Tableau for Data Scientists

When your data needs to get dressed up, Tableau is a fool-proof style service. It offers a sleek, drag-and-drop interface for data analytics with native integration to pull data from CSVs, JSON files, Google Sheets, SQL databases, and that back corner of the dryer where you’ve inevitably forgotten a sock.

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Tableau worksheet with dimensions in blue and measures in green. Sidebar at far left shows out-of-the-box analytics tools for basic summary statistics. via Tableau.

Data is automatically separated into dimensions (qualitative) and measures (quantitative) — and presumed to be ready for chart-making. Of course, if there are still a few data cleaning steps to be undertaken, Tableau can handle the dirty laundry as well. For example, it supports re-formatting data types and pivoting data from wide to tall format.

When ready to make a chart, simply ctrl+click features of interest and an option from the “Show me” box of defaults. This simplicity of interaction enables even the most design-impaired data scientist to easily marshal data into a presentable format. Tableau will put your data into a suit and tie and send it to the boardroom.


Follow these tips to go from “good” to “great” in your data visualization abilities

#1 — Sheets are the artist’s canvas and dashboards are the gallery wall. Sheets are for creating the artwork (ahem, charts), which you will then position onto a dashboard (using a tiled layout with containers — more on this in a second) along with any formatting elements.

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