Linear Aggression – Degrees, Data Science, and Career Development

In a world not too far away, we were taught to get a degree, get a job, and stay the course. For some of us, the emphasis was on the latter 2 with the former being optional. Yet, even still, I feel like I run into what feels like far too much support towards a specific degree in order to get a particular job. Want to work in any type of analytics, BI, data science, or [insert buzz word of choice here] related discipline? Then you need a degree with one of those words in it…

Or at least Computer Science, Math or Engineering…or so it seems.

This rigidity concerns me. Data analysis is a skill used in a wide array of professions by a wide variety of people. HR uses analytics. Librarians love it. And all sorts of “-ologists” (biologists, sociologists, psychologists) assess data in their profession. Yet few of these people would have the “proper degrees” for many/most analyst jobs I see (outside of those within a domain).

Yet, people DO approach me about opportunities. And I’ve taken a database class or two in my time. I’ve hit grabbed Statistics and all sorts of goodies to be relevant. Yet, there’s something sticky here:

My most valuable education came from my interpreting degree. And I still use it every day.

I don’t use it in the literal sense. I’m not out interpreting events or providing online signed renditions of anything (trust me, I’m not the person for artistic interpreting). But, the exposure to a professional practice (see rants here, here (midway), and here) and being on the hook to ensure clarity and accuracy transfers well to analysis. Most importantly, my degree taught me to think on my feet and recognize the effects my actions had on others. That’s what carries over in my work today.

There’s all kinds of books these days discussing this phenomenon. The Techie and the Fuzzy is one, but of course, it made me angry (no surprise). Education gets lumped all sorts of ways these days, it seems. There’s STEM. There’s liberal arts. There’s profession-centric coursework. And there’s everything in between.

With lots of degrees, there seems to be a clear path. You major in interpreting, you become an interpreter. You study psychology and you become a psychologist. Or not. The linear career path is dying. LinkedIn loves to tell you this. And with good reason – it’s interdisciplinary thought that drives many novel innovations, such as using DNA analysis techniques to find fraud or applying advanced mathematics to cancer research.

What’s more, after school, work starts shaping you. What you do, what you’ve done, and what you enjoy start determine more and more where you go. Including into wholly new directions. There’s nothing wrong with career changing. It often adds value, allowing you to see how users navigate a dashboard (consider the fear factor) or how executives approach problem solving (hint – it’s usually more gut-focused). And, yet, I still get numerous postings of job offers wanting a Computer Science degree or advised to seek a Masters in [Analytics | Data Science | Number Crunching Magic]. I see jobs wanting 5 years’ of experience for a skill set that is perhaps better measured by work created, types of problems solved, and diversity in experience. There’s numerous people creating a wide breadth of visualizations during their own free time that will have more practical experience in a year than someone supporting the same 5 reports for the last 4 years. Measure in years and you’ll miss out.

I hired people for a good spurt of my career. We all want qualified people. We want employees who are capable in their work, able to not only meet the goals, but contribute to shaping them, and to make the office a happy place. But, the ideals around linear career paths and specific degrees need shelved with my 8-track player (oh, the memories).

Here’s what you need to do data analysis at a high level:

• Statistics
• Logic/problem-solving
• Some kind of understanding of data “shape”
• Communication skills
• Curiosity
• Proficiency in tools of choice

As we move into a new economy where learning happens everywhere and where obsolescence is happening in months for certain skills, learning is becoming less structured and formal. Knowledge needed for jobs relies on a blend of skills, and less on a deep dive in one.

Data analysis needs all 6 skills listed above to truly be effective. Statistics provides a knowledge of not only what charts to use (graphicacy), but also how to be honest with the interpretation of numbers. Logic helps complete the calculations and validate the math required – people rarely need to have specific equations memorized as these are either available or can be found on the interwebs. While different tools favor certain structures of data, having some type of “database” knowledge helps, but being a full stack DBA is rarely required for most analytic jobs. You need to be able to communicate to make dashboards, but also to educate. Curiosity provides the drive, the commitment to accuracy, and the ability to find the “why” or “what” in an analysis.

A part of using Tableau is skill in the software, but the more important parts are all the skills we bring to Tableau, such as analyzing data, communicating effectively, design, and data structuring. These skills aren’t all tied to STEM. In fact, many of them come from a liberal education. Fewer and fewer people will follow a linear career path. Degrees are important for learning, for having a solid body of knowledge in something, for having a system that provides analogy to scaffold learning.

How we scaffold our learning – that’s up to us.