That’s right, employers really don’t care how much you know. “How can that be???” you ask? It’s true! What employers really care about is what you can DO with what you know. As another school year begins, I thought it worth reinforcing a critical point that anyone, whether a student or long-time professional, should remember as they decide where to spend their time to improve their skills and job prospects.
Memorization And Fact Accumulation Have Limited Value
I’ll start with a personal example. I have always been horrible at drawing and painting (and handwriting too!). I could take a range of art history and painting basics classes to learn about which type of brush and paint work in what situations, how each combination was used historically, and techniques to make paintings more realistic or more abstract. I might even get an A in the classes and impress real artists with my voluminous knowledge of their techniques. Does that mean that when I sit down to paint a picture that I’ll be a good artist? No! I simply lack the additional skills necessary to apply my painting knowledge in practice.
I could make a similar argument about car repair. I am horrible at taking things apart and putting them back together even if I have explicit instructions on how to do it. I wouldn’t get a job in either the painting or car repair fields no matter how much book knowledge I gained!
Tying The Concept To Data Science And Artificial Intelligence
Unfortunately, many people focus only on learning all about the theory of data science and AI, the syntax of coding, and the concepts behind translating a business problem into an analytical plan. However, just like in my examples above, passing tests on those topics and being able to describe how they work is NOT the same as being able to apply that knowledge to design and execute a real project.
Having the underlying knowledge is of course necessary if you’re going to succeed with a real project, but it isn’t sufficient. After gaining the requisite knowledge and theory, it is necessary to demonstrate that you can apply it and effectively design an analysis, generate the code, build an appropriate model, and interpret the results. Many who can recite the facts and theory of data science and AI struggle with putting them into practice in a real-world setting.
Go Beyond Courses And Theories
Based on the prior examples, what is the best use of your time if you want to improve your job prospects? Certainly, don’t shy away from degrees, certifications, and self-study courses. However, never forget that you must also learn how to apply your newly acquired knowledge and to be able to demonstrate to an employer that you can so.
I am frequently asked by both students and professionals about what I think of this class or that, this certification or that, this executive seminar or that. What I always stress is that there is nothing wrong with pursuing any of those. However, it is critical to also have a plan to apply whatever knowledge you gain in a real-world, practical setting.
Prioritizing Your Efforts
Let’s wrap things up with very specific examples of how to maximize your job prospects:
- Always prioritize the chance to do a real project requiring new skills. Whether it be an internship, a project at work, a hackathon competition, or just a project you create for yourself, nothing proves you can use your knowledge more than showing examples.
- If you do get a new certification or take a new course, always follow up by finding an opportunity to put your new skills to use and documenting your efforts.
- On your resume and LinkedIn profile, as well as in verbal discussions, always focus more on what you’ve actually done than what you have learned. Put project examples (what you can do) up top and classes and certifications below (what you know).
Given how much knowledge those of us in technical fields like data science and AI need to have, it is easy to focus too much on acquiring more knowledge. While that’s a great thing to do, as you acquire knowledge never forget that employers really won’t value that knowledge – at least, not until you can clearly demonstrate your ability to apply that knowledge to add value by solving real world problems.
Always remember … they don’t care what you know, they care what you can do!
Originally posted in the Analytics Matters newsletter on LinkedIn
