How is Tableau helpful for Data scientists?
Updated on 28th Sep, 21 17 Views

Data Science is one of the hottest career paths in the 21st century and the reason for it is the growth in internet usage and data generation. Gartner estimated that every human being around the world was generating 2.5MBs of data per second in the year 2020. And this number is estimated to increase by 10MBs per day by the year 2025.

This is the reason why many companies and organizations are looking for skilled data scientists and data engineers who can efficiently handle these messy datasets and convert them into meaningful insights.

Data scientists use various data science tools, machine learning models, and scripting languages to make sense out of raw data. Among these tools, Tableau models are some of the best tools used by various companies. So, let’s discuss the tableau in detail and see how useful it is for data scientists.

Check out this free Tableau tutorial on Youtube:

Introduction to Tableau

Tableau is a collection of various Business Intelligence and data analytics tools that allows the user to collect data from varied sources in both structured and unstructured format and convert that data into visualizations and other insights.

It’s a useful tool primarily used by data scientists and business analysts for extracting valuable information from raw datasets and use it for the business.

Tableau provides different products like tableau desktop, tableau online, reader, public, and tableau server, which the companies can use as per their requirements.

While tableau desktop is used for data blending, visualization, and creating dashboards, tableau server gives you the ability to collaborate with other users and connect live data sources for real-time analysis.

Tableau also has server and data management add-ons that can be used to integrate developer tools and implement various techniques of data science in tableau.

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Tableau Features

Following are some of the best Tableau features:

  • Tableau Dashboard

Dashboard in Tableau is a collection of multiple visualizations, calculated fields, and KPIs that provides you a wholesome idea about the current market state and business performance. Here, you can add different figures and charts in a customized layout and format. Also, certain filters can be deployed by the user to create custom views.

  • Collaboration and Sharing

With the tableau server, you can share dashboards on the internet and collaborate with other users and developers to work on different projects. Here, you can instantly share data in the form of KPIs and dashboards over the internet for real-time analysis. Also, you can create a secure connection between the dashboards and hybrid data sources.

  • Data sources

Tableau allows the user to connect with a myriad of data sources such as on-premise servers, relational databases, Excel spreadsheets, MySQL servers, Salesforce, Dropbox, Hadoop, Google sheets, Amazon Athena, Google Analytics, MemSQL, and more. You can establish a secure connection with any of these data sources and create models of Data Science with tableau.

  • Advanced Visualizations

Tableau provides a wide range of visualizations using which you can create dashboards that provide more information while acquiring less space. Visualizations like bullet charts, bar charts, treemaps, bump charts, waterfall charts, scatter plots, etc.

  • Ask your Data

Tableau has a unique feature named “Ask Data” where the user can enter its queries and interact with the available data. Tableau will provide the most relevant answers in the form of text as well as visualizations. This feature can directly be integrated with your dashboards or data sources to perform natural languages analysis.

Check out the Tableau interview questions if you’re preparing for a job interview.

Is Tableau necessary for Data Science?

Tableau is used by many industries and organizations to collect large amounts of datasets and interpret meaningful information for better understanding and decision-making processes. It provides an interactive platform where users from both technical and non-technical backgrounds can analyze the data and create visualizations to gain insights into the respective departments.

Is Tableau Necessary for Data Science?

Following are the reasons that make it necessary and easier to use Tableau in Data Science:

  • On the tableau, you can add data models designed in various scripting languages like R or Python and perform complex calculations.
  • Blend visualizations in the form of dashboards and deliver insights understandably
  • Tableau provides a straightforward process for dashboard creation. Simply, drag-n-drop the variable columns and convert them into interactive visuals within seconds. Also, you can have endless variations in the visualizations through calculated fields and measures.
  • The ability to connect with different data sources makes some tools a better choice for Tableau in Data Science operations. It allows you to data in varied forms and converts that information into a single format.
  • Last but not least is the huge community that tableau has. There are millions of users and data scientists from all over the world that continuously work on projects, add resources, and share the solutions for the problems a newbie might face.

Check out this Tableau tutorial for a step-by-step guide on how to install a tableau desktop on your device and create visualizations.

Now, let’s move on to our main topic for the blog and briefly discuss the roles and responsibilities a data scientist has to fulfill. With that, it will be easier for us to understand the impact of data science using tableau in their day-to-day operations.

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Data Scientist Roles and Responsibilities

Data Scientists are the professionals who gather huge amounts of data, analyze it, and use it to create actionable plans for a business or organization. They create models by using techniques from Data Science, Statistics, Machine Learning, and other disciplines. These data models are then used to build a relationship between different variables and make predictions out of messy datasets.

To learn more about their job, let’s go through the roles and responsibilities a data scientist have to fulfill:

  • Perform data analysis on huge datasets and discover important information for the business that would otherwise be hidden.
  • Create data models using different scripting and programming languages like Python, R, Hadoop, and SQL.
  • Ask questions from datasets and find answers in the form of visualizations
  • Collaborate with clients and managers to understand the requirements and collect real-time data, which will be used for creating a business strategy
  • Create Dashboards for specific departments to check their performance and find the areas of improvement
  • Present results and recommendations in front of the managers and help them make data-driven decisions
  • Leading a team of Junior Data Scientists or Business Analysts and guiding them through the process.

