View in EDS

Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach

Saved in:
Bibliographic Details
Title: Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach
Language: English
Authors: Stuart King, Serveh Sharifi Far (ORCID 0000-0001-8403-6286)
Source: Teaching Statistics: An International Journal for Teachers. 2026 48(1):S67-S76.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 10
Publication Date: 2026
Document Type: Journal Articles
Reports - Descriptive
Education Level: Higher Education
Postsecondary Education
Descriptors: Data Science, Statistics Education, Teaching Methods, Interdisciplinary Approach, Introductory Courses, Graduate Students, Masters Programs, Blended Learning, Data Analysis, Skill Development, Programming Languages, Programming, Problem Solving, Ethics, Data Processing
DOI: 10.1111/test.70014
ISSN: 0141-982X
1467-9639
Abstract: This paper describes the development and delivery of an introductory data science course designed for interdisciplinary master's students studying in a hybrid format, without advanced mathematics prerequisites. The course addresses the increasing need for data science skills among non-specialists, preparing them to tackle real-world challenges. It focuses on a hands-on, problem-led approach that emphasizes practical applications of data analysis and supports engagement among students from diverse academic backgrounds. The course teaches Python programming through Jupyter notebooks as the core tool for analyzing data and employs additional support tools to aid learning and assessment. This structure aims to build foundational data science skills and foster students' confidence in working with data.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1505713
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1505713
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Stuart+King%22">Stuart King</searchLink><br /><searchLink fieldCode="AR" term="%22Serveh+Sharifi+Far%22">Serveh Sharifi Far</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8403-6286">0000-0001-8403-6286</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Teaching+Statistics%3A+An+International+Journal+for+Teachers%22"><i>Teaching Statistics: An International Journal for Teachers</i></searchLink>. 2026 48(1):S67-S76.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 10
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Descriptive
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data+Science%22">Data Science</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics+Education%22">Statistics Education</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Interdisciplinary+Approach%22">Interdisciplinary Approach</searchLink><br /><searchLink fieldCode="DE" term="%22Introductory+Courses%22">Introductory Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Masters+Programs%22">Masters Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Blended+Learning%22">Blended Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Skill+Development%22">Skill Development</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+Languages%22">Programming Languages</searchLink><br /><searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Processing%22">Data Processing</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/test.70014
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0141-982X<br />1467-9639
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper describes the development and delivery of an introductory data science course designed for interdisciplinary master's students studying in a hybrid format, without advanced mathematics prerequisites. The course addresses the increasing need for data science skills among non-specialists, preparing them to tackle real-world challenges. It focuses on a hands-on, problem-led approach that emphasizes practical applications of data analysis and supports engagement among students from diverse academic backgrounds. The course teaches Python programming through Jupyter notebooks as the core tool for analyzing data and employs additional support tools to aid learning and assessment. This structure aims to build foundational data science skills and foster students' confidence in working with data.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1505713
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1505713
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/test.70014
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: S67
    Subjects:
      – SubjectFull: Data Science
        Type: general
      – SubjectFull: Statistics Education
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Interdisciplinary Approach
        Type: general
      – SubjectFull: Introductory Courses
        Type: general
      – SubjectFull: Graduate Students
        Type: general
      – SubjectFull: Masters Programs
        Type: general
      – SubjectFull: Blended Learning
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Skill Development
        Type: general
      – SubjectFull: Programming Languages
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Problem Solving
        Type: general
      – SubjectFull: Ethics
        Type: general
      – SubjectFull: Data Processing
        Type: general
    Titles:
      – TitleFull: Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Stuart King
      – PersonEntity:
          Name:
            NameFull: Serveh Sharifi Far
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0141-982X
            – Type: issn-electronic
              Value: 1467-9639
          Numbering:
            – Type: volume
              Value: 48
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Teaching Statistics: An International Journal for Teachers
              Type: main
ResultId 1