Importance of Data Visualization
All the best dataset, Artificial Intelligence, Machine Learning, and Business Intelligence tools are useless without effective visualization capabilities. In the end, data science is all about presentation, Whether you are a chief data scientist at Google or an all-in-one ‘many-hats’ data scientist at a start-up, you still have to show the results of your algorithm to a management executive for approval. We have all heard the adage, “a picture is worth a thousand words”. I would rephrase that for data science as “An effective infographic is worth an infinite amount of data”. Because even if you present the most amazing algorithms and statistics in the universe to your management, they will be unable to comprehend it. But present even a simple infographic – and everyone in the boardroom, from the CEO to your personnel manager, will be able to understand what your findings mean for your business enterprise.
Tools for Visualization
Because of the fundamental truth stated above, there are a ton of data visualization tools out there for the needs of every data scientist on the planet. There is a wide variety available. From premium and power-user based, to products from giants like Microsoft and Google, to free offerings for developers like Plot.ly across multiple languages and bokeh for Python developers, to DataWrapper for non-technical users. So I have picked five tools that vary widely but are all very effective and worth learning in depth. So let’s get started!
1. Tableau (https://public.tableau.com/)
Tableau Sample Email Marketing ReportTableau is the market leader for visualization as far as data science is concerned. The statistics speak for themselves. Over 32,000 companies use Tableau around the world and this tool is by far the most popular choice among top companies like Facebook and Amazon. What is more, once you learn Tableau, you will know visualization well enough to handle every other tool in the market. This tool is the most popular, the most powerful, and yet surprisingly intuitive to use. If you wanted to learn one single tool for data science, this is It.
2. Qlikview (https://www.qlik.com/us)
Qlikview is another solution like Tableau that requires payment for a commercial user, yet it is so powerful that I couldn’t help but include it in my article. This tool is situated more for the power-user and the well-experienced data scientists. While not as intuitive as Tableau, this tool boasts of powerful features that can be used by large-scale users. This is a very powerful choice for many companies all over the world.
3. Microsoft Power BI (https://powerbi.microsoft.com/)
Unlike the first two tools, Microsoft Power BI (Business Intelligence) is completely free to use and download. It integrates beautifully with Microsoft tools. If you’re on Microsoft Azure as a cloud computing solution, you will enjoy this tool’s seamless integration with Microsoft products. Contrary to popular business ethos at Microsoft, this tool is both free to download (full-featured) and free to use, even the Desktop version. If you use Microsoft tools, then this could be a solution that fits you well. (Although Tableau is the tool used the most by software companies).
4. Google Data Studio (https://datastudio.google.com)
This tool is strictly cloud-based and its highest USP is that it tightly integrates with the Google Internet Website Ecosystem. In fact, it is better that the solution be cloud-based and not on your desktop since a copy on your desktop would have to be continually resynchronized, whereas a cloud solution manages all requirements as required with the latest Internet datasets, refreshed every time you load the page. Nearly every single tool you need is at your fingertips, and this is one way to learn the Google-based way to manage your website or company. And did I mention – like Microsoft Power BI, it is completely free of cost! But again, Tableau is still the preferred solution for mainstream software companies.
5. Datawrapper (https://app.datawrapper.de/)
This is by far the most user-friendly visualization tool for data science available on the Internet today. And while I was skeptical, this tool can be used by completely non-technical users. And the version I used was free up to to a massive 10,000 chart views. So if you want to create a visualization and don’t have technical skills in coding or Python, this may be your best way to get started. In case you’re feeling skeptical (as I was), visit the website above and view the instructions video (100 seconds – less than 2 minutes). If you are a beginner to data visualization, this is where to go first.
6. Information is Beautiful (https://www.informationisbeautiful.net/)
This is an article on visualization and communicating concepts and analysis through graphics, so it would not be complete without this free gallery of samples at www.informationisbeautiful.net. What do we plan to communicate but information? Information is processed data. Data scientists deal with data but produce as output information. This website has opened my eyes as to how data can be presented effectively. While this is not something you would use for an industrial report, do visit the site for inspiration for ways to make your data visualization more good-looking. If you have business transformational data, it requires the best presentation available. This is a post for five data visualization tools, but consider this sixth one as a bonus for inspiration and all the times you wished your dashboard or charts could be more effective graphically.
While there is a ton of information out there, choose tools that cater to your domain. If you are a large scale enterprise, Tableau could be your best option. If you are a student or want a high-quality free solution, go for DataWrapper. QlikView can be used by companies who want to save on their budget and have plenty of experienced professionals (although this is also a use-case for Tableau). For convenient tools, you can’t go wrong with Microsoft Power BI if your company uses Microsoft ecosystem and Google Data Studio is you are integrated into the Google ecosystem instead. Finally, if you are a student of data visualization or just want to improve your data presentation, please visit informationisbeautiful.net. Trust me, it will be an eye-opener.
Finally, Tableau is what you need to learn to be a true data science professional, especially in FAMGA (Facebook, Apple, Microsoft, Google, and Amazon).
Also, remember to enjoy your work. This adds a fun element to your current job and ensures against burnout and other such problems. This is, in the end, artistry. Even if you are into coding. All the best!
For more on Data Visualization, I strongly recommend the articles below: