Data is beautiful – So are these career opportunities…

Posted By on Jan 9, 2015


Data is beautiful, and so are the career opportunities in data visualization and data journalism!! Just look at the latest figures published by IBM at Insight 2015:

data is beautiful

Just like in data science and big data, the demand for experts with skills in data visualization and data journalism has been rapidly increasing over the last several years.  The opportunity in data is BEAUTIFUL! And, the great news for you here is that:

  1. It doesn’t take that much work to develop these skills.
  2. There are plenty of free resources available to you online, if you are willing to invest your time in developing the skills.

Before we jump in though, it’s prudent to see where you currently fit within the Data Analyst – Data Journalist – Data Visualization Specialist spectrum.

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Defining the Roles: Data Analyst, Data Journalist, Data Visualization Specialists

To be clear, this article is NOT about Data Scientists. It’s about Data Analysts, Data Journalists, and Data Visualization Specialists. Let me define those roles.

  • Data Analysts – “A data analyst uses data to acquire information about specific topics. This usually starts with the survey process, in which data analysts find survey participants and gather the needed information. The data is then interpreted and presented in forms such as charts or reports. Data analysts may also put their survey data in online databases.” – PayScale Typical Data Analyst salaries are as follows:
Data Analyst (United States)

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  • Data Journalists – Data Journalists are “experts who craft all the cool data-driven (news) stories you see out there — (they) must be masters at collecting, analyzing, and presenting data.” – Data Science for Dummies. Data Journalist salaries can be estimated from PayScale, as shown below:
Journalist (United States)

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  • Data Visualization Specialist – A Data Visualization Specialist “synthesizes huge volumes of raw data, and refines that raw material into a visual experience that is comprehensible and impactful to the business. Great candidates thrive at the intersection of data analysis, project management, and artistry.” – AirBnB

A Comparison Between Analysts, Journalists, and Visualization Specialists

As you can see, all of these positions require us to have great communication skills, a fair bit of education, and at least some basic skills in data analysis. But, let me elaborate on how the skill sets differ.

According to a recent survey of position descriptions, Data Analysts can get away with having an AS degree, but it appears that a BS is strongly preferred. More importantly though, they should have experience working with databases and SQL, they should have some domain expertise, and they should be well-versed in requirements gathering, reporting, development, testing, and ad-hoc analysis.

In contrast, Data Journalists need to have lots of journalism and data visualization experience. Data journalists should know how to use Office, SQL, Tableau, GIS and how to do basic web programming. They need to have at least a 4 year degree and they need to be social media savvy. What’s similar between these two roles, however, is that both Data Analysts and Data Journalists need a lot fewer skills in math and statistics than the Data Visualization Specialist. They also don’t need to know how to program in R and Python to do their job.

And lastly, Data Visualization Specialists… They generally need a 4 year degree and they need solid experience in creating data visualizations from large datasets. They also need to have a solid understanding of classical, inferential (Bayesian), and descriptive statistics. They should have experience in SQL, databases, data management, and visualization science.

 

Data is Beautiful, and So Are These Free Online Resources

Let’s discuss how to transition from a Data Analyst to either a Data Journalist or a Data Visualization Specialist.

Transitioning From a Data Analyst to a Data Journalist

Data Journalists need to have a lot of experience writing for the public. If you haven’t got that, you can get started now by quickly setting up a free WordPress blog and getting started publishing blog posts every week or two. This will be a long, fun journey. You will also want to learn SQL. W3 Schools will teach you all you need to know for free here and then you can get deeper practice by downloading MySQL for free and practicing with your own data. You can also get training on web programming for free at W3 Schools. To get some GIS experience, start with free, open-source QGIS and this free QGIS users manual. The best way to get social media savvy is to open some accounts and start sharing and networking. Of course, learning resources like the ones referenced here will help you as you go. Lastly, Tableau — you can download Tableau Public for free and view their free training videos here. With Tableau Public you’ll quickly understand why all us data enthusiasts so fervently proclaim “Data is beautiful!!”

Transitioning From a Data Analyst to a Data Visualization Specialist

If you are short on SQL experience and want to get into a formal role as a Data Visualization Specialist, then start with the resource referenced above. You will also need to get good at using Python and R. To get started in R or to build up some R statistics experience, download R studio and start working through these free training materials. You can learn Python for free from Google. Get a deeper understanding of visualization science by reading the work of Nathan Yau and Ed Tufte. Oh, and, as for data visualization experience, play with Tableau Public (referenced above) and check out these cool web-based data visualization applications: CartoDB, Infogr.am, OpenHeatMap, and Data Wrangler.

Do you agree that data is beautiful? What suggestions or free resources would you add to this list?

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7 Comments

  1. Hi! Lilian. I sometimes feel rude, about discriminating among Data practices, been following your tweets. Some time, You do make an irresistible impression! 🙂 I from another standpoint, see “Bridges”, em-powerment, and similar deep-connoting short cut, methods as “dubious”, on the other hand, I’m helpless in need of. Data scientist professional’s. You are beautiful.:-)

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  2. Great article, although in my opinion – Data Scientists are a made up term that allows certain people to believe they’re above/beyond normal understanding yet the reality is most of these people are glorified SAS resources who have no clue about programming, story telling or even delivering advanced analytics (no, a decision tree model is not advanced analytics)

    You need..

    Data Engineer – someone who can source information from a variety of sources and use software engineering as well as SQL capabilities to deliver end to end data ingestion, abstraction and storage solutions

    Data Analyst – someone who can build sets of data, mostly within SQL Server but someone who also understands pathing and the ability to find patterns.

    Data Visualizer – someone who can build stories and insight through the use of mapping/dashboarding solutions

    Data Modeller – someone who can build, or at least apply other peoples statistical mdoels and algorithms

    Big Data Engineer (for the right industries) – i.e Telco, someone who can work and understand Cell Site Network data and architect solutions that stream it into a Hadoop ecosystem and output it into an environment that the above can understand

    Change Agent – someone who can take the insight/dashboard/visualisation and make the change within the business that allows that result to be commercialised.

    Chief Data Officer – someone who understands end to end the key capabilities, requirements and toolsets needed by the particular industry or organisation to be successful, someone who can take complex subjects and simplify them and someone who can educate the business around governance, mandate and monetisation.

    All roles above should be performing the Scientific Method – i.e “I think this… I can experiment by doing this… I can prove my thought with those experiments, I can repeat those experiments to prove the result is common across multiple grups and I can apply those results”

    Don’t let dicky people who think they’re God’s because they’ve done a MooC on R or run SAS stop you from believing you can work in the Data Area. Speaking as the Analytics and Big Data Lead for a Telco, I’d much rather hire a smart, engaging personality with no skills, than a person who thinks they know it all.

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  3. Well, I am little discouraged by the fact that I need a degree for getting into the data science since I am from the electronics background and I came to know about data science through internet and became interested in it and I learned R, cleaning of data and data visualisation through MOOC so if I won’t have degree then it doesn’t mean anything.

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    • I didn’t say your experience does not mean anything. I was just reporting the industry trends. I am sorry your feeling down!

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  4. As you said yes, data is a very beautiful thing.
    Chief Data Officer – someone who understands end to end the key capabilities, requirements and toolsets needed by the particular industry or organisation to be successful,.

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