Data ScienceLifestyle

A Data Scientist Guide to Income Diversification

As a data scientist, you are kind of a unicorn – you know some programming, databases, stats, and, of course, data. Not utilizing every bit of knowledge won’t get you too far, and 9 to 5 isn’t as safe as it once was. So, what can you do? Diversify.

It’s hard to get a job in data science in the first place. And it shouldn’t be an end goal, at least for Millenials and anyone younger (like myself), because:

  • It’s not that safe anymore (COVID crisis proved that) 
  • We don’t care too much about safety

But even if we did, relying on a single source of income is not the best thing one can do – not with a data scientist’s skillset at least. 

Ask yourself the following: If I were to lose my job now, for how long would the money keep coming? Until the 1st of the next month? Or the 10th? Anyway, you get the gist. You earn as long as you work.

That idea doesn’t get me excited at all. That’s why a year ago, I decided to diversify my income streams, and now I have 5 of them. 

How was that possible? Below you’ll find five sections, each describing a single income source and how I’m maintaining it. You’ll be able to the same as long as:

  • You have some marketable skills – data scientists and software engineers preferred. It’s not a must, but you’ll have to figure out more things if that’s not your background
  • You are willing to work – and by work, I mean to actually work. 12+ hour workdays and working weekends are a small price to pay for what’s coming next

Also, above-average intelligence is not required. A regular Joe willing to work hard beats talented lazy-ass know-it-all every time.

Let’s begin with the income source number one.

9 to 5

I guess this one was a no-brainer for most of you. I said a regular job isn’t that safe anymore, but that doesn’t mean you should quit it. At least in the beginning. Let me elaborate.

There’s no point in ditching your only income source to work on the other ones if you’re starting out. Some excitement about personal projects is great, but more often than not, it pays precisely $0, or it even takes some money out of the pocket. You can’t afford that lifestyle just yet, but it’s just a matter of time.

9 to 5 will get you relevant working experience. You’ll never put yourself that much outside of the comfort zone as a new, enterprise-level data science project you’re working will. And that’s essential to get better at things. 

Let me repeat myself once again – 9 to 5 shouldn’t be an end goal to anyone wanting to build multiple income streams. No matter if the job is at Google or Microsoft, or the salary is $500K. This income stream stops as soon as you stop working (it’s active income in other terms).

Side job

This one also shouldn’t come as a big surprise. If you have some spare time and don’t mind sparing it on work, it can’t hurt to try freelancing or providing some other B2B service. You’d be surprised how much you can earn for only a couple of hours a week.  

The possibilities are endless – from freelancing on sites like UpWork to starting your own business and scaling it through the years – it depends on your aspirations and the amount of time you can put in. Once again, your skillset as a data scientist is wide, so you can apply for more postings then frontend developers, for example.

If there’s one thing harder than getting a job without experience, it is getting your first freelance client, at least if your name is entirely unknown. So prepare yourself for a month(s) of rejection. It’s just a temporary thing.

Blogging

This one is my favorite by far. It’s a precious tool for any data scientist, because:

  • Let’s you stand out from the crowd – read: recruiters come to you, and not you to them (in the long run)
  • Makes you understand things more deeply – because you have to explain them in the simplest way possible
  • Get’s you continually learning new things – data science is evolving rapidly, so you’re always up to the track, and there’s no way you can run out of things to write about
  • Money on the side – a few hundred to few thousands of dollars per month, depending on the work you put in

Check out this article if you want to learn more about blogging as a data scientist (or tech professional):

How Having a Blog Can Advance Your Career as a Data Scientist

Honestly, this should be a no-brainer if you want to take yourself and your income to the next level. A great place to start is Medium, of course, but you can also choose to host your content. The first option is free and will show results quicker, but the ladder is better if you want 100% content control.

Affiliate marketing

Every so often, I read a book or watch a course that’s just better than the others. There’s no reason not to share it with my audience if I find it beneficial. Also, this income stream comes naturally with blogging, as there’s no reason not to make any product link an affiliate one.

If you are unsure of what an affiliate link is, continue reading. Essentially, it’s a referral link, which means you’ll make a small commission from any purchase that came through your link. Price for the end-user stays the same, or is even reduced in some cases. 

I do this a lot with tech books that have helped me become better at what I do. See the following article to get an idea:

The Single Best Introductory Statistics Book for Data Science

If you don’t have time to read it, here’s my thought process on affiliate content:

  1. I’ve read/watched a fantastic book/course
  2. My audience can benefit from it – write a short review
  3. Put an affiliate link to the book/course and make sure to disclose it

It’s a straightforward strategy that earns a couple of hundred bucks every month for no additional effort – because I would write book/course reviews anyway.

Amazon and Udemy have great affiliate programs.

Writing books

Sometimes I have a content idea that can’t into a single blog post because it would be a 45-minute read. No one wants that. So, why not make a short eBook out of it? 

I’m only a week or so in this income stream, but there are already a couple of purchases for my Time Series Forecasting with Python and XGBoost eBook.

And it’s incredibly easy to get started. The most challenging part is to, well, write and edit the book. Publishing is easy. Let’s discuss two options:

  • Through your website – if you have one, WordPress/WooCommerce combination will do
  • Through Amazon – I went with this option for starters, as they have an excellent Kindle Direct Publishing service

I can’t tell you exactly how profitable this income stream will be down the road, but it’s a great way to put your name out there and earn a couple of bucks.

Conclusion

And there you have it – exactly how to go from one to five income sources. There’s a lot of technicalities not mentioned here because, again, it would be a 45-minute read, but I hope you get the gist and will explore further on your own. 

Feel free to get in touch with any questions. Just find me on LinkedIn and send a message – I’ll be happy to assist.

Thanks for reading.

Join my private email list for more helpful insights.

 

Dario Radečić
Data scientist, blogger, and enthusiast. Passionate about deep learning, computer vision, and data-driven decision making.

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2 Comments

  1. very informative blog man, please keep it up. Also, may i have your mail id, if in case i need to connect to you on a one to one basis?

    1. Hi Ayush, thanks for the feedback! You can reach me at info@betterdatascience.com, it will get redirected to my email immediately 🙂

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