A Data Scientist’s Guide to the Side Hustle 💰 | by Garrett Eichhorn | Towards Data Science

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Ah, the side hustle. There’s something so enticing about having an extra revenue stream, a profitable passion project, or a back-burner bonus gig. A lot of us have at least some way of accruing extra cash on the side, even if it’s simply investing spare change. There are entire industries catering to the side-hustle: Uber / Lyft will pay you to chariot patrons during off-hours; AirBnB offers an entire network of users interested in renting your home; Instagram rewards influencers that tout goods and services to followers. What started for me as a means of saving money for college (flipping exercise equipment on Craigslist 🤸‍♂) has evolved into evangelizing data science via teaching, writing, building software, and more to generate extra income.
So, how can you monetize your data science skills to make a bit of extra cash? We’ll explore some of my favorite ways to earn some 🍞 and build data skills.
Maybe you want to generate extra income to pay for those pesky subscription services. Or maybe you plan on jetting off to Bali and embracing a more bucolic relationship with remote work. Whatever the reason, I suggest setting a tangible goal and outline the steps or micro-goals you’ll need to get there.
My goal was simple. I wanted to spend a few extra hours every week to improve my knowledge of data science, and get paid to do it. I set my goal at $50 per week, and went about recording my milestones in both revenue and data science skills.
Effort: generally, how much time will I need to commit per week?Value: normalizing for effort, how much is a viable payout?Difficulty: how much domain and/or technical expertise do I need?Consistency: how frequently can I expect to be paid?
Effort: generally, how much time will I need to commit per week?
Value: normalizing for effort, how much is a viable payout?
Difficulty: how much domain and/or technical expertise do I need?
Consistency: how frequently can I expect to be paid?
“Through a combination of algorithmic and editorial curation, posts on Medium get spread around based on interest and engagement. Some get hundreds of thousands of readers — and not because they were written by famous people. Medium is not about who you are or whom you know, but about what you have to say.”
— Medium
As a reader, you’re well acquainted with Medium and the amalgam of galvanizing perspectives that exist on the platform. It seems daunting to refine your brand and push content out into the world, but it can be empowering and lucrative. After months of casual observation (and many encouraging 👏), I finally turned on the tap and started publishing content. Wielding an old MacBook and a rudimentary understanding of prose, I set out to get published in my favorite Medium publication and earn enough to cover the $50 yearly subscription fee.
If you’re unfamiliar with how publishers earn money, check out this guide outlining the Medium Partner Program. Basically, you can publish content that’s eligible for revenue commensurate with views, claps, reading time and conversions (non-members sign-up for Medium). As a ‘fresh’ perspective in the Medium ecosystem, I look to established authors like Susan Li and Cassie Kozyrkov for inspiration.
Effort: 3Value: 1Difficulty: 2Consistency: 2---------------Overall Score: 2 
You can pour unlimited resources into your authorship skills, research, network etc. with low confidence of earning positive ROI. While most writers struggle to gain consistent enough exposure to make money (only 8.1% of writers made more than $100 in January 2019), it’s also a great way to market your talents and practice writing. It takes a powerful voice, engaging and original content, efficient distribution channels, and a bit of luck to be successful on Medium. Like anything else, it’s a discipline that requires a lot of time and nuanced improvement to find your groove.
It took me 30 days to hit my goal of making $50 in total revenue, and only 10 days to get my content published on Towards Data Science. Now, my focus is on writing an article every week and iteratively growing my following; it’s certainly a work in progress!
“APIs are the windows to new ecosystems.”
— Bala Iyer in Harvard Business Review
Developers use APIs to leverage external software in their own programs. By promoting interconnectivity, companies and technologists can share their work and empower the growing number of data professionals in building amazing solutions at variable scale. Although open-source-software encourages “freedom in society through education, collaboration, and infrastructure”, API revenue fuels some of the most beloved and lucrative tech companies in the world. According to the Harvard Business Review, “Salesforce.com generates 50% of its revenue through APIs, Expedia.com generates 90%, and eBay, 60%”. Whoa!
Many of the most popular APIs are developed by companies rather than individuals. But as the number of talented data professionals increases, so will the demand for cheap, sophisticated alternatives built with open-source tech.
So how do you get started? An API marketplace allows developers to publish and monetize their work, without having to curate custom payment systems and complicated infrastructure. RapidAPI is my favorite, featuring ‘freemium’ and paid subscription plans for every type of API you can imagine. You may not earn passive income immediately, but you’re well on your way in showcasing unique talent and entrepreneurial spirit.
Effort: 2Value: 1Difficulty: 4Consistency: 1---------------Overall Score: 2
Here’s a quick-guide for how to deploy your valuable work to an API:
towardsdatascience.com
I haven’t yet earned a penny with this method; I chose to write about the experience (^) instead of maximizing the value of my API. However, the sky is truly the limit: there are so many cool avenues for natural language processing (NLP) and text classification, image recognition, efficient data pipelines, etc. that you could quickly publish and monetize.
RapidAPI has several categories for tailored API solutions, like sports, travel, data, finance, and many more. A quick query shows you which APIs are most popular for a given category, and therefore how wide the market is for paid solutions.
If you build it, they will come!
An obvious choice! Kaggle is a wonderful place to augment your skills and explore datasets. A subsidiary of Google, it’s an online community tailored for data scientists and machine learning engineers to collaborate in elevating the technical aspects of the field. They offer competitions for audacious technologists to test their skills in a battle for CA$H PRIZES (yes, you read that right).
