Probably the best way to ensure you have a mediocre data science career is to believe that your personal abilities are largely static, and high performers were more or less just born that way.
In other words, thinking it’s all about just kind of having talent – vs the acquisition and development of skill.
The greatest excuse in the world: “I’m not talented enough”
I love this excuse – it’s one I often struggle with myself. It’s just so natural, so comforting.
It means, there’s no use in trying, no use in exerting yourself. No use in trying to build skill, because…well, skill doesn’t matter. No use in grinding to get better at something, because it’s pretty much all about TALENT.
Someone better at programming than you? They’re more talented. Someone makes better graphs and visualizations than you? They’re more talented. Someone is a better communicator of technical concepts to non-technical audiences? Obviously more talented.
It’s so easy to fall into this mode of thinking – as I often do. The problem is, it can quickly become a one-way depressing street to effectively deciding to just never get better.
When you tell yourself you’re not talented enough…it feels quite liberating. It just completely removes struggle from the equation – the struggle that will pretty much always accompany any efforts to build skill.
It can be really, really hard to persevere through the early stages of building a new skill
For example: I’m trying to become a better writer and communicator of complex ideas, so I try to consistently write articles here about the data science industry. I know that my initial articles will suck. And I hate sucking.
I’m finding out that I’m all-pro at finding excuses to quit, with the most persistent one being essentially “you’re not good at this, the effort isn’t worth it, so you should stop.” Again, a very alluring argument.
Except, it’s completely false. Not the part about how I’m currently bad; that is pretty accurate. More so, the part about how I can’t get better.
I’ll spare you the long philosophical and mental arguments of why this is, but here are some articles to check out if you’re interested why I strongly believe that skill development is way undervalued.
Data science skill example: making better graphs
By “better” graphs, I roughly mean: compelling to a non-technical audience, accurate and compelling to a technical audience, not overwhelming, non-inducement of eye-glaze.
I think data science graphs are an interesting example to talk about, because it kind of fits into the more subjective “art” category – where of course, the more subjective something is, the more it’s talent and the less you can actually build skill and make personal improvements (…right??).
But then, when you start to break it down, you start to think of some potential areas where you could maybe make some incremental improvements:
- Paying more attention to whether the color scheme roughly looks good or not
- Getting more comfortable asking for iterative client/user feedback – or from some other accessible domain expert
- Thinking about whether the graph is overwhelming at first glance – and brainstorming a couple potential reasons why
- Asking if the various graphical elements make sense on one graph, or if it should be split into multiple graphs
- Thinking about what steps could be taken to de-clutter the graph
- Focusing more on thinking like the client – relentlessly asking yourself, what exactly do they care about?
None of these considerations is a silver bullet – and depending on the graph type, might not even be relevant. However, when you start listing out some potential ‘little’ things that could maybe make your graph less bad – that is the process of you building skill. Now talent starts mattering way less.
When you break down a “talent”-based concept into the much more mundane “here’s how I can maybe make this suck a bit less” concept – that is how you build skill. The alternative being: nah bro it’s just innate talent, there’s nothing worth me trying to improve here, move along.
Wrapping up
When you implicitly believe that you can’t really change, that’s a great way to strategically demotivate yourself.
Telling yourself that you’re just “not great” at learning or understanding advanced ML algorithms? Now you’re guaranteeing you’ll never be good at that, because you’re telling yourself to never even try.
Alternatively, if you just keep picking at something, you will get better.
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