, skills, functional proficiencies, and professional experiences necessary for data science practitioners responsible for data stewardship at higher education institutions. This study used a sequential mixed methods design to gather data. Findings from this study supported past studies and their contributions to the overall evolution of the data science field. Specifically, this study explored the roles of data science practitioners in higher education, uncovering a critical balance between technical proficiency and a cognizance of their work's wider impact. This study emphasized the importance of merging technical expertise with an understanding of ethical, societal, and organizational contexts. It also focused attention on a notable gap in how practitioners perceive their role in fostering institutional equity. In addition, the research incorporated a detailed matrix, constructed from job recruitment bulletins, which profiled data scientists, alongside survey data that provided insights into their professional backgrounds, levels of job satisfaction, and their self-identification within the field. The findings advocate for the enrichment of educational programs and professional development initiatives in data science to enhance equity-mindedness among professionals in this sector. Furthermore, this study suggested several growth opportunities related to practitioners of data science. This is especially true for data science practitioners who work at higher education institutions.
"]},{"key":"dc:title","label":"Title","values":["Exploring Data Science at Institutions of Higher Education: Competencies, Skills, Proficiencies, and Professional Experiences"]}]}],"canonical_facts":{"dc:contributor":["David Drew","Jonathan Luke Wood"],"dc:creator":["Bolden, James LaMar"],"dc:date.available":["2025-01-22T08:00:00Z"],"dc:description.abstract":["This study explored the core competencies, technological skills, functional proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding research questions: (1) How do higher education institutions define the professional roles of data science practitioners? (2) What are the working environments of data science practitioners at higher education institutions? (3) What are the core competencies, functional proficiencies, technical knowledge, technological skills, professional experiences, levels of job satisfaction, and working environments of data science practitioners in higher education? (4) What is the career trajectory of data science practitioners in higher education? (5) What has influenced the job satisfaction levels of data science practitioners in higher education? (6) What steps do data science practitioners take to ensure the results of their analysis are equitable? Previous research needs to include more information on the intersection between the use of information technology and best practices for good data stewardship in higher education. The literature needs to be more extensive regarding the competencies, skills, functional proficiencies, and professional experiences necessary for data science practitioners responsible for data stewardship at higher education institutions. This study used a sequential mixed methods design to gather data. Findings from this study supported past studies and their contributions to the overall evolution of the data science field. Specifically, this study explored the roles of data science practitioners in higher education, uncovering a critical balance between technical proficiency and a cognizance of their work's wider impact. This study emphasized the importance of merging technical expertise with an understanding of ethical, societal, and organizational contexts. It also focused attention on a notable gap in how practitioners perceive their role in fostering institutional equity. In addition, the research incorporated a detailed matrix, constructed from job recruitment bulletins, which profiled data scientists, alongside survey data that provided insights into their professional backgrounds, levels of job satisfaction, and their self-identification within the field. The findings advocate for the enrichment of educational programs and professional development initiatives in data science to enhance equity-mindedness among professionals in this sector. Furthermore, this study suggested several growth opportunities related to practitioners of data science. This is especially true for data science practitioners who work at higher education institutions.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/911"],"dc:subject":["Data Management","Data Science","Data Scientist","Data Steward","Data Stewardship","Information Technology","Computer Sciences","Educational Technology"],"dc:title":["Exploring Data Science at Institutions of Higher Education: Competencies, Skills, Proficiencies, and Professional Experiences"],"thesis:degree_discipline":["School of Educational Studies"],"thesis:degree_level":["Restricted to Claremont Colleges Dissertation"],"thesis:degree_name":["Education PhD, Joint with San Diego State University"]},"updated_at":"2026-07-24T01:41:01Z"}