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

dc.contributor.author Jensen, Scott
dc.date.accessioned 2016-12-29T00:27:59Z
dc.date.available 2016-12-29T00:27:59Z
dc.date.issued 2017-01-04
dc.description.abstract There 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.122
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41275
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data
dc.subject Data Science
dc.subject Data Wrangling
dc.subject Data Profiling
dc.subject Education
dc.title Introducing Data Science to Undergraduates through Big Data: Answering Questions by Wrangling and Profiling a Yelp Dataset
dc.type Conference Paper
dc.type.dcmi Text
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