Ultimate Guide to Tableau Data Science: 6 Critical Features

on BI Tool, Data Visualization, Tableau • July 6th, 2021 • Write for Hevo

In the 21st century, decisions are being driven by data at a staggering rate every day as compared to the simpler times before. Even for everyday tasks, data is starting to play a significant role and is probably one of the reasons why data is now the most valuable resource in the world. 

While Chandler Bing from Friends did the same job back in the ‘90s, it was a mystery what he did. Today, that job and the people are called Data Science and Data Scientists respectively. Due to these wizards, even the most mundane Nano byte streams of data can make sense and drive insightful decisions.

One of the ways that decisions have become insightful is via visualizations of critical metrics and data. And in today’s time, when we have tons of data to work around, Data Visualization has become even more imperative and one of the best tools in the business for visualization is Tableau. So, Let’s find out more about Tableau Data Science.

Table of Contents

Prerequisites 

One of the most significant advantages of Tableau is that there is no prerequisite knowledge or tools required to learn it. Whether you are a novice or an amateur in data science, learning and using Tableau will be a significant asset in the long run. This is also one reason why it is a hot topic in today’s time for career advancement.

Understanding Tableau

Tableau Data Science
Image Source: UIS

Tableau is a Business Intelligence and Data Visualization stage established in 2003 by Christian Chabot, Pat Hanrahan, and Chris Stolte. It turned out to be tremendously famous as every business needed to assemble important insights from numerous data sources and at the same time collaborate with all the employees of the organization. Visualization is an extraordinary method to analyze colossal amounts of data and that is actually what Tableau does.

Tableau has helped driving associations across ventures cut down their processing time and make their business more data-driven while guaranteeing flexibility, security, and reliability across the entirety of their cycles.

Steps to Install Tableau

Steps to install Tableau Server
Image Source: Educba

Tableau is available in two versions, Desktop and Public. The Public is the free and open-source version, while Desktop is the premium licensed version available from $35 onwards/month. Before investing any money, it is best to get the Public version first and try your hand out at data visualizations. 

The following are the steps to install Tableau Public:

  1. Go to https://public.tableau.com/en-us/s/download and enter your email ID in the “Download The App” bar. 
  2. Once you press it, a .exe install application file will be downloaded. 
  3. Open the file, accept the terms and conditions and click install. 
  4. Once installation is complete, Tableau will open automatically.

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Understanding the Features of Tableau Data Science

While it is often said that Tableau is not helpful for a data scientist as it is just a data visualization tool, the reality is far from it. In its true sense, Tableau can make many things more manageable for a data scientist, not just process-wise but also in decision making. The following are the prime uses of Tableau Data Science:

1. Tableau Data Science: Quick and Simple to Use

One of the most significant benefits for data scientists is the easy-to-use nature of Tableau. If the data set or the CSV file is ready, the dashboard creation becomes relatively straightforward. Furthermore, you can have different charts, maps, and visuals in other sheets as per your requirements and convenience. 

Simple features like drag and drop for creating visuals are assets that every data scientist wants after writing countless lines of code in Python or R. 

2. Tableau Data Science: Generating Advanced Graphs

Generating Advanced Graphs in Tableau
Image Source: Analytics Vidhya

As stated before, Tableau is a data visualization tool. And for data scientists, using graphs, charts, and maps is imperative to understand what the data represents. For this purpose, they can generate various kinds of advanced graphs. Some of the most common ones are:

  • Motion Chart: This is an animated line graph that can be used strategically to show any particular parameter’s rise and fall. 
  • Bump Chart: A bump chart comes into play when you want the line graph to become more precise in the data it represents. The primary usage of the bump chart is for segment examination for the popularity of a particular product over some time against some other parameter. 
  • Doughnut Chart: A glorified yet quirky representation of a pie chart, doughnut charts make the picture more straightforward to read than a pie chart due to the hole in the middle.
  • Waterfall Chart: Another way to make a line chart even more detailed and informative is using a waterfall chart. This chart represents the increase and drip in the particular trend, making it easier to track any anomaly or progress. 
  • Pareto Chart: One of the biggest areas where data scientists work is risk management and reduction. While actuaries do this in the financial field, data scientists do it in any other field. And this is mainly where Pareto charts come into play. When you want to analyze an area of business according to the 80-20 principle, the best way to understand it is via Pareto charts.

