my imposter syndrome

On the heels of World Mental Health Day I will share some considerations about mental health and (as my usual) the world of data, hoping it might be interesting, relevant and even beneficial for someone.

The confession: as a data practitioner I often struggle with the so-called "imposter syndrome", or something along the lines. It can be described as a feeling of being incompetent and unprepared, fearing being exposed as a "fraud", in spite of objective successes. The question: Am I the only one? My guess is that I am not.

As for the why, apart from the general Linkedin/social-media pressure, my theory is that the DS environment is exceptionally prone to this condition because of its nature and some external factors. The main ones I see are: - [people assume you know a lot] since DS enjoys so much hype I feel like we must bring something extraordinary to the table and we must keep up to the expectations; - [there are indeed a lot of fraudsters] since so many people who talk about data are bluffers we do perceive the constant risk of being considered one and the need to set apart; - [perfection is the enemy of progress] since “data scientist” is such a poor and broad definition, we tend to feel like we must be prepared in almost every single topic in DS: from Python to R to SQL, from descriptive to inferential statistics, from linear regression to neural networks, from recommender systems to time series analysis to NLP, from DevOps to MLOps, you name it… The impossibility of it makes us feel frustrated more than enthusiastic, and makes any learning much more difficult.

It would be interesting to know if my point of view resonates with anyone and to see how many of us are in the same boat.