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A Comparison of Key Concepts in Data Analytics and Data Science

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Title: A Comparison of Key Concepts in Data Analytics and Data Science
Language: English
Authors: McMaster, Kirby, Rague, Brian, Wolthuis, Stuart L., Sambasivam, Samuel
Source: Information Systems Education Journal. Feb 2018 16(1):33-40.
Availability: Information Systems and Computing Academic Professionals. Box 488, Wrightsville Beach, NC 28480. e-mail: publisher@isedj.org; Web site: http://isedj.org
Peer Reviewed: Y
Page Count: 8
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Information Analyses
Descriptors: Data Analysis, Data Collection, Comparative Analysis, Statistics, Word Frequency, Mathematical Concepts, Probability
ISSN: 1545-679X
Abstract: This research study provides an examination of the relatively new fields of Data Analytics and Data Science. We compare word rates in Data Analytics and Data Science documents to determine which concepts are mentioned most often. The most frequent concept in both fields is "data." The word rate for "data" is more than twice the next highest word rate, which is for "model." This contrasts sharply with how often the word "data" appears in most Mathematics books. Overall, we observed substantial agreement on important concepts in Data Analysis and Data Science. Eighteen of the 25 most frequent concepts are shared by both fields. One difference is that the words "problem" and "solution" had Top 25 word rates for Data Science, but not for Data Analytics. A close look at Statistics concepts suggests that Data Analytics is more focused on "exploratory" concerns, such as searching for patterns in data. Data Science retains more of the classical inferential activities that use sample data to draw conclusions about populations. Both fields deal with Big Data situations, but Data Scientists must continue to be prepared for traditional small sample applications.
Abstractor: As Provided
Number of References: 17
Entry Date: 2018
Accession Number: EJ1173725
Database: ERIC
FullText Text:
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  Data: This research study provides an examination of the relatively new fields of Data Analytics and Data Science. We compare word rates in Data Analytics and Data Science documents to determine which concepts are mentioned most often. The most frequent concept in both fields is "data." The word rate for "data" is more than twice the next highest word rate, which is for "model." This contrasts sharply with how often the word "data" appears in most Mathematics books. Overall, we observed substantial agreement on important concepts in Data Analysis and Data Science. Eighteen of the 25 most frequent concepts are shared by both fields. One difference is that the words "problem" and "solution" had Top 25 word rates for Data Science, but not for Data Analytics. A close look at Statistics concepts suggests that Data Analytics is more focused on "exploratory" concerns, such as searching for patterns in data. Data Science retains more of the classical inferential activities that use sample data to draw conclusions about populations. Both fields deal with Big Data situations, but Data Scientists must continue to be prepared for traditional small sample applications.
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      – SubjectFull: Comparative Analysis
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      – SubjectFull: Word Frequency
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      – TitleFull: A Comparison of Key Concepts in Data Analytics and Data Science
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