Data analysts are in high demand, and a number of platforms have emerged to make it easier than ever for data analysts looking into a path off the beaten track—one of them being freelancing!
If you’ve ever wondered about what it takes to become a freelance data analyst, or are curious about the field in general, this article will explain everything from whether this is the right career move for you, current earnings, and a detailed guide on how to get started—even if you’re just starting out in data analytics altogether!
Here’s what we’ll cover:
- What does a freelance data analyst actually do?
- Is a career as a freelance data analyst right for you?
- How much could you earn as a freelance data analyst?
- How to get set started as a freelance data analyst (a step-by-step guide)
- Key takeaways and next steps
You can use the clickable menu to skip to any section, if you’d like. Now, let’s begin!
1. What does a freelance data analyst actually do?
Freelance data analysts work independently with clients or companies on analytical projects that can vary greatly in complexity and contract length. Some freelancers work through agencies and are placed with clients for contracted periods. Others work as independent consultants who work directly with the client, which generally means that you can charge a much higher hourly rate than those working through an agency.
Freelance data analysts typically work on the same types of projects as full-time employees would. The difference is mainly in how long the projects last. Some are one-off projects that can be completed in an afternoon–regression analysis in a Jupyter notebook, cleaning data, or setting up a database. Others are longer projects that can span months to a year, where you work closely with teams to create recommender systems, dashboards, and consult on advanced machine learning systems.
2. Is a career as a freelance data analyst right for you?
Being able to consult on projects with different clients means that you gain broader experience faster than if you worked full-time for a single company. Having a view into different applied machine learning use cases and data analytics best practices is incredibly valuable when attaining professional experience, particularly as a new entrant to data analytics.
Companies appreciate this “consultant” mindset, and it’s worth keeping in mind that if the freelancing life no longer suits you, having a wealth of analytical knowledge at your disposal will make you an attractive candidate to recruiters.
If you have a niche and specialised data skillset, say you’re an expert in time series forecasting within the clean energy industry, you’ll be able to charge higher rates than you would be paid as a full-time employee embedded within a larger company.
Another great reason to freelance is that you’ll have control over the types of projects you want to take on. Oftentimes, employees have to work on the company’s defined priorities and product roadmap, which may not always be relevant to your interests, or where you would like to take your career growth to.
As a freelancer, ultimately you have the power to decide whether you would like to take on a particular project or client, and limit your commitments upfront so you can move on to another exciting gig once you’ve delivered your work.
However, as an independent freelancer, you’re responsible for managing aspects of the business that employees don’t need to consider. This means figuring out your daily workspace (such as your home office, co-working spot, office rental), keeping track of invoicing and bookkeeping, and most importantly, calculating an appropriate hourly or project-based rate that would provide your desired income after taxes and business expenses.
This last one can be tricky when you’re just starting out: it might be hard to find projects that pay decently as it generally scales with acquired experience. It’ll likely take some trial and error with your billing model before you hit on a reliable business model.
Most importantly, you’ll have to create your own pipeline of clients. This means becoming your own sales person, which is not for everyone as it requires soft skills such as persuasive communication, negotiation, and managing client expectations over the ups and downs of a project.
How will you advertise your services? Can you build your own website, or do you contract it out? How do you find leads for prospective clients? How do you brand yourself to stand out among your competitors on sites like Upwork? These are some questions worth considering before embarking on the freelance analyst path.
3. How much could you earn as a freelance data analyst?
There are two ways to price your services: with an hourly rate or a project-based rate. On Upwork, you can look through more than a thousand posted jobs that vary in pay, experience level, and duration. Some projects pay as little as $30 USD for an hour’s worth of work from those looking to get assistance on creating a dashboard in Power BI.
Others pay a fixed price in the low thousands per month for full-time work on a project, with the possibility of extending the contract. On Indeed, it’s easy to filter jobs by hourly range anywhere from an average of $65 USD to more than $150 USD an hour.
Getting a sense of how to charge project-based rates is a bit harder: you’ll have to break down the work in a proposal, suggest a timeline for each deliverable, and calculate the number of hours you’ll need for each one.
For a freelancer’s perspective on pricing models, we’ve written a pricing guide that shares advice on the pros and cons of charging by the hour or project.
