Comment by datademon | Hacker News Reader

Comment by datademon | Hacker News Reader

As an undergraduate who is about to graduate with a degree in "Data Science" this post encapsulates a lot of my worries as I move into the work world. Should I focus on being a "thinker" a "doer" or a "plumber"? For the first three years I was planning on being a CS major until I was denied from the department: now the data science major is my only hope to graduate. I feel as though my programming skills are solid: but not good enough to be on any sort of fast paced infrastructure/devops team. On the flipside: I feel as though I am so far behind on stats/math knowledge that it's pointless to try and become a data scientist/analyst. I've thought about data engineering (the 'doer') as a happy compromise between the two. However there are barely any intern or entry level data engineering positions that I can find. The ones I do find require knowledge of so many frameworks that I don't know where to start. Additionally, I'm not even sure if data engineering even is a happy compromise, especially after reading the post. Time is ticking, and sooner or later I'm going to have to figure out what route to take, and how I want to specialize. I go to a hyper competitive university in a hyper competitive region of the country and I'm starting to feel like I'm falling behind and getting lost.

If any of you older/more experienced engineers and scientist have advice or wisdom for me, I would very much appreciate it.

A bit OT, but as a more experienced engineer who dropped out of school to start a company, I'm curious: why weren't you able to get into your school's CS program?

Don't worry too much about "falling behind". There will always be time to learn more math or a new framework. Worry more about finding that first job, any job, then you can branch out once inside the industry. Networking beats recruiters beats sending a resume, so try to find a friend who already works where you want to be.

I did poorly on a math class that was required to declare the major. It's ironic since now that I'm in the data science major, I have to do even more math classes and less programming classes.

I would love to do my own startup. I have a few ideas floating around. But I feel like I lack the discipline to sit down every day and force myself to work on them without external deadlines/pressure.

In terms of jumping into the tech industry: I understand the advice about looking for any job when starting out. It just seems that even a lot of the entry level jobs are very specialized.

I'd recommend against doing a startup straight out of school unless you get accepted into a notable accelerator with a solid cofounder. Apply for the seemingly specialized jobs anyway, the worst they can do is say no.

1. I think it’s better to focus on doing - especially if you’re interested in working with an earier stage company. You’re much more versatile and if you choose the right company with an upward trajectory then you have the chance to specialize more into data science and learn model building if you want to. Also, data science seems sexy, but I find it most rewarding when you can put your own models into prod and also I think it’s useful for people to have the context around what it involves before they specialize. I’m 28, and had a lot of peers go into data science and quickly realize that it was the hype that lead them there and that they enjoy engineering more.

2. Look for work in a different part of the country... or maybe just the right organization that’s willing to take a chance on you. We’re in Austin and we’ve hired smart, hardworking kids who’ve never touched the languages we use and get them contributing meaningfully in <2 months.

3) I used to work for a startup where our CTO would not hire a data scientist unless they could write production code (in backbone and rails which I didn’t know at the time) and after I started, I spend 4-5 months just learning to be a full stack dev- I think that was so useful for my career as a data scientist. It meant that data scientists at this company could put whatever models they were running into the product- it drastically simplified org structure—- much more autonomy and fewer project management dependencies.

Sure not a lot of data scientists wanted to also be or make them selves into full stack developed, but you’d end up with the really gritty ones who and they’d end up being more loyal and much more on the same page as the rest of the engineering team it was way better for the whole org.

We’re hiring interns + junior full time people by the way

https://angel.co/schoolinks/jobs

Focus on the one you enjoy most, graduate, then go get some work experience.

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