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Department or Administrative Unit

Educational Foundations and Curriculum

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Aside from statistics courses, accessible data analytics skills are often excluded from traditional non-technical university programs. These are topics that are typically the domain of programs that focus on math, statistics and computer science. Yet the need for these skills in non-technical disciplines is changing. A rapid expansion of data-related processes in organizations of many types requires individuals who have at least a working knowledge of common analytic tools. This article briefly describes three categories of data analytics tools that can be useful for graduates in any discipline. The first category covers descriptive tools that allow students to learn what is in a data set and what meaning can be made of it. The second category of tools teaches students how to predict likely outcomes based on relationships in past data. The final category introduces students to tools that allow them to segment data into useful clusters and classes and to build meaningful associations within the data.


This article was originally published Open Access in International Conference on Life Sciences, Engineering and Technology. The conference proceedings from the publisher can be found here.


International Conference on Life Sciences, Engineering and Technology

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.