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Comment by minimaxir | Hacker News Reader

I wrote a similar article 2 years ago (https://minimaxir.com/2018/10/data-science-protips/) arguing against MOOCs for learning data science/machine learning skills, as they are not reflective of real world applications.

I'm all for personal projects especially in DS/ML (it's how I started my career in the field), but the unfortunate reality is that there's really no way nowadays to learn the hard/boring parts of DS/ML without already having a DS/ML job. Any attempts at an analogous MOOC/YouTube would likely not be very popular.

That said, the DS/ML job market 2 years later is even more competitive, and niche personal projects on your resume are no longer enough. Even after working as a data scientist for 3 years, I'm not confident I could get another DS/ML job.

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How horribly disappointing. I'm at the tail end of a 6 month, $17,000 online DS bootcamp. I've got some great projects and learned a ton but yeh, they seem to be slowly preparing to let us down. They keep saying how hard it will be to find a job and may take months. LOL, I'm screwed.

The frame of mind that kept me going was focusing on my effort, instead of an outcome. I visualized my effort by tracking job applications. I applied to a lot, so at the end of the day, that metric is what made me feel successful at the end of the day, not necessarily whether some inundated recruiter emailed me back.

It's down to the market. There aren't that many beginner or mid-level jobs in the field and there is a big oversupply of candidates. I wouldn't recommend data science or machine learning to people who are changing career direction or need to invest a lot of time/money/effort to catch up with all the theory. Not that it's impossible, just very hard. It's also getting more and more gatekept over time with increasing requirements for advanced degrees.

You're not screwed. If you know any working data scientists // MLEs I would really recommend you get feedback from them on your projects. Ask them to do some mock interviews. Way too often I see bootcamp candidates who do well on technical screens but fall apart during in person interviews because they just did what the instructor told them (use docker, put it on github) rather than understanding why people want to see those things.

A lot of it may come down to research AI vs. applied AI. MOOCs that are online versions of university courses are heavily slanted towards research AI, which involves lots of math and may not be all that applicable to what most data scientists are doing on a day-to-day basis.

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