Can You Use Tableau for Data Analysis?

As part of an analyst’s work, data is gathered and presented to stakeholders in a manner that makes sense to the business, be it to explain the past, predict future trends, or show a current data representation based on facts. However, when you look at such insightful charts that are easy to understand and to interpret, data visualization software is usually the key to gathering and presenting meaningful information. So, can you use Tableau for data analysis?

You can use Tableau for data analysis. It is the industry-standard data visualization software and is commonly used in the field of data science and analytics. The user-friendly drag and drop interface easily enables anyone to make data queries, organize information, and understand outcomes. 

Consequently, this article defines Tableau, its robust history, and its current organization. Also considered are the advantages and disadvantages of Tableau and other software options for the data analyst. Finally, in order to add value to your data analytics career, you will learn how to gain expertise in Tableau via resources, training, and certification.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Is Tableau?

Gathering business intelligence allows organizations to turn data into something insightful and actionable to improve products, services, and customer experience. Ultimately it is used to answer questions like:

  • How do I know my customers are happy?
  • Demographically, who are my customers?
  • What is the best channel to target customers?
  • What is my product life cycle?
  • What are market trends?
  • What is my five-year forecast?

Tableau can answer these questions because you will use it to mine data and visualize answers to business questions. A list of Tableau products and an overview are:

  • Tableau Desktop is where all development is accomplished, including, for instance, building dashboards.
  • Tableau Public is a free, scaled-down version of the desktop.
  • Tableau Prep Builder enables you to prepare the data for analysis visually before setting up connections.
  • Tableau Online is a cloud-based version of the server that can be shared securely.
  • Tableau Server is the on-premise server.
  • Tableau Vizable is a consumer data visualization mobile application.

As a data visualization software tool, Tableau is user friendly and enables you to aggregate data and weave a captivating picture together. Quickly and clearly getting the point across is crucial, especially when presenting impactful information to large groups or C-suite executives. For example, data may be used to visualize market trends, affecting which direction the company moves toward. 

Microsoft Excel is commonly used to manipulate data and analyze results. While Excel has its purpose, it is not quick or powerful enough to handle huge datasets; therefore, additional items should be added to your toolbox. But knowing how to build pivot tables in Excel prepares you for the basis of Tableau.

History of Tableau

Previously, the job of the Information Technology department was to create reports and analyze data for the company. But that changed in 2003 when three Stanford University students collaborated to develop new analytics software to make data more accessible and understandable. 

The co-founders are currently still involved with the company: Christian Chabot is the Technical Advisor, Pat Hanrahan is the Chief Scientist, and Chris Stolte is the Chairman. The company originated in Mountain View, California, but is currently headquartered in Seattle, Washington.

Microsoft Excel was at the forefront in the marketplace back in 2003, but Tableau reinvigorated the concept by incorporating much more powerful visualization features in databases without limitations, making it easier to digest and spit out meaningful data. Although IT professionals did not immediately see the value, marketing and business professionals certainly did, and Tableau has grown exponentially over the years to come. 

Salesforce acquired Tableau in June 2019, continuing the mission to improve organizations with data. Data transparency instills trust and confidence. Business decisions can be challenged with data and thereby transformed. 

The Tableau Foundation aligns with non-profit partners to improve conditions throughout the world. The areas of focus, also known as issue areas, are monumental in category size as listed here:

  • Climate
  • Human rights  
  • Global Health
  • Poverty

Within these categories are 22 featured programs; however, the foundation stresses the importance of contributing to the people’s welfare behind the data.

Skills Needed to Get Started in Tableau

Data Preparation

Preparing data to make connections is often a job for the data analyst and may take some strategy. Luckily Tableau makes it easy. For example, some common duties performed are to join tables for better performance. 

If data lives in multiple sources and the data sources are connected by a similar field, a data analyst blends data onto one single worksheet when it is coming from more than one database. You can also perform a data extract by saving subsets of data to one source to improve performance. 

Connections and Dashboards

The analyst sets up live connections to receive constant or regular updates. Dashboards are created to make it easy to disburse updates to others in your company, tapping into Tableau’s computational power. For an intuitive dashboard, it’s important to structure the user view from left to right, top to bottom, in the same way, that we read. 

