arbiter
JournalsPricingAccount

Teaching Data Joins: A Conceptual Approach Using SQL, Alteryx, and Tableau

Jie Li & Lorraine Lee

AIS Educator Journal2021https://doi.org/10.3194/1935-8156-16.1.60article
ABDC C
Weight
0.45

What the paper says

Abstract The many ways of joining data can be confusing for beginning, or even intermediate, data analytics students. Several accounting information system textbooks cover SQL and joins, but few supplemental resources exist that can reinforce and clarify join concepts. In this paper, we use a multi-tool approach in a three-part assignment for teaching data joins using three different software tools. We use four different join types (INNER JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN, and FULL OUTER JOIN) in Microsoft Access, Alteryx, and Tableau. We further demonstrate how the Tableau data model, using separate logical and physical layers, implements the various join types depending on the data needs of a particular visualization. Overall, thus three-part assignment can help students develop a comprehensive understanding of joins and how to implement them in widely-used software tools. The assignment also gives students practice in selecting, using, and validating joins in data analytics.

4 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.3194/1935-8156-16.1.60

Or copy a formatted citation

@article{jie2021,
  title        = {{Teaching Data Joins: A Conceptual Approach Using SQL, Alteryx, and Tableau}},
  author       = {Jie Li & Lorraine Lee},
  journal      = {AIS Educator Journal},
  year         = {2021},
  doi          = {https://doi.org/https://doi.org/10.3194/1935-8156-16.1.60},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Teaching Data Joins: A Conceptual Approach Using SQL, Alteryx, and Tableau

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.45

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.26 × 0.4 = 0.10
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.