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 of the 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.
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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 to extract valuable information from raw datasets and use it for the business.
Tableau provides different products like Tableau Desktop, Tableau Server, Tableau Online, and Tableau Prep, 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.
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.
Following are the reasons that make Tableau necessary and easy to use in data science:
- On 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 and 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 save data in varied forms and converts that information into a single format.
- Last but not least Tableau has a huge community. There are millions of users and data scientists from all over the world who continuously work on projects, add resources, and share the solutions for the problems a newbie might face.
How does Tableau help Data Scientists?
Today, data scientists have a lot of different tools that they use to process data. Tableau is just one of those tools. Since there are so many different areas in Data Science, there are a lot of different tools that are used together for data analysis. Similarly, Tableau is used along with different tools.
Tableau is immensely important when it comes to communication. Data scientists have to usually break the information down to make it easier to understand for the teams, colleagues, executives, and customers. In these instances, the data has to be easy to view and understand.
Tableau helps teams dig deeper into data, uncover insights that are usually hidden, and then visualize that data in an appealing, easy-to-understand way. Tableau also helps data scientists to explore the data quickly, adding and deleting on the way so that the end result is an interactive visualization of everything that is important.
Tableau’s ability to connect to various data sources is another help that it provides to data scientists. It can connect to relational database management systems like Oracle or SQL Server, cloud systems like Azure or Google BigQuery, and basic files like CSV and Excel.
Advantages of Tableau for Data Science
- Tableau saves up a lot of time for data scientists by generating appealing visualizations in lesser time and without coding.
- Exploratory Data Analysis or EDA is important for data science processes. A Data Scientist needs to be able to quickly visualize the data they’re dealing with before creating the model, and Tableau helps with that.
- Tableau is a great help for summarizing success metrics. If your results, score, etc. are stored in an SQL database, then Tableau can quickly visualize your model metrics easily.
- Tableau integrates really well with SQL Queries.
Disadvantages of Tableau for Data Science
- Automation and integration are really important for data scientists. Unfortunately, Tableau doesn’t have an integration feature. While data Scientists will be automating all their processes and trying to integrate all of them on a single platform so that they can refer to them later, Tableau’s visualization cannot be integrated into the platform.
- Tableau tends to be slow at times when you have too many tabs or sheets open simultaneously.
- Tableau has its own limitations when it comes to data science applications as it is a data analysis tool rather than a data science tool.
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Implementation Of Tableau in Data Science
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 scores, 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.
Last but not 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.
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-sized 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.
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 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 a 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 in the United States
| Experience Level | Salary |
| 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 in United Kingdom
| Experience Level | Salary |
| 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 in India
| Experience Level | Salary |
| Entry-level (less than 1 year) | ₹5,40,000 |
| Early-career (1–4 years) | ₹7,86,000 |
| Mid-career (5–9 years) | ₹13,92,000 |
| Late-career (10–19 years) | ₹17,47,800 |
| Experienced (20+ years) | ₹21,00,000 |
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 save 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.