The important charts, graphs, and information are at the upper left of the screen, while reference material or information of lesser importance would be showcased on the bottom right. Make sure it is not too cluttered for readability which is easily accomplished with Tableaus drag and drop interface.

Learn how to format a dashboard in this twenty-minute video, an excellent introduction for beginners which will get you excited to get started:


The data can be extracted and stored in order to create a snapshot in time. Without being connected to the database or live data, you can play around with the data, creating visualizations with extracts. Incorporate dashboards to tell a broader story. 

Note that charts and graphs created on a dashboard in a financial setting may not work in other industries or job roles. Possibly your data analysis is affected by geographic location. Therefore, what is applicable to one organization may certainly not be in another, but Tableau is flexible and variable across all channels.

For example, as a project manager, you may want to display a project timeline in a different manner. Find step by step instructions, as in a blog post entitled, “Creating a Tableau Calendar From Project Data,” complete with screenshots. 

Industry-Standard Programming Software


Whereas knowing how to code is not necessary, it is advantageous to your career to also know how to program. Python is a highly popular and easy to learn, open-source (free) scripted computer programming language. Since it can be used to handle massive datasets, is extremely powerful, has a large community of users, and has been in existence for over 30 years, it is commonly used in conjunction with Tableau.

TabPy is Tableau + Python

TabPy is a combination of the Python computer programming language that integrates with Tableau to define fields in Python and pull them into the results of Tableau, powerfully enabling the ability to visualize data. 

Install and use a Python library in your TabPy scripts. An example of using TabPy is using it to cluster tornado origins. In this instance, a calculated field is dynamically run on the TabPy server, which results in returning the data back to Tableau in real-time. 

GitHub is a resource in which millions of developers contribute to one repository to build software and review code together. TabPy is an active community and, therefore, an excellent resource for reading documentation, troubleshooting, and tapping into other technical information and resources. 

TabPy community is a forum for all questions not related to technical questions, connects to others for inspiration, and develops best practices. Special interest groups exist in large numbers, for instance:

  • Data and Women
  • Newbies
  • Higher Education

You may also join the weekly Makeover Monday social data project in which knowledgeable people aid in the discussion of a variety of projects. Also, as a community member, exercise your right to nominate a Zen Master, who has mastered the product and has been a stellar collaborator, willing to share expertise and suggest improvements. 

In summary, Python and Tableau work well together to communicate a compelling story to your stakeholders, which is valuable to you as a data analyst. 

Tableau Resources

Tableau interview questions are technical or conceptual instead of surface-level questions about what changed from one software version to the next. In this one hour video, you will gain enough functional knowledge to answer Tableau questions:

Start from the beginning and discover all about Business Intelligence and everything from installing Tableau to building donut charts in this three-hour video course:

As a newbie, do you need help with the terminology or the commands? Take a look at one of the many Tableau desktop Cheat Sheets available on the Internet.

If you want to get certified as a way to boost your resume, take the test online after learning the skills you need by viewing instructor-led or self-paced training, reading books, taking practice exams, and putting in the time to apply yourself to understanding the software. Although relevant and coveted, computer programming skills are not necessary, just a good understanding of data and an aptitude for analysis. 

  • Tableau Desktop Associate exam is for individuals with foundational skills and at least three months of functional experience. 
  • Tableau Desktop Certified Associate exam is for users with comprehensive knowledge of the product’s functionality for at least five months.
  • Tableau Desktop Certified Professional exam is for the person with advanced skills and at least one year of experience. 
  • Tableau Server Certified Associate exam is for systems administrators or consultants with four to six months of experience. Although no prerequisites are required, a person should have a comprehensive knowledge of the Tableau Server. 
  • Tableau Server Certified Professional exam is for the individual with at least nine months of experience and has a valid Tableau Server Certified Associate certification.

Associates hold an active certification for two years, and professionals are active for three years. To stay active, before expiration, a new exam must be taken and passed. There are many free resources, such as videos, preparation guides, and practice exams. Many companies purport to offer, but employers are looking for original Tableau sanctioned certification. 

A 14-day free trial of Tableau is offered by filling out an online form. This is an excellent way to get a feel for it and decide if you want to pursue learning more if you don’t have any experience with Tableau yet.

