When pursuing a career in data science or business intelligence, working with computers is an essential part of the job. And if you have found your way here, chances are you are planning to add Tableau to your program suite. But if you plan to use Tableau regularly, should you purchase a new computer to accommodate the software on your existing one? Does Tableau work better on Mac or Windows?
Tableau is not better or worse on Mac than it is on Windows. There are minor differences between the two versions, but not enough to switch systems. It is more important to invest in a laptop that serves your everyday life as a data analyst. A mid to high-quality computer that travels well is ideal.
Read on to learn more about how Tableau works on Mac and Windows computers. If you are interested in purchasing a new computer for your data analyst career, keep reading for some tips.
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!
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How Does Tableau Work?
Over the past few years, Tableau has become a stand-out program for data analysts to create high-quality visualizations in record time. With its simple, intuitive, interactive dashboards, Tableau makes business trends easy to communicate with other team members in your company.
What makes Tableau different from other data visualization tools? Here are a few benefits:
Learning Curve
Compared to other data analysis tools, Tableau takes much less time to learn. While having programming knowledge is useful for a designer, creating a visualization on Tableau requires no coding. If you are using Tableau to view visualizations, the learning curve is even more shallow. Its lack of difficulty makes Tableau a cross-occupational tool for those familiar and unfamiliar with data science.
Presentation
There are some data visualization tools, like Excel, that can develop graphs quickly but without much pizazz. Excel visualizations are relatively bare-bones. On the other hand, Tableau can create interactive visualizations that highlight different trends with a mouse’s click. This on-demand flexibility saves time you would be spending making multiple graphs and tables for one presentation.
Ease of Operation
It bears repeating: Tableau is easy peasy lemon squeezy! Even as a developer, creation is achieved through simple drag and drop functions. While it does not always offer the in-depth customization of other tools, like R Shiny, it makes up for it by being easy to use by viewers and developers alike. Furthermore, viewers can view visualizations without the desktop app, via Tableau Server or Tableau Online.
What makes Tableau so easy to use? The answer is VizQL, the programming language that runs Tableau’s software. VizQL, or Visual Query Language, is a relative of SQL, Structured Query Language. The program intakes formulae from various databases, including SQL, and outputs them as visual presentations like graphs and tables. Luckily, VizQL does not require SQL data to run. The program can interpret all sorts of programming languages.
Tableau on Mac vs. Windows: What Is the Difference?
The business analytics market experienced a massive surge in the past decade: from $37.7 billion in 2013 to $59.2 billion in 2018. However, Pat Hanrahan, Christian Chabot, and Chris Stolte created the software back in 2003 at Stanford University. The first Mac version of Tableau, version 8.2, was released recently in 2014.
Does the Mac edition of Tableau run better considering the strength of the Apple hardware?
The short answer to this question is no, Tableau on Mac does not have any significant advantages over Windows. Tableau team member Dmitry Chirkov stated that, besides data connectors, both versions of the software are identical. Furthermore, he cited no significant differences in driver functionality and performance between either version.
Two minor differences may determine which version of the software is right for you, according to InterWork’s Katie Wagner. Here are some insights from her review of Tableau on Mac from 2014:
Search Functionality
macOS benefits from having a smooth and understandable user interface. Tableau on Mac takes advantage of this UI to provide an optimized search engine. On Windows, you may notice complications, especially when looking for parameters that you finished only moments ago.
However, Mac users can search through their data by clicking on the Help tab at the top of the screen. From there, all they need to do is type what they are looking for into the search bar.
Available Databases
However, Tableau on Mac’s one drawback is its lack of available databases. The program’s driver is still powerful. Unfortunately, it cannot intake all of the sources that its Windows counterpart can. Here is a quick review of the programs’ differences in databases:
This lack of data sources may seem disappointing at first glance. However, if Tableau on Mac can intake all of the data you usually use, you will not notice the difference. In sum, this detail is the most important to check when considering which system you will install Tableau.
Finding the Best Computer for Data Analysis
If Tableau runs the same on Mac and Windows, give or take some data inputs, does that mean the computer you analyze data with does not matter. In some ways, yes. Most data analysis runs through a remote server. Therefore, the remote server’s computing capacity will determine your data analysis’s efficiency, not the computer you run.
You define the task first and send it to the server, then the server processes the task and sends the results back to you.
Here is an infographic for reference:
However, just because most data analysis gets done over a server does not mean your choice in a computer is entirely irrelevant. If you keep these considerations in mind, you will have an easier time performing data analysis tasks daily:
Operating System
There are some programs you will use on your computer rather than over a server. Tableau, for example, is one of them. Both Mac and Windows can handle all of these programs. Newer, more experimental operating systems, like Chromebook, are not recommended due to lack of software support.
However, there is one caveat: if you do not use Mac, use Windows 10 with Ubuntu installed. Windows has no dedicated terminal application, which makes Ubuntu vital. However, Linux alone will prove fruitless when running specific essential software. You need these two systems working in tandem for the best results.
Ease of Carrying
One of your tasks as a data analyst is presenting your findings with other people. Whether it is to a group via PowerPoint presentation or a query among peers, you should be able to pull your work up quickly. Furthermore, your work should be on-hand wherever you go. That is why it is best to invest in a laptop that is lightweight and easy to carry.
CPU Power (for Local Use)
Times will come when connecting to the internet is unnecessary or unavailable. Perhaps you are on an airplane, or booting the server is impractical for the task at hand. In these cases, your computer should be powerful enough to handle local work.
Accessibility
Lastly, you need a computer that is easy to use. What features will be useful to you? Will a touch screen help your cause or hinder your work? Is the keyboard layout clear, or are you continually touching unwanted keys? These are some questions you should ask when considering the critical aspects of your laptop.
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!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- 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 does not run better on Mac or Windows. The software contains small differences between each computer, but nothing deal-braking. What is more important is choosing a computer that will suit your needs as a data analyst. Besides considering what data resources you will be working with, you should consider how the computer contributes to your daily analysis tasks. Choose a laptop that is powerful, accessible, and easy to carry.
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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