Photo by Vlada Karpovich
 

I started teaching myself data science in January 2020. At that time, my only goal was to land a full-time job in the field.

However, although data scientists are paid very well, it takes a long time to climb the corporate ladder and build wealth with a 9–5 job.

Due to this, I started looking for different ways to apply my data science skill set outside my corporate job. Since my full-time role is flexible and allows me to work remotely, I have around 3 to 4 hours of free time every day that I use to generate a side income.

I have now successfully built multiple revenue streams outside my full-time job that provide me with approximately $3,000-$3,500 every month.

Many of these income streams are passive, which means that I earn without having to actively invest my time and effort into them.

In this article, I will show you how I did it. If you are a data scientist or aspire to become one, you can use some of these ideas to monetize your skill set.

 

1. Writing Online

 

I make a significant portion of my income from writing online. This includes creating data science tutorials, tips, and advice. I started blogging on Medium in May 2020.

After building an audience on the platform, I have been approached by employers to write freelance articles for their brands. In the past two years, I have created a variety of blog posts, tutorials, whitepapers, and SEO content for six different companies.

 

Takeaways:

 

a) Just Start Writing

You don’t have to be a subject matter expert to start sharing what you know. In fact, according to Rachel Thomas, the co-founder of Fast.AI, you are best positioned to help someone one step behind you.

This means that if you just learned a concept, it is still fresh in your mind. You can easily simplify this and explain it to another beginner in the field — and would be able to do this better than an expert who has forgotten what it was like to be a beginner.

b) Market Yourself

To grow as a content creator, you need to market yourself. Create a compelling LinkedIn profile and share your articles on the platform. Post regularly, join data science groups and connect with other professionals in the field.

Increasing your contacts in the data world will increase your blog views and improve your chances of landing a paid writing gig.

 

2. Affiliate Marketing

 

When teaching myself data science, I took many online courses on Udemy, Coursera, and Datacamp. I would recommend these courses to co-workers and peers who wanted my advice on how to become a data scientist.

After a while, I realized that I could get paid for sharing my learning path with others. Affiliate marketing allows publishers to share courses with other people using an affiliate link. If someone purchases the program using their link, the publisher gets a small commission.

 

Takeaways:

 

Get Paid for Things You Already Do

Even before including affiliate links to my content, I would share learning material in almost every blog post I wrote. The only difference is that I now get paid for doing it. In fact, according to a poll from Affise, over 25% of affiliates make between $81,000 to $200,000 per year.

While I earn only a fraction of this from affiliate marketing (around $100-$200 a month every time I publish), it is a huge revenue driver for many bloggers and is definitely something you should consider adding to your content.

However, remember to be ethical and only promote products that you have consumed and benefitted from. You must also be transparent and clearly disclose the use of affiliate links to readers.

 

3. Performing Market Research

 

This might sound like an unconventional way to make money as a data scientist, but hear me out.

My first full-time data science job was in the field of marketing analytics. In this role, I learned to apply data science techniques to create personalized customer targeting strategies and drive marketing success.

I wrote an article about applying data science techniques in the field of marketing, which caught the attention of an employer who was looking to hire a freelancer with the same set of skills I possessed. He reached out to me on LinkedIn, and I am now working with the company on a contract basis.

 

Takeaways:

 

a) Select a Niche

Since I have worked in the field of marketing analytics for some time, I am familiar with some of the biggest challenges faced in the industry. I also know how to use data to solve them.

This is my niche. It is difficult to find someone with the same combination of skills that I have, which made me a strong contender for this freelance job.

If you are an aspiring data scientist, I suggest selecting an area of specialization when starting out. This can be finance, marketing, healthcare, insurance, or anything else you enjoy doing.

The value of data scientists lies in their ability to solve problems. If you can do this in a specific industry, you have a competitive edge over other data scientists.

I can say with confidence that the job I landed would not have been the right fit for someone without domain experience, even if they had a Master’s degree or Ph.D. in data science.

b) Build An Online Presence

I got this role only because the employer found my Medium profile while browsing through the platform. I have worked with other marketing data scientists, many of whom are more experienced and know the field better than I do.

Regardless, I got the job because the employer found me first — thanks to my blog posts and social media presence.

If you don’t have the time to write articles about your work, I suggest that you at least create a portfolio website that contains a summary of your skill set. Include a link to the site on LinkedIn and other social media platforms so potential employers can easily find you when hiring for open positions.

If you don’t already have one, read this guide for tips on how to create a portfolio website.

 

4. Creating Courses & Workshops

 

I have conducted workshops on topics like data collection and analytics to teach non-technical students to work with data. This involved hours of preparation, since I had to familiarize myself with every concept I was teaching and ensure that I wasn’t making any mistakes.

The best part about becoming an instructor was that teaching solidified my understanding of the subject and dramatically improved my ability to break down complex concepts to beginners in the field.

 

Takeaways:

 

Teach What You Know

I started learning data science around two to three years ago and am hardly an expert in the field. However, I have learned a lot during this time and can teach it to a group of people who will benefit from learning my skill set.

For instance, as someone who has worked in the fields of data science and marketing, I am well-positioned to teach data literacy skills to marketers. I can also teach data scientists about marketing analytics so they can gain domain knowledge and potentially land a job in the industry.

Even if you are an aspiring data scientist who is in the learning stage, you can earn a side income from sharing what you know with others. Often, this works best when you combine a unique set of skills that not many people have.

For instance, an “Introduction to Python” course may not pique students’ interest since similar programs are abundant on the Internet. However, an “Introduction to Python for Finance” course is more specialized and likely to attract a group of viewers who are interested in predicting the stock market.

YouTube, Udemy, Pluralsight, and Thinkific are some platforms you can use to build and share online courses.

 

5. Other Freelance Tasks

 

Additionally, I have worked on freelance data science tasks like data collection, model building, and dashboard creation for clients. While most freelancers swear by platforms like Upwork and Fiverr, I got most of my job opportunities from Medium, LinkedIn, and my website.

Here are some articles that have landed me freelance gigs:

Customer Segmentation with Python: I ended up building a K-Means clustering model for the client and presented my results in a slide deck.
How to Collect Twitter Data with Python: I guided the client to collect Twitter data using a Python API.
A Complete Data Analytics Project with Python: I performed a similar competitive analysis for the client’s product.

 

Takeaways:

 

Build Projects: When an employer is looking to hire a freelancer, they often scour the Internet to find people working on similar projects. Building projects and posting about them frequently will improve your odds of getting noticed and landing a job.

 

Conclusion

 

Regardless of where you are in your data science journey, you can start building multiple streams of online income today.

Start by writing online and teaching what you know. This can be done on publishing platforms like Medium. You can even create your own blog site using web development services like Wix and WordPress.

Then, choose an area of specialization within data science. I suggest getting a full-time job in the field, since this will provide you with industry specific experience that cannot be learned elsewhere.

Finally, use your domain experience and data science skills to branch out into freelancing and course creation. You can also offer consultation session and conduct data science workshops in your area.

 

“The secret to getting ahead is getting started.”— Mark Twain

 
 
Natassha Selvaraj is a self-taught data scientist with a passion for writing. You can connect with her on LinkedIn.

 
Original. Reposted with permission.