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3 Ways to Make Extra Income as a Data Scientist

This article was originally published on Towards Data Science on June 8th, 2020.

There’s no debate that data science is a lucrative career. The current report from Glassdoor states the average salary of a US data scientist — all industries, all company types, irrelevant of the work experience — is $113.3K per year. For comparison, the median pay in the US for the first quarter of 2020 is $49.7K.

Now that’s for the US only, and actual numbers differ from country to country, but let’s assume the ratios are more or less the same — on average, average data scientist earns roughly 2x more than the average resident, anywhere in the world.

But how can one go from $113K to $200K while working on the same job? 
That’s where the power of side income comes into play. And you, as a data scientist, have the potential to earn a lot of it.

There are multiple reasons why. Let’s explore the trends for data science and machine learning through Google Trends:

Image for post
Blue: Data Science; Red: Machine Learning

This is 5 years worth of data, and there appears to be a strong relationship between these two terms (as expected). Also, we can clearly see that the trend is positive. I doubt we can expect any drastic changes any time soon.

Another obvious reason is the plain value of your skills. I see so many beginners in various Facebook groups asking questions anybody with 6 months of experience can answer without breaking a sweat. Teaching basics to complete beginners could be a viable way to go, but more on that in a bit.

In today’s article, I’ll share with you my top 3 ways of making an extra income that I use on a daily basis. These 3 enabled me to increase my earnings by a factor of 2.5 in less than a year. Most of the side income comes from the #1 tip, so let’s jump straight to it.

#1: Blogging

Having a blog impacts every aspect of your professional life. I’ve dedicated the whole article to this topic, so feel free to read it:

To recap, here are the top benefits of blogging:

  • You learn things in more depth
  • Great reputation builder — leads to more job offers
  • Amazing earning potential

I advise you to go through the article for in-depth explanations, as it’s only a 5-minute read. Writing data science articles, or articles on any tech topic requires a good understanding of the topic first. Being a successful blogger requires you to explain the complex topics with the most simple words, which then further helps your understanding.

I know what your thinking right now — I need to have years and years of experience before I start writing — and that can’t be further away from the truth. The reality is, you need to be only one step ahead of your target reader. That’s it. Let me elaborate.

Let’s say you want to write about Principal Component Analysis. To do so, you need a decent understanding of the topic, and your target reader is either someone who doesn’t know anything about PCA or doesn’t understand it well enough. Hence, you’re only one step ahead of your reader, as you know PCA well enough to explain it.

Plus, writing an educational article is a great motivation for learning some topic in the first place. Just take it easy and don’t overthink things. No one will resent you getting some bits wrong, and even if someone does, that’s their problem.

There’s still a high chance of someone with nothing else to do correcting your grammar, even if you do everything right, so just learn to ignore it.

#2: Educational Videos

I found educational videos to be a great way to supplement your blog. Having the same topic covered in text and video format makes your content that much more visible.

Not everyone wants to read through a tough tech article. Also, some topics are just too visual to be explained in depth with text. I always remember the linear algebra videos from 3Blue1Brown
Can you imagine learning complex topics like linear algebra from a textbook?That’s most likely how you learned it in the first place, but how good was your understanding?

I’m willing to take a bet you’ve remembered how to calculate things just for the exam’s sake, but completely missed the bigger picture of how things work on a visual level. No shame in that, as we’ve all been there. I repeat — some things are just better explained by the video.

Okay, so what are your options for making data science videos?

Well, the first and the most obvious one is YouTube. You can upload basically anything there, as long as it doesn’t violate their content policies, but it’s hard for beginners to get monetized. Let me explain why.

To get monetized on YouTube, you need at least 1000 subscribers and at least 4000 public watch hours in the last year. And that’s not that easy of a task for a beginner. My channel isn’t there yet, but I hope it will be soon. You need to put in a lot of hours before seeing results, and making videos, in general, takes a lot more time than you would think.

I know I felt tired AF after making my first video, even though I had the code already prepared.

But I’m not a native English speaker

Neither am I, and I’m not the most comfortable with the idea of speaking English for a world to hear. Google Slavic accents — you’ll immediately understand why. You could either:

  • Just suck it up and do it anyway
  • Use a text-to-speech software

The latter option got me covered, as these tools are getting better over time. IBM’s Watson TTS is a great way to get started for cheap.

If you think YouTube is too saturated, there are other platforms to consider. One that comes into mind is Udemy, but I don’t have any experience with them so I’ll leave that for you to research.

#3: Affiliate Marketing

Every now and then you’ll read a great book or watch a great course. There’s no reason not to share it with your audience if you think it’s something they could benefit from.

You could recommend the book or a course in two ways:

  • Through a normal link
  • Through an affiliate link

The only difference being the affiliate links will earn you some money for every purchase/subscription, while the normal ones won’t. If someone decides to but a book or a course, the type of link doesn’t matter to them. Also, if they like your content, there’s a good chance they’ll want to support you.

One of my recent articles is a great example of this idea:

So, in a nutshell, I read a great book. I was 100% sure at least someone from my audience could benefit from it, so I’ve written a short review and placed an affiliate link. It’s a win-win situation.

However, you should be careful with affiliate marketing. I will never recommend something I haven’t personally read or watched and aren’t 100% satisfied with. That’s a big no-no of affiliate marketing, as readers can easily sense when you don’t have an idea what you’re talking about.

So, where to start?

Amazon has an amazing affiliate program I use to recommend high-quality books. 
Udemy has a great program I use to recommend online courses.

Before you go

You have great earning potential as a data scientist, and it shouldn’t be limited only to what the company you work for is willing to pay you. Scaling your side hustles over time will double your income — guaranteed. It’s up to you to work your ass off until it happens, but it will happen inevitably.

Then you are free to choose if 9–5 is actually worth it, as the majority of your side income will be passive, which means you won’t need to actually work for it.

Just put in the time and trust in the process. You’ll get there.

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

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  1. Hi Dario. Valuable information on side hustle in datascience.

    My question is you have published same article in medium and on your website also.
    Can we do that. Can we monetize same article on Medium and on our website.

    Doses medium allow it.

    Because i was also confuse i we could do that.
    I am also starting to blog, to increase my reach i was planning to distribute the article on medium, linkedIn and my blog.

    1. Hi Yaser,
      thanks for the feedback.

      It’s okay according to the Medium’s policies, but please check with the publication you’re writing for (if any).

Comments are closed.

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