Teaching the Foundations of Data Science: An Interdisciplinary Approach

The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary approach and experiences in teaching a Data Science course, the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2015-12
Hauptverfasser: Asamoah, Daniel, Doran, Derek, Schiller, Shu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Asamoah, Daniel
Doran, Derek
Schiller, Shu
description The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary approach and experiences in teaching a Data Science course, the first of its kind offered at the Wright State University. Two faculty members from the Management Information Systems (MIS) and Computer Science (CS) departments designed and co-taught the course with perspectives from their previous research and teaching experiences. Students in the class had mix backgrounds with mainly MIS and CS majors. Students' learning outcomes and post course survey responses suggested that the course delivered a broad overview of data science as desired, and that students worked synergistically with those of different majors in collaborative lab assignments and in a semester long project. The interdisciplinary pedagogy helped build collaboration and create satisfaction among learners.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2083825312</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2083825312</sourcerecordid><originalsourceid>FETCH-proquest_journals_20838253123</originalsourceid><addsrcrecordid>eNqNjLEOgjAUABsTE4nyDy9xJimvoMSNqKiz7KQpRUrIK7Zl8O9l8AOcbrjLrViEQqRJkSFuWOz9wDnHwxHzXETsVmupekMvCL2Gys7UymAsebAdXGSQ8FRGk9InKAkeFLRrjVdmGg1J94FympxdDju27uTodfzjlu2ra32-J4t-z9qHZrCzo0U1yAtRYC5SFP9VX77HOys</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2083825312</pqid></control><display><type>article</type><title>Teaching the Foundations of Data Science: An Interdisciplinary Approach</title><source>Free E- Journals</source><creator>Asamoah, Daniel ; Doran, Derek ; Schiller, Shu</creator><creatorcontrib>Asamoah, Daniel ; Doran, Derek ; Schiller, Shu</creatorcontrib><description>The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary approach and experiences in teaching a Data Science course, the first of its kind offered at the Wright State University. Two faculty members from the Management Information Systems (MIS) and Computer Science (CS) departments designed and co-taught the course with perspectives from their previous research and teaching experiences. Students in the class had mix backgrounds with mainly MIS and CS majors. Students' learning outcomes and post course survey responses suggested that the course delivered a broad overview of data science as desired, and that students worked synergistically with those of different majors in collaborative lab assignments and in a semester long project. The interdisciplinary pedagogy helped build collaboration and create satisfaction among learners.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Collaboration ; Colleges &amp; universities ; Data science ; Information management ; Interdisciplinary aspects ; Management information systems ; Students ; Teaching</subject><ispartof>arXiv.org, 2015-12</ispartof><rights>2015. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Asamoah, Daniel</creatorcontrib><creatorcontrib>Doran, Derek</creatorcontrib><creatorcontrib>Schiller, Shu</creatorcontrib><title>Teaching the Foundations of Data Science: An Interdisciplinary Approach</title><title>arXiv.org</title><description>The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary approach and experiences in teaching a Data Science course, the first of its kind offered at the Wright State University. Two faculty members from the Management Information Systems (MIS) and Computer Science (CS) departments designed and co-taught the course with perspectives from their previous research and teaching experiences. Students in the class had mix backgrounds with mainly MIS and CS majors. Students' learning outcomes and post course survey responses suggested that the course delivered a broad overview of data science as desired, and that students worked synergistically with those of different majors in collaborative lab assignments and in a semester long project. The interdisciplinary pedagogy helped build collaboration and create satisfaction among learners.</description><subject>Collaboration</subject><subject>Colleges &amp; universities</subject><subject>Data science</subject><subject>Information management</subject><subject>Interdisciplinary aspects</subject><subject>Management information systems</subject><subject>Students</subject><subject>Teaching</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjLEOgjAUABsTE4nyDy9xJimvoMSNqKiz7KQpRUrIK7Zl8O9l8AOcbrjLrViEQqRJkSFuWOz9wDnHwxHzXETsVmupekMvCL2Gys7UymAsebAdXGSQ8FRGk9InKAkeFLRrjVdmGg1J94FympxdDju27uTodfzjlu2ra32-J4t-z9qHZrCzo0U1yAtRYC5SFP9VX77HOys</recordid><startdate>20151214</startdate><enddate>20151214</enddate><creator>Asamoah, Daniel</creator><creator>Doran, Derek</creator><creator>Schiller, Shu</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20151214</creationdate><title>Teaching the Foundations of Data Science: An Interdisciplinary Approach</title><author>Asamoah, Daniel ; Doran, Derek ; Schiller, Shu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_20838253123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Collaboration</topic><topic>Colleges &amp; universities</topic><topic>Data science</topic><topic>Information management</topic><topic>Interdisciplinary aspects</topic><topic>Management information systems</topic><topic>Students</topic><topic>Teaching</topic><toplevel>online_resources</toplevel><creatorcontrib>Asamoah, Daniel</creatorcontrib><creatorcontrib>Doran, Derek</creatorcontrib><creatorcontrib>Schiller, Shu</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Asamoah, Daniel</au><au>Doran, Derek</au><au>Schiller, Shu</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Teaching the Foundations of Data Science: An Interdisciplinary Approach</atitle><jtitle>arXiv.org</jtitle><date>2015-12-14</date><risdate>2015</risdate><eissn>2331-8422</eissn><abstract>The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary approach and experiences in teaching a Data Science course, the first of its kind offered at the Wright State University. Two faculty members from the Management Information Systems (MIS) and Computer Science (CS) departments designed and co-taught the course with perspectives from their previous research and teaching experiences. Students in the class had mix backgrounds with mainly MIS and CS majors. Students' learning outcomes and post course survey responses suggested that the course delivered a broad overview of data science as desired, and that students worked synergistically with those of different majors in collaborative lab assignments and in a semester long project. The interdisciplinary pedagogy helped build collaboration and create satisfaction among learners.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2015-12
issn 2331-8422
language eng
recordid cdi_proquest_journals_2083825312
source Free E- Journals
subjects Collaboration
Colleges & universities
Data science
Information management
Interdisciplinary aspects
Management information systems
Students
Teaching
title Teaching the Foundations of Data Science: An Interdisciplinary Approach
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T13%3A48%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Teaching%20the%20Foundations%20of%20Data%20Science:%20An%20Interdisciplinary%20Approach&rft.jtitle=arXiv.org&rft.au=Asamoah,%20Daniel&rft.date=2015-12-14&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2083825312%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2083825312&rft_id=info:pmid/&rfr_iscdi=true