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From theory perspective, understand basic stats theory. Too many CS background morons building machine learning models without understanding the theory first. From a skillset and market value perspective, pick up data engg skills, you will be much more effective in industry.
There are statistical concepts that are critical from a solution development context.
One thing I’ve noticed statisticians do more rigorously is the observation of biases. For instance, user data is inherently biased towards those most engaged with a given product or service. Things like survival biases, look forward biases, etc... aren’t quantified but certainly are relevant.
The question, “is it worth developing models on this data?” is a more frequent thought.
Would love general career advice as no idea what one does after jr data scientist without a STEM background or software dev exp
want to get good! Any advice is helpful.
Currently proficient enough in Python, have done classification,regression,clustering across several industries and have a few models in production affecting businesses (low scale, X thousand predictions per month, binary classification)
No experience with deep learning, image/video,NLP and frankly feel out of depth given my lib arts background on tackling those at clients
What level are you and location? We got lots of resources and a solid contingent of data scientists that can help with their perspective.
You can learn programming on the side easier than statistical or mathematical theory. For that reason, I value my stats masters. As I learn more programming and gain business exp, I’m continuing to unlock latent skills while non-mathematical counterparts seem to hit a wall.
Really get into the stats side, and not just the math, but learning to approach the problem and understand uncertainty in data. The programming languages are just tools.
Any pointers on where to focus my efforts if I'm looking at data science? Did you do Kaggle and bring that into your interview?