4. How to get started as a freelance data analyst (a step-by-step guide)
As data analytics is a highly lucrative field, you can expect the freelancing path to be fairly competitive, which can be intimidating even for experienced analysts to transition into. Take a look at our guide here on best practices for getting started.
1. Establishing your area of expertise
The first question to ask yourself is: what kind of freelance data analyst will you be? Data analytics is a large field that is growing as new state-of-the-art frameworks get released all the time. It’s helpful to narrow down your interests and experiences by these two categories: domain knowledge and technical skills.
What industries are you interested in, or already have professional experience working in? When you’re just starting out, it’s best to pick just one. This can be healthcare, education, government, energy, and retail—any one of these industries has a growing need for experienced analysts to consult on a project or two.
Ideally, this should be something you’re also interested in beyond just securing gigs, as you’ll be spending time staying on top of the news and learning about the specific analytical problems that your clients generally encounter. Going the extra mile in this way will establish you as a domain expert.
Then, you’ll need to consider what type of analyst you’ll be, whether you’ll be focused on natural language processing, computer vision, sentiment analyst, time series forecasting, or spatial data analysis. Will you code in Python or R? Or will you be well-versed in popular industry tools like Power BI, Tableau, or Looker? Take a look at projects you’ve done to create a comprehensive list of skills to get a sense of where you might gain a competitive advantage as a burgeoning expert.
2. Publish a portfolio
Whether you’re already an experienced professional or just starting out, clients generally want to see what you’ve previously achieved before they award you a contract. At a minimum, you should have a website that acts as a portfolio, showcasing your projects and describes your skillset. Creating a web portfolio can be as easy as building one through WordPress or Notion. A search on Google or Towards Data Science provides examples of what a data analyst portfolio should look like and contain.
To improve your SEO (which increases the chance that new clients can stumble on your website when they’re looking for help), it can be helpful to include any positive testimonials you’ve received for previously delivered projects. If you’re still looking for your first client, write a few blog posts that either explain the code in your portfolio projects, or dive into a popular analytical challenge and explain it to your readers.
This not only gives prospective clients a way to evaluate your technical skills but also important soft skills like written communication—which is important as clients are not just looking for analytical help, but the ability to explain complex statistical models to their business stakeholders.
3. Finding (and landing) clients
The first client is the hardest to land. After that, you can use their testimonials or referrals to land the next one, making the process easier over time. But how do you find the first client?
The answer is, as always, networking. Most of your initial leads will come from your existing network. If you’re leaving an established career to freelance, this can mean sending a brief message to former coworkers and people you’ve met at industry conferences. It can also be helpful to create a public LinkedIn post announcing your new venture–that way, the net will be cast wider beyond your immediate professional contacts.
You can also attend in-person or online conferences to expand your network. Popular tech conferences like Collison are a great way to make connections with all kinds of companies in analytics. Talking to companies directly at conferences is a great way to better understand their pain points, and provides an opportunity for you to follow up later with a proposal for work that would solve a problem for them.
Even if there is no immediate need for work, reaching out to them ensures that you’re on their radar should the proposed project get the green light one day. Outside of large conferences, you can also attend your local tech meetup (PyData has chapters in almost all major cities and many smaller ones) to meet developers and market your skillset.
4. Business operations
As your business grows, it’s important to keep your business operations organized, as things can get complex very quickly! A smooth system will also impress your clients, as they will be confident in your abilities to handle large projects for them. You will need to figure out the legal requirements behind running your business in your country, creating a boilerplate contract that specifies preferred payment terms, setting up an invoicing system, and keeping track of payments and taxes.
5. Key takeaways and next steps
It can be tough to figure out whether being a freelance data analyst is the right step for your career. As we discussed, it’s helpful to take a look at your strengths and interests first. Looking at job opportunities on platforms like Indeed and Upwork can also give you an idea of the going market rate for different kinds of analytical work.
After, you can follow our guide on how to systematically set up your own freelancing work, which covers everything from defining your offered services to marketing yourself with the goal of landing your first client.
To learn more about what a career in data analytics might entail, check out this free, 5-day short course or read the following introductory guides: