Launch your data visualization career with these expert tips

If you’re looking for a career that capitalizes on your data visualization skills, the opportunities are out there. According to a 2017 report published by IBM and Burning Glass Insights, there will be an estimated 2.7 million job openings for professionals with data skills this year. 

As the founder of Data Story Academy and Co-Founder of Decisive Data, a Seattle-based analytics consultancy, I’ve been able to help many professionals launch their analytics and data visualization careers. Regardless of whether you’re just starting out in your data visualization career or looking to make a transition, here are my tips for getting started: 

1) Do your research. 

Being proficient in Excel is only the beginning when it comes to effectively positioning yourself for a career in data visualization and analytics. 

Because analytics skills can be applied to any company where data is collected and your day-to-day responsibilities can vary depending on your industry, department, and role— before you ever sit down for an interview, it’s critical that you do your research. 

Familiarize yourself with the industry. Try to get a sense of what dataset(s) you’ll be working with on a regular basis, what metrics are most important to the business, and why. 

A lot of this information can be found within the job description itself, but there are also plenty of other avenues for research: Read through the career advice threads in Reddit communities like r/analytics or r/businessintelligence (or, if you’re feeling brave, ask directly!). You can also attend analytics networking events and any professional development opportunities, like those provided by the Digital Analytics Association. 

The stronger your grasp of all the ways data can be used in your role, and at your company, the better prepared you will be for the interview, as well as when you inevitably land the job itself. 

2) Supplement your data visualization skills with data storytelling.

Data storytelling is a structured approach for communicating information via a combination of data, data visualizations, and narrative. For companies, hiring effective data storytellers is the equivalent of striking gold—particularly for the thousands of organizations that are rich in data but need help putting it to use.  

Data storytellers are experts at translating data into meaningful insights for executives, management, and other business stakeholders to drive informed business decisions. In short, these are the people who are responsible for bringing data to life and are, therefore, critical to enabling companies to become truly data-driven. 

Download: 20 Keys to Data Storytelling Success

This is why growing your data storytelling skills can give you an edge not only in your job search but also in your broader career. 

There are many resources out there to learn effective data storytelling. If you have a strong academic background is in communication, the skill may come somewhat naturally to you. But for those who need a little more structure, I’d encourage you to check out Data Story Academy’s three-part foundational course.

Over the course of 22 on-demand video lessons, you’ll learn the necessary foundational skills for data storytelling and put it into action. Through Data Story Academy, you’ll learn how to structure, organize, design data visualizations, and deliver reports and dashboards in a way that inspires quick insight and dazzles your executive team. 

3) Practice. Practice. Practice. 

There are many resources available to help you show off your research efforts and combine your newfound data storytelling skills with your existing data visualization abilities. 

Start by downloading Tableau Public, a free software program that enables you to publish interactive data visualizations to the Internet. Once you’ve done that, set up your profile and start by checking out Tableau’s library video tutorials. Watch as many of these videos as you can. 

Recommended Read: The Fundamental Challenges of Dashboard Design

Once you’ve familiarized yourself with Tableau, you can start building your own data visualizations and dashboards using free, public datasets. Public datasets are a great resource to anyone looking to show off their data visualization and data storytelling skills.

Here are a few of my favorite public datasets: 

• Tableau Sample Data: Tableau provides its own library of sample datasets, spanning many topics including sports, government, education, science, technology, lifestyle, health and more. Check it out here.
• Google Trends: Google’s trends data provides a multidimensional view of what people are searching for on the web. You can use a variety of filters, including time and location, to narrow down the data. Access Google trends here.
• Pew Research Center Data: The folks at the Pew Research Center collect and analyze data from all over the world, covering a range of topics including technology, social media, politics, journalism, the economy, online privacy, religion, and more. They produce a lot of their own analysis, but they also offer up the raw data for secondary analysis. Read more and access the data here

4) Build your own dashboard portfolio.

Once you’ve got your data, practice building 10-12 Tableau dashboards and publish them to your new Tableau public profile. If you need some inspiration, check out a few examples from my team at Decisive Data. You can also browse Tableau’s Public Gallery - clicking through their Viz of the Day is always a great place to start.

Once you have a solid set of example dashboards demonstrating your skills in data visualization, and data storytelling, start applying to entry-level analytics roles and include a link to your Tableau public profile. 

Starting off your data visualization career can be a fun and exciting process, and one where you really get to see how your analytics skills can positively impact a company. Through these tips, you’ll not only stand out in the application process, but also be better positioned for a truly rewarding career in data visualization and analytics.