3. Tableau Data Science: Seamless Integration with Data Sources

Tableau has become the go-to choice for many individuals because of the integration and ease of access. Some of the best tools that it seamlessly integrates with are:

  • Excel: Being a data processor, MS Excel is one of the go-to choices for spreadsheets for a data scientist. Tableau can easily integrate with MS Excel, understand and analyze the data, and generate simple-to-read visualizations. 
  • Raw text files: Sometimes, the data is provided in TXT format and not in the standard CSV format. Fret not; Tableau can make sense of that and connect the text file with it. 
  • Access: If you have any file stored in MS Access, you can directly link it to Tableau by going to MS Access through the dashboard. The only data types it won’t connect to are OBE Object and Hyperlink.
  • Hadoop: When you are analyzing and computing data using multiple computers, Hadoop comes in handy. And to generate a visual from all the data from various access points, Tableau can help out via accessing Hadoop directly if you have the 7.0 version of it. 
  • Amazon EMR: Amazon has its own Hadoop-based cloud server to compute and analyze data called the Elastic MapReduce (EMR). Linking that to Tableau is similar to how you can connect Hadoop. 
  • SQL Server: Another tool similar to MS Excel for data sets, and the primary choice for database systems is SQL. Earlier, data scientists needed to write complex code in SQL first, but with VizQL in Tableau, you can now use the same features easily.
  • Salesforce: Being a Salesforce product, Tableau can easily integrate and work with any product and data source from the CRM software to help generate insightful decisions. 
  • Any ODBC-compliant database: One of the oldest and most basic ways of working with Tableau is using Open Database Connectivity (ODBC).
  • R and Python: With this integration, associated libraries, packages, and saved data models in R can be imported very easily. Integrating with Python, you get TabPy which is a framework that allows you to access and execute Python code remotely for data cleaning and predictive algorithms.
  • Matlab: If you use Matlab to generate models, they can be imported directly into Tableau for further processing and visualization.

4. Tableau Data Science: ML Algorithms (k-means)

ML Algorithms (k-means)
Image Source: Javatpoint

One of the most extensive algorithms in Machine Learning is clustering, also called the k-means. The purpose of k-means is to find patterns in the data set by grouping similar data together. This grouping includes techniques like a within-group sum of squares (WGSS) and a between-group sum of squares (BGSS).

When you import data into Tableau, isolate the variables from the columns and rows and perform the clustering. The dashboard offers you the choice of building clusters based on metrics or forcing them manually. The end visual result will be clusters that are correctly labeled, interactive and color-coded for easy understanding. 

5. Tableau Data Science: Great for Visualizations with Exploratory Data Analysis with Success Metrics

Sometimes, you don’t want to write the Python code for the data set and form a visual model from the preliminary data. This is called exploratory data analysis (EDA) and an undermined technique in data science. 

It is also one of the precursors that helps determine the success or failure of a model along with metrics. Tableau comes with EDA capability and enables you to understand the preliminary success rate of a model. 

6. Tableau Data Science: Better than Matplotlib and Seaborn Libraries in Python

Though data scientists are comfortable with the crude charts produced by Python, a better way to get them is via Tableau. The overall layout and the presentation are much better with Tableau. Hence, it is easy to say that it is better than Matplotlib and Seaborn libraries which need long codes.

Conclusion

You might think that Tableau may be a mere data visualization tool, but it is so much more in reality. It can seamlessly interlink with many tools for data science activities and decipher a lot of information with its visual proclivity. 

In conclusion, it is safe to say that Tableau is a handy tool for Data Scientists that have helped them thus far and will continue to do so in the future. Integrating and analyzing your data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. 

Hevo is a No-code Data Pipeline and has awesome 100+ pre-built integrations that you can choose from. Hevo can help you integrate your data from numerous sources and load them into a destination to analyze real-time data with a BI tool and create your Dashboards. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure. Check out the pricing details here. Try Hevo by signing up for a 14-day free trial and see the difference!

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