Introduction

It's challenging to land your first office job in data science. For example, some job postings by recruiters appear to have high criteria, stating that an entry-level position requires a master's degree! However, who is to say that earning money in Data requires a real job? It's 2023, we have weathered a pandemic that has taught us more about money than any recession has! Everyone wants to be able to work from home and set their own hours, and freelancing is one of the finest ways to achieve this. And no, I'm not only referring to platforms like Upwork or Fiverr; instead, let's go through some of the simplest ways to make money in this industry.

Side Hustles in Data Science

1. Participating in Competitions and Hackathons

Given its popularity in the industry, data has become one of the tech occupations with the highest salaries. Competitions for data science are frequently held on websites like Zindi and Kaggle. Some of these hackathons offer cash awards of up to $5,000 or even job interviews as incentives. One is guaranteed to make money from these events as long as they have the necessary talents and are willing to put in the work. These websites let you create a portfolio as well because they are well-known platforms in the data industry.

2. Tutoring and Teaching

As previously mentioned, the field of data is expanding, and more people are making career changes to enter it. A simpler approach to freelance is to provide training sessions either physically in training facilities or as online courses. You might think about providing coaching and tutoring to those looking to enter the industry and selling your knowledge as courses with educational chapters tailored to your tech stack.

3. Freelancing Sites

A thorough approach and several crucial steps are required to get started with data science freelance sites. Create a well-structured profile to start off with that shows your data science abilities, know-how, and pertinent work history. Second, show off an impressive portfolio of prior projects and case studies to convince prospective clients of your skills. Third, regularly participate in pertinent data science discussions on the platform for freelancing to network and enhance your reputation. Fourth, be sure to carefully define your project scopes and price to draw clients while providing fair compensation for your services. Finally, maintain a high level of professionalism, attentiveness, and fast delivery in order to get great ratings and establish long-lasting relationships on freelancing platforms.

4. Technical and Content Writing

For data scientists, technical writing is a crucial side job that entails producing clear and simple documentation for algorithms, codebases, and data pipelines. Data scientists can better collaborate and understand one another by using this ability to successfully convey their work to both technical and non-technical stakeholders. On the other hand, writing instructional blog posts, tutorials, and articles as a side job in data science entails showcasing data-driven insights, best practices, and market trends. Data scientists may build their personal brands, exchange expertise, and possibly make extra money by producing technical and content as a side job. Here are links to sites to can pitch topics to or sell cold emails to for technical writing, Paid Community and Community Writers Program

4. Consultancy Services

Many start-up companies looking to establish a data science department could benefit from consulting based on their software stack and business plan. Consultancy, like the other gigs mentioned, necessitates the expertise of a data scientist. Building projects and machine learning algorithms for these companies, on the other side, could help you make money quickly! These projects could be rented out or sold for a profit. For example, creating facial recognition algorithm for a client.

Conclusion

These are some of the best-paying and easiest-to-learn freelancing opportunities for data scientists. However, it is strongly advised that before applying, one should increase their skill set. Freelancing takes time and requires you to put in the effort and show up on a daily basis. Fortunately, there is a wealth of information available online on how to get started with each of the concepts suggested above.

Follow me on Twitter for more information on data science.