Students receive a one year license, for free, if actively taking classes at an accredited college. Use it at home, for class, or on social media to create beautiful creations. The Marvel Social Network is a fantastic example of a comprehensive and visually appealing way to tell a story. If you need help, read the Tableau for Students’ Frequently Asked Questions.

Data Science and Analytics

Data science has evolved in conjunction with technology because of how computer science plays an integral part in processing and analyzing big data. Machines can be trained with a data-driven approach.  

By broad definition, data science is almost everything that pertains to data. Furthermore, it is a combination of computing power and data mining. A data scientist solves company issues by using data. The data scientist may need to perform all of these skills in a small company; therefore, experience with a data visualization program is essential. 

In a large company, skill sets are specialized, and the data analyst would also need to create data visualizations.

  • Define the problem
  • Acquire data
  • Store data, data warehousing
  • Clean and transform
  • Analyze, model, and visualize
  • Experiment
  • Determine resolutions
  • Monitor resolution in production

Consequently, data science as a field most definitely supports the needs of the business to manage and gather insight from the massive amounts of data in a very short amount of time, no matter which position you are in.

Big Data

As a field of study, Big Data refers to extracts and data handling far too complex for traditional databases. Within the Big Data realm, predictive analytics techniques are used to gather historical data and statistically forecast future unknowns using data mining and modeling, among other processes. 

Tableau solves many of the issues related to Big Data. Data translation is used to concentrate on creating a data picture for organizational usefulness. For example, identifying market trends, operational efficiencies, profit margins, and revenues.

Machine Learning, Data Mining, and Deep Learning

As it pertains to data science, to make business decisions, data insights, and relationships are studied. Due to advances in the volume and speed of computing available data, machine learning, data mining, and deep learning are methods of providing this information. As such, this is another area that is highly valued in the data scientists toolkit. 

Once again, due to the connections made with Tableau, data mining is beautifully performed.

Data Analyst Careers

All areas of the company will need information from the data analyst. As part of the C-Suite, the executive that will most closely work with you is the Chief Data Officer, who needs to understand and oversee company data systems, gaps, and risk, in order to develop strategies to fix issues, meet goals, and move the company forward. The Chief Data Officer will escalate, strategize, and resolve data issues.

There is no end to directors, managers, and colleagues that will also rely on your collection of insights. The beauty of Tableau is that it can easily be altered to massage the data in a format that is relevant to each area of business.

Which industries see the value of data analysis employees? Just about all of them. 

  • Consulting
  • Education
  • Healthcare
  • Finance
  • Government
  • Information Systems
  • Insurance
  • Manufacturing
  • Utilities

There are a lot of videos and books on becoming a data analyst that delivers information specific to your industry. The book entitled “Analytics: Data Science, Data Analysis, and Predictive Analytics for Business” on provides an overview that encompasses many industries. 

Take a look at this seven-minute video that describes the career path for a data analyst. Jen, a former data analyst and current data analyst manager, looks at four different career paths and why you may want to veer in each direction. She describes the growth opportunities and salary pockets. 

An eight-minute video concentrating on who the analyst is, salaries, skills, and background is demonstrated in “How to Become a Data Analyst in 2020.” Created by 365 Data Science, it is from a business-like perspective as opposed to the previous personal example.

Practice presentation skills, creating a powerful delivery, as shown in this seven-minute video. 

Earn a college degree in Data Science, Data Analytics, Statistics, or Mathematics. Choose from a Bachelor of Science, Bachelor of Arts, or Master of Science.

Data Analyst Qualifications and Career Path

A data analyst is a stepping stone to becoming a data scientist because you gain valuable experience in the data science environment. Your knowledge is used to determine resolutions to problems using computer systems, identify and report what is behind the numbers, and discover trends and new opportunities. 

  • Aggregate data, create reports, and dashboards.
  • Report performance metrics and risks. 
  • Present data-driven insights and discoveries.

A data analyst will shift back and forth from operations to strategy. With this in mind, it would be helpful to learn statistics and to be able to code. A data analyst must be a team player who is thinking about the big picture but not afraid to jump into the numbers as needed. 

