Introducing Data Science Techniques by Connecting Database Concepts and dplyr
Early exposure to data science skills, such as relational databases, is essential for students in statistics as well as many other disciplines in an increasingly data driven society. The goal of the presented pedagogy is to introduce undergraduate students to fundamental database concepts and to ill...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-09-01
|
| Series: | Journal of Statistics Education |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10691898.2019.1647768 |
| _version_ | 1818336692066582528 |
|---|---|
| author | Jennifer E. Broatch Suzanne Dietrich Don Goelman |
| author_facet | Jennifer E. Broatch Suzanne Dietrich Don Goelman |
| author_sort | Jennifer E. Broatch |
| collection | DOAJ |
| description | Early exposure to data science skills, such as relational databases, is essential for students in statistics as well as many other disciplines in an increasingly data driven society. The goal of the presented pedagogy is to introduce undergraduate students to fundamental database concepts and to illuminate the connection between these database concepts and the functionality provided by the dplyr package for R. Specifically, students are introduced to relational database concepts using visualizations that are specifically designed for students with no data science or computing background. These educational tools, which are freely available on the Web, engage students in the learning process through a dynamic presentation that gently introduces relational databases and how to ask questions of data stored in a relational database. The visualizations are specifically designed for self-study by students, including a formative self-assessment feature. Students are then assigned a corresponding statistics lesson to utilize statistical software in R within the dplyr framework and to emphasize the need for these database skills. This article describes a pilot experience of introducing this pedagogy into a calculus-based introductory statistics course for mathematics and statistics majors, and provides a brief evaluation of the student perspective of the experience. Supplementary materials for this article are available online. |
| first_indexed | 2024-12-13T14:43:21Z |
| format | Article |
| id | doaj.art-03aa5013afe54f7b99cefe67a4f7368a |
| institution | Directory Open Access Journal |
| issn | 1069-1898 |
| language | English |
| last_indexed | 2024-12-13T14:43:21Z |
| publishDate | 2019-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Statistics Education |
| spelling | doaj.art-03aa5013afe54f7b99cefe67a4f7368a2022-12-21T23:41:32ZengTaylor & Francis GroupJournal of Statistics Education1069-18982019-09-0127314715310.1080/10691898.2019.16477681647768Introducing Data Science Techniques by Connecting Database Concepts and dplyrJennifer E. Broatch0Suzanne Dietrich1Don Goelman2Arizona State UniversityArizona State UniversityVillanova UniversityEarly exposure to data science skills, such as relational databases, is essential for students in statistics as well as many other disciplines in an increasingly data driven society. The goal of the presented pedagogy is to introduce undergraduate students to fundamental database concepts and to illuminate the connection between these database concepts and the functionality provided by the dplyr package for R. Specifically, students are introduced to relational database concepts using visualizations that are specifically designed for students with no data science or computing background. These educational tools, which are freely available on the Web, engage students in the learning process through a dynamic presentation that gently introduces relational databases and how to ask questions of data stored in a relational database. The visualizations are specifically designed for self-study by students, including a formative self-assessment feature. Students are then assigned a corresponding statistics lesson to utilize statistical software in R within the dplyr framework and to emphasize the need for these database skills. This article describes a pilot experience of introducing this pedagogy into a calculus-based introductory statistics course for mathematics and statistics majors, and provides a brief evaluation of the student perspective of the experience. Supplementary materials for this article are available online.http://dx.doi.org/10.1080/10691898.2019.1647768data sciencedatabaseseducationteaching tool |
| spellingShingle | Jennifer E. Broatch Suzanne Dietrich Don Goelman Introducing Data Science Techniques by Connecting Database Concepts and dplyr Journal of Statistics Education data science databases education teaching tool |
| title | Introducing Data Science Techniques by Connecting Database Concepts and dplyr |
| title_full | Introducing Data Science Techniques by Connecting Database Concepts and dplyr |
| title_fullStr | Introducing Data Science Techniques by Connecting Database Concepts and dplyr |
| title_full_unstemmed | Introducing Data Science Techniques by Connecting Database Concepts and dplyr |
| title_short | Introducing Data Science Techniques by Connecting Database Concepts and dplyr |
| title_sort | introducing data science techniques by connecting database concepts and dplyr |
| topic | data science databases education teaching tool |
| url | http://dx.doi.org/10.1080/10691898.2019.1647768 |
| work_keys_str_mv | AT jenniferebroatch introducingdatasciencetechniquesbyconnectingdatabaseconceptsanddplyr AT suzannedietrich introducingdatasciencetechniquesbyconnectingdatabaseconceptsanddplyr AT dongoelman introducingdatasciencetechniquesbyconnectingdatabaseconceptsanddplyr |