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Choosing Among Computational Software Tools to Enhance Learning in Introductory Business Statistics
Author(s) -
Johnson Marina E.,
Berenson Mark L.
Publication year - 2019
Publication title -
decision sciences journal of innovative education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 19
eISSN - 1540-4609
pISSN - 1540-4595
DOI - 10.1111/dsji.12186
Subject(s) - business statistics , computer science , business analytics , analytics , data science , learning analytics , curriculum , business intelligence , software analytics , software , set (abstract data type) , subject (documents) , big data , statistics education , knowledge management , mathematics education , software development , business analysis , world wide web , statistics , data mining , management , business model , psychology , software development process , mathematics , pedagogy , economics , programming language
AACSB has now mandated that analytics be integrated into the undergraduate business curriculum. Given that the subject of statistics provides the underpinnings of the developing discipline of business analytics, this article focuses on effective course delivery aimed at enhancing the learning of introductory business statistics in the modern world of big data. To this end, the choice and use of computational software tools are essential to the successful delivery of the course. A set of seven competing tools are compared and contrasted, their advantages and disadvantages are discussed, and a link is provided to show demonstrations of needed instructions and resulting output.