Math and statistical concepts that are useful to be familiar with to become a data analyst are:

  • Confidence intervals
  • Predictive modeling
  • Quantitative methods
  • Sampling
  • Test control cells

Another area that a data analyst may touch on in some companies is predictive analysis. Data scientists develop statistical or mathematical models, while the data analyst uses those models as tools on a known set of data to predict future insights. 

In a larger company, statisticians or programmers may also be involved in the process, but data analysts collect the data and are involved in presenting results. Executives rely more frequently on analytics to make informed decisions about future business goals and direction; therefore, predictive analysis is an increasingly important part of the job. 

Mathematical models frequently used to leverage predictive analysis are regression models, clustering models, optimal estimation, linear regression, and text mining. While most of these techniques have been used in the past to look at historical trends, the significance of predicting future trends is more prevalent. 

Data scientists spend time learning how to develop new tools while the role of the data analyst is to interpret them and utilize what is already in existence. The scientist is a senior position and has more years on the job and education. At the same time, an analyst aspires to be at that higher level by working and learning on the job and possibly getting additional education.

Advantages of Tableau in Data Analytics

There are many benefits, starting with the fact that companies aspire to create a culture where data visualization is used daily in the business world. And this data culture is quickly picking up steam. 

Also, technical skills are not needed in order to take advantage of Tableau. It integrates well with any platform and works on any data type. Since so many people are already using it, there are a lot of resources available and a thriving community of users. If you ever need an answer, you can certainly find it on the internet by asking at Reddit, GitHub, or the Tableau support website. 

Tableau is high performing in that it runs quickly, is responsive, and handles huge amounts of data on any platform. Depending on what is needed, Tableau can deliver. For instance, do you need to collaborate? Use the cloud-based server, Tableau Online. Dashboards can be easily integrated into Salesforce, and many other programs, for real-time updates.  

Disadvantages of Tableau in Data Analytics

Although there are few red flags, there are instances in which Tableau may not be the appropriate choice for a business. First is the high cost due to stringent pricing without adaptability. Tableau reasons that licenses are all-inclusive, whether or not you use all of the features. And that does work well for some companies but not others.

A data analyst uses the software by reviewing a business problem, querying data, generating reports, and advising corrective actions to appropriate stakeholders. The data analyst makes recommendations because they are the users, but the ultimate budget spent, especially in large corporations, resides with someone else. 

If the budget decision-makers have an allegiance to one of the competitors or a conflicting personal preference, another data visualization software program may be chosen. 

Salesforce recently acquired Tableau. Changes that new management brings may be positive, but that is an unknown until Salesforce has had time to integrate the companies and make organizational decisions. 

In order to further functionality and enhancements, in-house IT support is needed due to the necessity for SQL queries by developers. Otherwise, routine maintenance for the fundamentals can be completed without additional assistance. If these items are troublesome, it is worthwhile to look into other options.

Lastly, there is no turning back, or grandfathering, to previous versions in Tableau. Therefore, it is even more important to take a thorough look before updating to the newest version of Tableau. Big companies such as Abode, Nike, Skype, and LinkedIn are currently using Tableau. 

Alternatives to Tableau

Excel is limited in size and functionality, while Tableau can handle Big Data and is much faster as well. Microsoft Power BI is a more likely choice for data analytics. It is indeed recognized as a competitor to Tableau and is a leader in Business Intelligence platforms. 

If an organization already has Microsoft products, it is worthwhile to compare it feature for feature to Tableau, including cost and usability, to make a personalized decision as to what is best for the company. Power BI works seamlessly with other Microsoft business elements, although it is not known for its ease of use. 

QlikView is a dashboard but does not lend itself to analytics. Qlik Sense is a much broader Business Intelligence tool, but it is newly released and does not have the following that Tableau has. You would need to purchase both in order to compare to Tableau’s functionality.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


There is plenty of evidence that Tableau can be used for data analysis. Business intelligence requires the analysis of data. A company with a successful data culture, in which data is used successfully to make decisions on a daily basis in a much-desired goal by many organizations. 

By gaining expertise in data visualization software, Tableau being the industry standard, the data analyst can eloquently resolve many Big Data problems. Becoming certified is a smart career move for the data analyst. Considering the field of data science is projected to continue to increase now and in the years to come steadily.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.


Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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