The Specialized “Data Scientist” Will Win in The Long-Run. Becoming a Specialist Will Take You Further than A Generalist.
In my last post, “The importance of Branding in Data Science” I mentioned that Data Science has become too broad hence branding is an excellent tactic that may be employed to overcome the noise of being a “Data Scientist” which in turn works in our favour during the vetting process.
After pondering on my own writing for some time, it made me wonder…
Would it of Been Easier If I just told people to specialize?
Despite the identity crisis we are facing as a community, I am usually not one to care about titles. However, I understand the importance of distinguishing between roles, and on that basis, I wouldn’t be surprised if we begin to see roles like “Statistical Modeller”, “Natural Language Processing Engineer”, or “Computer Vision Engineer” — Maybe not these exact names, but you get my gist — popping up, whilst roles like Data Analyst get to reclaim their identity.In other words, the Data Science buzz has run its course and it’s time to specialize. Here’s why:
There are a lot of things that go into becoming an indispensable “Data Scientist” including staying up to date on the newest trends, the best practices, and developments. Given the broadness of Data Science, having to remember all of these things across the board is like fighting a losing battle. You are better off losing that battle in order to preserve your resources to win the war.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different
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In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.