Is Tableau Enough To Get a Job?


Since Tableau is a software that caters to those unfamiliar with programming, it is a quick and easy addition to your resume skill set. The shallow learning curve makes it appealing to learn. But will learning Tableau get you seen by job recruiters?

Tableau is not enough to get a job. The software assists data scientists and analysts in designing quick and intuitive data visualizations. Therefore, a Tableau-specific job still requires programming language training to do well. Most Tableau jobs require a data or computer science-related degree.

Read on to learn more about Tableau, jobs related to the platform, and the additional tools required to perform these jobs.

Important Sidenote: I interviewed 100+ 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?

The Tableau suite of software generates simple charts and dashboards from intakes of data. However, the visualizations require no lines of code to create. VizQL, a programming language that runs in Tableau’s background, makes these visualizations possible.

The language, an offshoot of SQL, translates expressions to interactive images. Compared to other software that requires code to create visualizations, Tableau users can make them with the click of a mouse.

Tableau limits itself to data visualization. It is not a programming language in and of itself. Furthermore, compared to peers like R Shiny, it does not accommodate minor detail adjustments. However, it makes up for these shortcomings with accessibility.

Tableau does not cater exclusively to programmers and data scientists. Other associates in your company can read and understand Tableau dashboards easily. This benefits company decision-making.

The software also boasts flexibility. Although VizQL and SQL are relatives, Tableau can interpret data written in other languages, including C++, R, Python, etc. Additionally, although Tableau is not free to use, it offers varying licenses depending on job role. That means someone who needs to see a dashboard can purchase a Viewer license. This license avoids any accidental edits or the complexities of sorting through additional software.

However, if you are gunning for a job in Tableau, you need a Creator license or an Explorer license. A Creator license gives the user authority to design and share visualizations. An Explorer license grants these abilities on a limited basis. In other words, Tableau positions are often for creators. Knowing how to read Tableau is not enough. While you do not need to code to design in Tableau, programming knowledge is helpful.

What Jobs Are Available in Tableau?

Now that you know what Tableau is and the nature of its use, let’s talk about the occupations that often utilize this software. Some of these jobs do not require Tableau but will favor your resume if you can use it:

  • Tableau Consultant: A tableau consultant’s role is in the title: consulting businesses using Tableau’s solutions. These are creators that generate automated visualizations via Tableau Desktop, Tableau Server, and Tableau Online.
  • Data Analyst: A data analyst is like a translator or data whisperer that communicates numbers, statistics, and figures in a tangible way. This role requires collecting, storing, and interpreting data to gain business insights that can drive decision-making.
  • Business Analyst: Making business decisions based on data-driven evidence is always a smart move. However, making a decision and putting that decision into action are two different tasks. Business analysts bridge the gap between businesses and IT to interpret these decisions’ consequences and make the most profit.
  • Business Intelligence Analyst: Contrary to business analysts, who focus on increasing profit, BI analysts find areas that need improvement to decrease loss. This multidisciplinary job requires a combination of IT, communication, and problem-solving skills. BI analysts need to mine through a lot of data and compare the various trends among their findings. Then they use this information to guide decision-making.
  • Business Intelligence Developer: Business Intelligence is a multi-layered system with multiple interfaces. In the storage layer, unstructured data sits in its rawest state. This data gets organized in the warehouse layer. Finally, the findings from this data get visualized in the reporting layer. A BI developer develops, deploys, and maintains the various Business Intelligence interfaces, ensuring these tools work correctly.
  • Business Intelligence Manager: Every team needs a leader, including business intelligence groups. A business intelligence manager supervises, trains, and directs a data analysis team to pursue data-driven assessment and risk analysis. A BI manager prepares reports based on their group’s findings and presents them to other company teams’ key executives.

As you can see, Tableau may be helpful in these positions. Still, the only job that requires proficiency is a Tableau consultant. Since they take on a developer role, consultants still need to understand basic programming to provide adequate solutions.