Whether you’re a data novice or full-fledged unicorn, Kaggle competitions will test your domain expertise. Topics range in breadth and depth, from COVID-19 forecasting to neuroimaging and everything in-between. It’s also an amazing platform for companies/organizations to crowdsource talent; Zillow ($1.2 Million 💰), GE ($250,000 💰) and the Department of Homeland Security ($1.5 Million 💰) post Excalibur-level challenges for massive cash prizes 🏆. More than likely, you’ll simply admire the cutting-edge solutions from afar instead of bringing home the bacon, but you might as well try!
Effort: 3Value: 2Difficulty: 5Consistency: 2---------------Overall Score: 3
It’s simple: Kaggle competitions are fun! Whether or not you cash out, you’ll enjoy a challenging opportunity to put your skills to the test. Personally, I’ve only ever received intangible prizes like “kudos” and “knowledge” that accompany victory, but I have peers who’ve made good money from Kaggle.
Competitions can also be a great way to expand your network and dive into unexplored facets of data science. LeetCode is another platform for expanding your data science repertoire by taking part in interesting challenges, with emphasis on the interview process. Regardless of the specific outcome, you’ll definitely be a better technologist if you choose to partake.
Like a moth to the flame, intelligent job-posting platforms connect freelancers (🦋) with vetted opportunities (🔥). Instead of managing your own business development pipeline, you simply pay a percentage of your rate to hijack data roles in the open market. From full-time opportunities to one-time trainings, these platforms scale to fit your needs and marketability.
Depending on your level of expertise, freelancing can turn into much more than a mere side-hustle. I know of many data professionals who’ve ditched the traditional 9–5 for full-time freelancing — and you can too! But for the purpose of this article, I’ll focus on limited opportunities with low-maintenance time commitment.
Effort: 3Value: 4Difficulty: 2Consistency: 3---------------Overall Score: 3
My rating criteria for this approach feels especially subjective. I used Upwork and Toptal exclusively, both having efficient onboarding processes and lots of data science/developer roles. Bidding for gigs takes some getting used to, and finding a niche hourly rate depends on a lot more than mere years of experience. That being said, it’s pretty easy to earn money if you’re diligent and accepting of a platform that skims roughly 20% off the top.
I earn about $100 per month via freelancing, but I’m quite picky when bidding for roles I’m interested in. As a consultant by day, I know my hourly rate and the specific skills/technologies that are highly valued: I focus my time on finding opportunities rife with text analysis and/or NLP.
There are a million-and-one ways to learn data science and general programming skills. Lucky for us, there’s a HUGE market for online learning as it pertains to building valuable skills and an impressive portfolio of work. While a lot of e-learning advocates suggest building and/or curating your own personalized course, I’m much more interested in helping administer a curriculum vetted by others. Late last year, I signed up to be a Teaching Assistant (T.A) for a prominent data bootcamp via the University of Minnesota, Twin Cities.
Effort: 4Value: 2Difficulty: 3Consistency: 5---------------Overall Score: 4
This particular course runs in increments of 6 months, and covers most of the tech you’d find in any data science handbook: Python / R programming, database querying and administration, front-end web development, statistics, machine learning, and more. Because I don’t have to manage curriculum updates, new student onboarding, or any of the administrative tasks associated with building a course, I get to focus on the art of teaching. I dedicate nearly 15 hours of my time every week to support students as they traverse the course material, for relatively little pay. Despite the effort, the time commitment is super consistent and I get to work with students of all types of academic/professional backgrounds. Check out some upcoming courses for ideas 📚:
www.switchup.org
www.coursereport.com
Compared to building your own curriculum, this approach requires significantly lower investment up-front but limits your potential revenue ceiling; like me, you’ll likely receive a modest bi-annual stipend. All you have to do is reach out to your alma mater or connect with an ally in the field (like me!), and substantiate your knowledge of various topics.
If you’re interested in learning more about the structure, demand, logistics, and/or onboarding process, feel free to leave a comment below or message me directly 😃.
If you don’t have the bandwidth to commit 6+ months of your time to teaching in a structured environment or feel uncomfortable socializing such a broad range of topics, then tutoring might be up your alley. By buttressing the growing demand for online learning, tutorial services are highly customized and increasingly profitable.
A lot of private companies offer tutorial services to drive down attrition and improve student satisfaction. In lieu of other expensive support mechanisms, tutors provide high value compared to their cost.
Effort: 3Value: 3Difficulty: 2Consistency: 5---------------Overall Score: 4.2
I set my hours, number of students I want to work with, and the topics I’m comfortable teaching. I’m responsible for curating an efficient, comfortable learning experience in exchange for the attractive hourly rate of $30. It’s low-maintenance, flexible and profitable enough to be my FAVORITE side hustle in the data science ecosystem.
There are so many more options for generating extra revenue and learning along the way:
Publish your own data science textbook/coursework
Contribute to Stack Overflow
Create content with affiliate marketing
Build a cool app
Predict domain names
... and the list goes on!
At the time of writing, I earn about $350 per week with a mixture of the outlined approaches. Not bad for a side hustle! I exceeded my initial goal of $50/week, and have continued to learn a LOT along the way. The best part? These methods are totally reproducible and ripe for the taking. All you need is a bit of resolve and the desire to learn.
I’m currently working as an educator and tutor (part-time) while writing as much as possible about my experiences on Medium. I frequently peruse Kaggle for challenges, try to publish an API to the marketplace every quarter, and consistently bid for interesting freelance opportunities. This is the best combination I’ve found for profitable side hustlin’, and helps me grow my network and data science skillset. Depending on your level of expertise, interest, and availability, you can use these methods to jumpstart and/or augment your own opportunities.
Thanks for reading! Please comment below with any additional side hustle ideas or constructive feedback. Follow me (Garrett Eichhorn) or connect with me on LinkedIn.