Benefits of Tableau for Data Scientists

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This post was originally published by Matt Przybyla at Towards Data Science

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Photo by Kaitlyn Baker on Unsplash [1].

Table of Contents

  1. Quick and Simple
  2. SQL, R, Python, and MATLAB
  3. k-means Algorithm
  4. Cross-functional
  5. Summary
  6. References

Introduction

Quick and Simple

SQL, R, Python, and MATLAB

As a Data Scientist, you will become familiar with SQL, or structured query language. In order to develop your dataset for your Data Science model, you will need to query from a database using SQL (most likely). Tableau makes it easy to connect to your current SQL database so that you can perform the query within Tableau and then develop reports from there. For Data Science, this feature can be useful when you need to do the following:

  • visualize exploratory data analysis
  • visualize model metrics

Integrations

— R Integration

You can import R packages as well as their associated libraries. More powerful, you can also import your saved data models into Tableau.

— Python

Also known as TabPy, this integration of Tableau allows users to use a framework that can remotely execute Python code. Some of the main usages are for data cleaning and predictive algorithms (and the use of calculated fields). Here is a useful link for TabPy [3]:

tableau/TabPy

Execute Python code on the fly and display results in Tableau visualizations: – tableau/TabPy

github.com

— MATLAB

For deploying models from MATLAB, you can utilize this integration. It includes use in predictive insights, as well as preprocessing data.

Of course, all of these integrations and languages can be used for data analysis as part of your Data Science process. Additionally, the results you develop from your predictive models can be displayed in Tableau.

k-means Algorithm

Since Tableau focuses on visualization, your clusters will be well labeled, interactive, and colorized for easy viewing and easier understanding.

The benefit of utilizing this popular algorthim in Tableau is that it performs it fairly quickly and does not require any code on your part.

Here is a link that describes clustering in Tableau in more detail [4]:

Find Clusters in Data

Cluster analysis partitions marks in the view into clusters, where the marks within each cluster are more similar to…

help.tableau.com

Cross-functional

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Photo by Campaign Creators on Unsplash [5].

Perhaps the most important benefit of Tableau for Data Scientists is that you can use the tool for its main function — to share data visually. As a Data Scientist, you may encounter different types of people in different types of departments within your company. It is your job to be able to explain your complex Machine Learning algorthim and its respective results to others. One of the best ways to do that is to visualize it. You can use any of the charts to describe your model results — whether you made improvements to the business, which groups of your data performed better, etc. There are countless use cases of Tableau in Data Science. Here are some of the advantages of Tableau in Data Science.

  • lots of people use Tableau so sharing visualizations will be easy
  • lots of companies so it’s good to know in general
  • you can turn your complex model into an easy-to-read visual

Summary

To sum, here are the benefits laid out and summarized:

Quick and SimpleSQL, R, Python, and MATLABk-means AlgorithmCross-functional

Thank you for reading! I appreciate it. Please feel free to comment down below and write about your experience with Tableau as any user or as a Data Scientist.

References

[2] TABLEAU SOFTWARE, LLC, A SALESFORCE COMPANY, Tableau homepage, (2003–2020)

[3] TABLEAU SOFTWARE, LLC, A SALESFORCE COMPANY, TabPy, (2003–2020)

[4] TABLEAU SOFTWARE, LLC, A SALESFORCE COMPANY, Find Clusters in Data, (2003–2020)

[5] Photo by Campaign Creators on Unsplash, (2018)

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This post was originally published by Matt Przybyla at Towards Data Science

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