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A practical and sustainable model for learning and teaching data science

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

37 Scopus citations

Abstract

This paper details our experiences with design and implementation of data science curriculum at University at Buffalo (UB). We discuss (i) briefly the history of project, (ii) a certificate program that we created, (iii) a data-intensive computing course that forms the core of the curriculum and (iv) some of the challenges we faced and how we addressed them. Major goal of the project was to improve the preparedness of our workforce for the emerging data-intensive computing area. We measured this through assessment of student learning on various concepts and topics related to data-intensive computing. We also discuss the best practices in building a data science program. We highlight the importance of external funding support and multidisciplinary collaborations in the success of the project. The pedagogical resources created for the project are freely available to help educators and other learners navigate the path to learning data science. We expect this paper about our experience will provide a road map for educators who desire to introduce data science in their curriculum.

Original languageEnglish
Title of host publicationSIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education
PublisherAssociation for Computing Machinery
Pages169-174
Number of pages6
ISBN (Electronic)9781450338561
DOIs
StatePublished - Feb 17 2016
Event47th ACM Technical Symposium on Computing Science Education, SIGCSE 2016 - Memphis, United States
Duration: Mar 2 2016Mar 5 2016

Publication series

NameSIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education

Conference

Conference47th ACM Technical Symposium on Computing Science Education, SIGCSE 2016
Country/TerritoryUnited States
CityMemphis
Period03/2/1603/5/16

Keywords

  • Cloud computing
  • Curriculum
  • Data Science
  • Data analysis
  • Hadoop
  • MapReduce
  • Spark

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