Introducing Data Science to Undergraduates through Big Data: Answering Questions by Wrangling and Profiling a Yelp Dataset

dc.contributor.authorJensen, Scott
dc.date.accessioned2016-12-29T00:27:59Z
dc.date.available2016-12-29T00:27:59Z
dc.date.issued2017-01-04
dc.description.abstractThere is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist’s work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper we present hands-on exercises to introduce Big Data to undergraduate MIS students using the CoNVO Framework and Big Data tools to scope a data problem and then wrangle the data to answer questions using a real world dataset. This can provide undergraduates with a single course introduction to an important aspect of data science.
dc.format.extent10 pages
dc.identifier.doihttps://doi.org/10.24251/HICSS.2017.122
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41275
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data
dc.subjectData Science
dc.subjectData Wrangling
dc.subjectData Profiling
dc.subjectEducation
dc.titleIntroducing Data Science to Undergraduates through Big Data: Answering Questions by Wrangling and Profiling a Yelp Dataset
dc.typeConference Paper
dc.type.dcmiText

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