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On a related topic, I'm graduating with a master's in (computational) physics, and am already incredibly insecure about not having a PhD as many of the data science positions seem to prefer those. Would a four year PhD, let's say in ML, be a worthwhile investment from a data science career point of view?
Those exist? Be careful, very few doctorates are short and well-scoped.
Source: three letter, several of my friends were 7+, masters or not.
What a self-taught DS would need to do in order for me to feel comfortable hiring them is have a public body of work that I find impressive.
There are a huge number of publicly available datasets packed full of interesting information. Someone that shows they can do the work with a few findings on their github would be equivalent to a degree on a resume.
If you need structure to go through a few years of coursework on your own, you should go for the degree. If you just want to learn how to put pieces together and not learn how/why they work under the hood, you should opt for something else.
As with most questions about going nontraditional routes, you have to be really good to compensate, and getting really good is constant exhausting work.
If you're coming from a programming background, I'd suggest becoming a Data Engineer with the goal of becoming a Data Scientist. I've had several students do that. They were general programmers who learned Big Data/data engineering and eventually became more technical Data Scientists. You can start to learn more about the whys here: http://www.jesse-anderson.com/2017/03/what-happens-when-you-....
http://htmlpreview.github.io/?https://github.com/aguaviva/Ar...
It is computing the derivatives of the error with respect to the weights.
If you feel comfortable reading that then you are good to go.
A good programmer with even just a high level overview of ML and Stats concepts would be an incredibly valuable asset to a data science team. Most ML people are academics who tend to not have good software engineering skills, finding people who master both domains is really hard. Also to add to that most of the work in ML is feature engineering, data cleaning, testing and building pipelines which all require a good s…
I saw a mention to David Barber's book in one of the threads here, but what else?
A good programmer with even just a high level overview of ML and Stats concepts would be an incredibly valuable asset to a data science team. Most ML people are academics who tend to not have good software engineering skills, finding people who master both domains is really hard. Also to add to that most of the work in ML is feature engineering, data cleaning, testing and building pipelines which all require a good s…
I work for a technical statistical team in the financial world, we've been hiring PHDs lately to my team which has just meant exactly what this comment points out. I do a lot of the grunt work of getting the data sourced, cleaned and ready and am called the 'data wizard' and other such annoying names. What's frustrating is I can run the last lines of code and read and understand the output of the last step, but as th…