I’m starting to get the feeling that most data science education programs are underemphasizing the role of non-technical skills – and sometimes by a significant margin.
I was talking to my little industry group about this – and we didn’t quite agree on what I say below. However, we did generally agree that, especially for newer data scientists, there’s currently a lot of room for improvement in the land of data science education.
It doesn’t matter how great your technical skills are if you don’t how how to use them
It seems there’s a maybe a growing group people who (on paper) are highly technical with all the right words on their resume, and maybe even some interesting early-stage data science experience. However, when it comes to even having the mindset for actually producing something that a client or user might value – there might be a steep drop-off.
It’s not that they don’t know the latest ML algorithms, or they’re not proficient in R/Python – rather, they just essentially freeze when faced with even the prospect of a professional real-world problem.
Of course, they might be quite effective at doing the preliminary data exploration, maybe some clustering or regression to get a feel for what’s going on amongst some ‘key’ variables. But when it comes to producing something that’s remotely beneficial to a real-world client (or user)…there could be a bit of a disconnect.
Obsessed with the data, but blind to the client
There seems to be a (maybe growing) disconnect between what data science boot camps, online courses, and master’s programs are emphasizing – vs what actually matters when making a client happy.
Again, this isn’t a technical skill problem. It’s more, I’m sensing that these programs are slowly forgetting the real-world aspect that none of your world-class technical skill matters if you immediately alienate your client or user.
Especially with the growing trend of automating away the more technical aspects of a data scientist’s job, it’s becoming much more important to be able to instill trust that you actually understand what the client wants, and are interested in requesting their ongoing feedback.
You need to convince the client you’re interested in their business domain
The client does not care what fancy algorithms you’re using. They do not care if you produce some fancy graphs. They care if you help them find relevant, credible insights in their data that they were previously unaware of.
I’m pretty sure most data scientists wouldn’t disagree with that sentiment – but I think many of us are losing sight of this critical piece.
In your initial meetings with your client or user, if they get the impression that you (a) don’t really understand their business domain, and (b) you’re not apparently that interested in learning…it’s highly likely that you will lose that client – and maybe quickly.
In other words, if they get the impression that your specific methodology for finding insights is invariant to whatever domain (eg fintech, healthcare, biotech, manufacturing, social media) you’re looking at, you could be in trouble.
It’s easy to find ‘anomalies.’ It’s hard to find valuable anomalies.
If you’re not familiar with the domain, and you have no persistent desire to learn, you’re simply not going to be able to know whether the ‘anomaly’ or correlation you found is at all useful – or rather has been plainly known to the industry for the past five years.
It doesn’t matter how fancy your framework and statistical inference was to make this ‘discovery.’ If you present ‘findings’ like this to a client and feel that you’ve done something substantial and useful – you will probably lose that client.
Especially amongst newer data scientists, I feel like there is an over-reliance on technical skill – to the extent that it doesn’t even really matter if you understand the data you’re looking at…let alone the business.
In educational settings, that’s maybe fine. In the real world, that’s how you lose a client.
You don’t have to be an expert on the domain to get a client, or to generate business. However, if you continually show a lack of at least trying to become more of an expert – you’ll probably struggle in your data science career.
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