Take the Tableau Server training and learn how to organize, edit, and collaborate with other users to work tableau dashboards.         

Now that you know about the responsibilities of a data scientist, let’s see how tableau will help enhance your career as a data scientist.

How does Tableau help Data Scientists?

From the above discussion, it’s clear that data scientists have to utilize multiple tools and software to perform their day-to-day operations. With software like Tableau and Data Science techniques, the implementation of various models and dashboards becomes much easier and faster, which eventually benefits the organization. This is the reason why companies look for Data Science and Tableau in the skillset of their employees and candidates.

Therefore, you must learn tableau and get hands-on experience by working on different projects. There are different ways Tableau in Data Science can help you as a data scientist. Let’s go through them one by one:

  • Works well for Exploratory Data Analysis

Exploratory Data Analysis or EDA is a critical process in Data Science. Data Scientists use it to perform the initial investigation on the datasets to discover anomalies, hidden patterns, test hypotheses and check their assumptions along with summary statistics.

It takes a lot of time and resources to perform EDA on huge datasets, and this is where it comes into play. Tableau enables you to perform different EDA tasks on a single platform while consuming fewer resources and minimal timeframes. Simply, load your datasets from different sources and perform different operations.

  • Better than Matplotlib and Seaborn Python libraries

Normally you have to write a lot of python scripts using different libraries like matplotlib. It’s a painstaking process and even the result often would not be satisfactory.

However, with tableau, you can replace those unappealing charts with informative and visually appealing figures such as bullet charts. Now, you can invest the time in other activities, which you would otherwise spend writing codes.

  • Visualize success metrics to your Data Science models

In these models, you can easily add the performance metrics through the SQL database and also add confidence score, and summarize for manual review. This way, your reports can be reviewed by subject matter experts and further improvements can be done along the way.

  • Integrates well with SQL queries

In Tableau, you can run SQL queries on static Excel/CSV files and use the reference to make anything you want. Also, you can simply paste your queries and interact with the databases that don’t necessarily require you to be live.

  • Perform Clustering

Last but not the least, Tableau enables the user to apply clustering algorithms like K-means clustering on multiple datasets without writing a single line of code.

Clustering is a great way to find similarities between different customer groups so that you can market your products and design your campaigns accordingly.

Now that you know how Tableau models and other tools can be used by a data scientist, let’s have a look at the average salary of the data scientists with tableau as their added skill.

Career Transition

Data Scientist with Tableau Skills Salary

Compared to other data analysis and visualization tools, Tableau is more popular and preferred by millions of people around the world. According to Gartner, thousands of companies including government agencies, research institutes, universities, and medium-size businesses choose tableau for solving their data problems.

Companies like Amazon, Adobe, Coca-Cola, The New York Times, Skype, Nike, The World Bank, Walmart, Cisco, etc use tableau for real-time analysis, efficiency, vivid visualizations, cloud service management, and data sharing.

Data Scientist with Tableau Skills Salary

The growing popularity of Tableau and Data Science tools leads to the rise in demand for data scientists who are comfortable with tableau and able to perform various operations on it.

Also, the salary of a tableau data scientist is more than compared to the ones having skills in other tools like Power BI. For example, PayScale shows the average salary of a data scientist with Power BI skills is US$62,079, and for the ones with tableau skills is US$87,577.

So, learning Tableau will instantly benefit you in terms of salary hike or getting a job with a handsome salary.

Now, let’s see the average salary of a Data Scientist who uses Tableau and Data Science in different countries based on their experience as per the PayScale report.

  • Data Scientist with Tableau Skills Salary: United States
Experience LevelSalary
Entry-level (less than 1 year)US$77,000
Early-career (1–4 years)US$87,000
Mid-career (5–9 years)US$1,03,000
Late-career (10–19 years)US$1,05,000
Experienced (20+ years)US$1,42,000
  • Data Scientist Salary: United Kingdom
Experience LevelSalary
Entry-level (less than 1 year)£34,000
Early-career (1–4 years)£41,000
Mid-career (5–9 years)£49,000
Late-career (10–19 years)£51,000
Experienced (20+ years)£53,000
  • Data Scientist salary: India
Experience LevelSalary
Entry-level (less than 1 year)₹5,40,000
Early-career (1–4 years)₹7,86,000
Mid-career (5–9 years)₹1,392,000
Late-career (10–19 years)₹1,747,800
Experienced (20+ years)₹2,100,000

Conclusion

Becoming a data scientist requires a lot of skills, time, and practice through various projects and certifications. But, you can make this journey a bit smoother through smart choices and picking up the right skills like a tableau. Tableau has a number of features that saves both your time and resources. No matter if you’re a newbie or an expert data scientist, tableau will surely help you both technically and financially by providing you with better career opportunities.

Check out our Data Science community and learn more about data visualization, KPIs, and other concepts.

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