Imposter syndrome sucks.
It makes you feel like a fraud and usually causes anxiety for being found out eventually. It can be isolating and make you question whether you're supposed to be in a certain space. It is very isolating.
Data science and machine learning are especially prone to imposter syndrome.
Multi-disciplinarity is Hard
Data science and machine learning are very multi-disciplinary.
That means, there's always someone better at each particular discipline than you. Whichever subject matter expertise you work with, in my case meteorology, they know their domain in and out. They know more than you could ever understand.
But it gets worse.
Comparison is the Thief of Happiness
Even among your peers, your fellow data scientists, you will never come away looking great.
If we think of all the skills, we all have our specialities. Some are better at communicating, some are better at stats, some are better programmers. When you compare yourself to another data scientist you will always fall short in some way.
When you focus on your short-comings you can easily seem like a fraud.
Between Media and Frauds
These shortcomings can easily be aggravated with two groups that rely on other's expertise.
I love reading Morningbrew and they have some incredible AI writers. They're well-written and really bring across recent developments intuitively and in greater context. It's easy to forget that these writers can draw from expert interviews, extensive research, and their writing experience.
On the other side of the same coin, there are fraudsters on Social Media like Twitter, Linkedin and Youtube. They will readily steal work others have done and pass it off as their own. They're forming a superhuman influencer image with insane productivity.
These expert-based content producers (legitimate or not) can sometimes make you feel even more inferior.
Jack of all trades, Master of None, better still than Master of One
In the end, it's very hard for me and others to feel legitimate around their machine learning expertise, but in the end, I try to remember that full saying.