The rest of the jobs listed require a multitude of skills. Data storage, management, translation, calculation, interpretation, and presentation are among these jobs’ responsibilities. Additionally, management skills are a prerequisite for directing a team of data analysts. Many of these jobs pertain to business intelligence, which adds business education to the list of demands.

With all of these demands, how does one even prepare for a Tableau-focused job?

Applying for a Tableau Job: What To Expect

As evidenced earlier, learning Tableau is only one step if you plan to get a Tableau job. A data science education, business know-how, and leadership skills will also serve your pursuits. Let’s discuss what an application for this kind of job may look like and the skills that will help you nail it.

Here is an example from data analyst and Medium contributor Harry Ngo. Upon graduation with a bachelor’s degree in Statistics in 2019, Ngo faced resistance while looking for his first job. Rather than give up, Ngo spent his free time earning various certifications: SAS Base Programming, Microsoft Azure/Power Platform, IBM Data Science Professional, and Tableau Desktop.

When Ngo applied for his current graduate job in data science and analytics, he needed to complete an “Analysis & Presentation” task. For this assessment, the employer will provide a scenario with data for you to analyze. Then, you display a presentation based on the insights and interpretations you found from the data. In Ngo’s case, this presentation is via PowerPoint.

First, Ngo calculated statistics using formulas like AVERAGE and SUM in Excel. However, while Excel was appropriate for completing this simple task, its graphical presentation lacks visual appeal. That is why Ngo plugged his findings into Tableau. Tableau helped Ngo create aesthetically appealing graphs for his presentation.

Since Tableau visualizations are interactive, Ngo took screenshots of his interactions to present different findings from a highlight table.

Source: Tableau: Build a Highlight Table or Heat Map
Source: Towards Data Science: How Tableau helped me get a graduate job in data analytics | by Harry Ngo

There were multiple applications involved in this process. Ngo used Excel for calculation and Tableau to visualize his analysis. Then, he incorporated images of those visualizations into a PowerPoint presentation. Furthermore, his path to that application included multiple certifications of data science applications.

It is true his Tableau proficiency catapulted Ngo’s application above the rest. However, it was the icing on the cake to a host of skills.

If you didn’t notice, that is a lot of learning! Where do you even start? Some resources we recommend include Codecademy, Coursera, DataCamp, Udemy, and LinkedIn Learning. Recommended areas of study include data science and analysis, computer science, statistics, business intelligence, and information science.

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.

Conclusion

In short, Tableau is a great skill to have, but it complements an expansive skillset that any data analyst needs to book a job.

Some of these skills include programming literacy from learning software like SQL, C++, and Python. Calculating data requires formulae from programs like Excel. Business experience is necessary to connect data analysis to business practices. Lastly, communication skills are needed when presenting data to executives and team members.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. I interviewed 100+ 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.

  1. Editor. (2019, November 28). What is business intelligence developer: Role description, responsibilities, and skills. AltexSoft. https://www.altexsoft.com/blog/bi-developer-role-responsibilities-skills/
  2. Ngo, H. (2020, November 30). How tableau helped me get a graduate job in data analytics. Medium. https://towardsdatascience.com/how-tableau-helped-me-get-a-graduate-job-in-data-analytics-76dc448e2ad4
  3. Tableau career opportunities: What you should know in 2021? (2021, January 11). upGrad blog. https://www.upgrad.com/blog/tableau-career-opportunities/
  4. VizQL: A language for query, analysis and visualization. (n.d.). ResearchGate. https://www.researchgate.net/publication/221213647_VizQL_a_language_for_query_analysis_and_visualization
  5. What does a data scientist do? (2020, August 13). Northeastern University Graduate Programs. https://www.northeastern.edu/graduate/blog/what-does-a-data-scientist-do/
  6. White, S. K. (n.d.). What is a business intelligence analyst? A role for driving business value with data. CIO. https://www.cio.com/article/3387619/what-is-a-business-intelligence-analyst-a-role-for-driving-business-value-with-data.html

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.

How to Become a Data Scientist? x
How to Become a Data Scientist?

Daisy

Daisy is the founder of DataScienceNerd.com. 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.

Recent Posts