So You Want a Job in Analytics?


I was recently approached by a soon-to-be college graduate, and he asked me my thoughts about careers in analytics. I have had assistance and guidance from many folks over the years, so I’m always more than happy to share insights with those who are just starting out in their professional careers. Traditionally, breaking in to a new business meant starting at the bottom… in the mailroom. That’s a tough gig. Don’t get me wrong—working in the mailroom can be fun, but with all that’s going on in the analytics space these days, there’s got to be a better way. Like with any endeavor, the adage of “you get out of it what you put into it” holds true, and that’s especially true with analytics. There are many things one can do while in college to prep yourself for careers in the data and analytics world.

In my years of management, I’ve often asked employees this question: what’s a skill that you can do, better than the average guy or gal? Let me use this as context for talking about jobs in the analytics space. Specifically, I’ll split the world into three job types:

  • Data analyst: moving and transforming data from one place to another
  • Data science: using mathematical or software techniques to turn raw data into valuable information
  • Visualization: presenting information graphically/visually so as to tell a story

While these aren’t the only three types, I find it’s a good way to classify “data jobs.” There are skills that go along with these (communication, listening, critical thinking), but I want to focus on these three job types and go into detail as to how one can prepare for jobs in these fields. Let’s dig in.

Data Analyst

This represents the backbone, the grunt worker, and the 80% of all data work. At one company, we used to call ourselves data janitors instead of data scientists because we were pushing a lot of data around with a mop (and sometimes the work was kind of stinky!). There’s nothing wrong with this work. I’d argue that any analytics person hasn’t cut their teeth until they’ve spent a few hours/days fighting with a complex SQL query that has a self-referencing table and runs overnight. There’s nothing wrong with learning SQL and starting off your career as a data analyst. It’s like being an X-Ray technician. You’re not the doctor getting the big salary, but you eventually learn as much (if not more) and it sets you up well for future careers. There are also lots of way to get started here on your own. The key things you need are:

(Note: I am not endorsing any of these tools/links, they are not the only ones of their kind, and I don’t think one particular tool/resource is any better than another… I’m just providing examples.)

If you’re in college (or rolling your own with MOOC’s and other resources), you’ll probably want to take some database courses to know what is happening under the covers and some statistics courses for basic analysis. But please make sure to do hands-on querying with some complex use cases to hone your skills. Pro tip: pick something topical (like polling/election/census data) as a way to help you think of different ways to slice the data.

Data Science

Data science is the “sexy” career du jour. You’ve gotta love jobs that inspire articles like “Tell Your Kids to Be Data Scientists, Not Doctors.” However, this is the trickier of the three fields listed above. Why? Because getting a job in this field might usually require an advanced degree in math, statistics, or computer science and that’s not the kind of advice I’m giving here. Given that, if it’s still a field of interest, there are some things you can do. The best advice I’d give is to take advantage of some of the great free courses that have been developed over the last few years:

The nice thing about using MOOC’s like these and others is that they are self-diagnostic. If you start falling behind in the course, then you know you need to get some more foundational knowledge. In addition to courses, you’ll probably want to familiarize yourself with a tool like R or a programming language like Python. Oh… and get used to reading lots of research papers from Google Scholar.


This is my personal favorite. It’s a world that combines the technical knowledge of a data analyst, the philosophical world of Design Thinking, and the creative world of graphic design. All of these come together in the magic that is visualization. I often refer to the narrative or storytelling of visualization, because that’s what’s going on. You started with raw data, it was turned into some valuable bits of information, and now you need to paint a picture and/or tell a story so you can convince someone to take action. That’s a grand challenge.

First off, take a few minutes to read about some of the pioneers in this space like Edward Tufte and Stephen Few. Next, make sure you have access to some tool that you can use for visualizations. Tableau is a wonderful (but not free) tool. Excel or Google Sheets have charting built in… it doesn’t need to be fancy. The biggest requirement on this front, though, is a portfolio. Again, you don’t need anything fancy. I would suggest thinking about something where you have an interest/passion, getting data, and then building visualizations. I’m a baseball guy— it’s very easy for me to get baseball data, and then start writing blog posts where I create visualizations about different aspects of baseball stats. The goal here isn’t to get subscribers to your blog. It’s to show that you can create visualizations, and that you cared enough to publish your work publicly. I can’t tell you how impactful it is to be able to see someone’s work so that I can have an in-depth conversation about how or why they did something. You can tell stories with data about box office numbersmountain elevations, oramphibians… it doesn’t matter! One last thing: make sure you learn about the best statistical graphic ever created. It’ll definitely score you points in a job interview somewhere along the way.

If you’re interested in a career in data, I hope this post has been helpful in making you think more specifically about what that career might entail and how you can get prepared for it. There are no promises here, and getting any job is not a simple thing. As the data folk say, all we’re trying to do is increase your probability of success. I should make a visualization about that.

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