The 5 Levels of Aptitude in Data Science – Useful Insights for both Data Scientists and People Managing Data Science Teams

Recently this article found its way to my Pocket feed. Being a 22 minute read, I was hesitant about whether I should save it since I don’t have the time for such long articles. So, I decided to save it as an insomnia remedy, since I figured that it would probably put me to sleep before finishing it. However, it had the opposite effect! What’s more, it urged me to think about how all this applies to data science and how it can help all those related to the field (be it as practitioners of data science, or people handling data science resources).

The author of the article highlights 5 categories of professionals, based on their aptitude level, the 5 levels of expertise as he calls them. I prefer to avoid the term expertise since some can be an expert in one aspect of data science and yet not be an expert in the field overall. Aptitude sounds more appropriate but if you find that another word is more suitable to describe general competence in a field, please let me know. So, the 5 levels of aptitude are:

1. Novice – someone who is a beginner to the field. In data science, this is often referred as junior data scientist, or in some cases, a data analyst or statistician.
2. Advanced Beginner – someone who knows enough to be dangerous, but still needs to rely on other people’s guidance in order to get anything done. Most data scientists today fall into this category.
   
3. Competent – someone who has a sense of discernment when it comes to picking the right tools, and is more engaged, but is still not adept at handling the cases when things go wrong. In data science, that person may often be a “senior data scientist” but rarely a team lead.
   
4. Proficient – someone who has developed a sense of intuition when it comes to the goal in a given situation. This kind of data scientist can handle all kinds of scenarios, but she may still need to research certain problems before figuring out the best strategy for solving them. Such people tend to be team leaders or bear some fancy title in an organization.
   
5. Expert – someone who really knows what they are doing (even if they cannot always explain it), with a very well-rounded intuition on the subject. A virtuoso of sorts, the expert data scientist is usually adept at all kinds of areas and is sought after by conferences and other data science events, as well as publishing houses. Also, he may make noticeable contributions to the field and even have her own data science company.
   

I would very much like to go into the details of each one of these levels, like the author of that article did, but I’d rather cover this topic in a video, if there is sufficient interest for it. One point I’d like to make, however, which may not have been conveyed clearly in the original article is the usefulness of this classification. Whatever point you are in your data science career, it is important to be fully aware of where you stand and what you need to do in order to better yourself. This can become apparent if you contemplate on this taxonomy and be honest with yourself. If you are in the lookout for hiring a data scientist, this is useful for you too, since the role you wish to fill has more to do with a sense of aptitude and responsibility, rather than merely a set of skills and/or X years of experience in the field. This way, not only will your hire be a good investment as a resource, but may help clarify what data science can do for your organization.

One last thing. It is important to remember that no matter what category you fall into in this taxonomy, there is always room for improvement. Even an expert has things to learn, so keep an open mind about what data science has in store for you. Let’s remember one of the great Rennaisance master artist / sculptor Michalangelo, when he said, while in his advanced years, “I’m still learning...”