Clearly, we only want people who are competent being responsible for work that we rely on. This is true across fields, but certainly holds for analytics and data science. The newer a field is and the faster it is growing, the easier it is for the lines between “trained” and “competent” to become blurred. This is a concern today in the world of data science and analytics.
What Does It Mean to Be Trained?
Getting trained in something new is easy these days. Not only are there traditional classes in virtually anything you want to learn, but there are also lots of online classes and other information you can find online to educate yourself and become trained in a new skill. It has never been easier to gain knowledge.
Let’s say I want to learn about painting. A quick internet search shows me that there are myriad courses, videos, and how-to guides out there that I can make use of to learn all about how to paint. I can learn about paint types, brush types, how to mix colors, etc. I can learn all about how to make magic with the tools seen in Figure 1. I can also earn certificates of completion showing that I passed tests about various aspects of painting.
Within a few weeks, I can claim to be “trained” in painting. I will be able to describe different types of paint, various painting styles, and when to consider different brushes and colors. None of this is bad, but is it enough?
Coming back closer to home, I regularly talk to people who want to learn about, or even focus their career on, analytics and data science. They often ask about the best programming language to learn, or the most important algorithms to study, or the best certificates or degrees to pursue. These questions are all in the spirit of getting trained and knowledgeable, which is a good and noble goal. However, I am often shocked at how some people think that this core training is all it takes to become competent. In reality, it takes a lot more.
What Does It Mean to Be Competent?
Does having a few courses under your belt, including certificates of completion, showing you’ve been “trained” mean much in practice? Not necessarily.
Competence is all about being able to apply the fundamental skills you’ve learned about in an impactful way in a real-world setting. Being able to describe the best type of brush and paint for a given purpose is not the same as being able to actually use that brush and paint to paint a quality picture. Similarly, being able to recite how an algorithm works and being able to discuss programming syntax does not equate to being able to actually create a meaningful analysis.
I recall the artist Bob Ross from Figure 2 who had a show on when I was young. He painted a picture from start to finish over the course of a show. It should have been painfully boring, but he made it fascinating and enjoyable. I have heard that his show is making a comeback online. Check him out and see if you aren’t also oddly compelled to finish an episode once you start it.
The reality is that Bob Ross is highly competent in his field. Even if I sat and tried to mimic his every move during a show, I would not be able to copy his paintings. Why? Because there is more to it than just following his instructions. Even as he says, “feather this color in a bit just like this”, I would struggle to do it correctly. You see, courses are just the beginning.
Moving from Trained to Competent
Achieving competence requires a lot of practice and practical application of the base skills we learn. Not many people would suggest that taking a few book-based painting courses would lead, by itself, to becoming a competent painter. There is simply no substitute for applying that knowledge again and again toward trying to make a real painting. Artists will rightly scoff at the idea that someone can be competent in absence of that practical practice.
Somehow, however, many people delude themselves into thinking that having a few analytics and data science courses under someone’s belt makes them a competent analytics and data science professional ready to be hired and put to work. As anyone who has ever executed real projects can tell you, there is a lot that you don’t learn from the books, but by hard earned practice. The unfortunate reality is that many people simply won’t be very good even after a lot of practice. I know I’ll never be able to paint like Bob Ross.
The action to take from here is to be skeptical of both yourself and others when there is an urge to claim that training equals competency. Training is a starting point, but competency comes only after a lot of practice and effort. There is also no guarantee that any given individual is able to become highly competent in any given skill. Whether hiring or developing existing talent, look at training as the first step toward competency, not the last.
Originally published by the International Institute for Analytics
Bill Franks, Chief Analytics Officer, helps drive IIA's strategy and thought leadership, as well as heading up IIA's advisory services. IIA's advisory services help clients navigate common challenges that analytics organizations face throughout each annual cycle. Bill is also the author of Taming The Big Data Tidal Wave and The Analytics Revolution. His work has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations. You can learn more at http://www.bill-franks.com.
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