Why Freelance Data Analysis May Be the Right Career Path for You

Data analysis is on the rise. Companies of different sizes and from every industry are using data analytics to not only examine historical transactions and events, but also to understand emerging trends in customer or user behavior and predict the future.

From physical sensors to cookies on website browsers and mobile phones, tech devices are increasingly collecting data in real time, leading to new opportunities to make more informed, data-driven business decisions.

Many people pursuing a career in data analytics envision a full-time, in-office role with all the perks commonly found at tech companies. But there’s an alternative that provides more freedom and flexibility: freelance data analysis.

A freelancer is self-employed and sells their time and services to organizations on a contractual basis. These jobs are growing in popularity. Today, one-third of workers freelance, with almost half of millennials pursuing freelance work.

 

What Is Freelance Data Analysis?

Data analytics is often combined with data science, but the two concepts actually are quite distinct. Data science is a macro field involving a mix of programming and statistical analysis, building algorithms and predictive mathematical models to process and manipulate data. Data analytics involves the statistical analysis of data sets to find actionable insights, often to inform business decisions in marketing, pricing, sales, and product development.

We use the term “big data analysis” to describe the interpretation of large and complex data sets that cannot be processed with traditional applications.

Freelance data analysis implies that you are working for yourself as an independent business owner—finding your own clients and projects; taking care of your own accounting, marketing, and insurance; and managing your own time, billing, and rates.

To learn more about the differences between data scientists and data analysts, including potential career progression and required skills, check out this Springboard resource.

 

Why Choose Freelance Data Analysis?

Freelance data analysis provides the ability to “be your own boss.” You can work remotely and take on clients from anywhere that meet your project conditions and are requesting work in your areas of expertise. This can offer a better work/life balance and freedom of choice in creating a schedule that fits your lifestyle.

The challenges around freelance work are the unpredictability of the workflow, the need to constantly market yourself and maintain an attractive online presence to attract job offers, the lack of employment benefits like health insurance and 401(k) contributions, and the social isolation.

 

What Skills Will You Need to Succeed?

Most data analysts have a STEM background and a solid quantitative foundation. Data analysts may clean data sets, enter data into online systems or spreadsheets, create dashboards for key KPIs, and turn data into content for reports or creative visualizations.

You will usually need to know at least one data processing language, like Excel, Tableau, and SQL. Other general knowledge and skills required to succeed in data analysis include an analytical mindset, mathematics, statistics, business acumen, domain expertise, and data visualization.

In addition to general competence in data analysis, freelance data analysts need entrepreneurial skills, such as marketing, personal branding, social networking, lead generation, sales, content writing, accounting, and budgeting.

Today, it is not necessary to earn a degree specific to data science. There are myriad free resources for self-learners. Springboard’s data analysis learning path covers exploratory and predictive statistics, basic computer programming in Python, algorithms, R for statistical analysis, Unix, and Git, and is appropriate for people with very little prior programming or data analysis knowledge.

There also are more formal data science bootcamps, both offline and online, that offer a comprehensive data analytics education.

 

How Should You Get Started in Freelance Data Analysis?

First, in order to attract freelance data analysis jobs, you will need to build an online portfolio and marketing platform. Make sure to update your LinkedIn profile and ask for referrals from past employers and colleagues, as this can help you stand out from the crowd.

It can be useful to create your own website with a custom domain where you can list past projects, clients, and recommendations.

Next, you will probably need to register with one or more freelance platforms to search for gigs.

Fiverr is a freelance services marketplace that offers a wider range of projects at affordable prices. On Fiverr, you don’t browse job descriptions, but you can offer a gig at a certain price point and see who needs your services. Many of the data analysis freelancers on Fiverr offer services such as data source connectivity, model documentation, model validation/testing, interactive/animated visuals, web embedding, and toolbar integration.

Upwork is one of the most popular freelance platforms with over 2,000 listings for data scientists and data analysts. You can start by submitting an online cover letter and portfolio to different postings and then improve your search ranking as you build your client and experience list. It is now harder for freelancers to receive approval for their initial application to work on Upwork. To boost your chances of acceptance, choose multiple work subcategories, list the maximum number of skills, create a solid job title with the right level of specificity, elaborate on your education and employment history, and build a portfolio with several previous work samples.

Toptal is a site that prides itself on matching top freelancers with well-known brands like Airbnb, Zendesk, and Pfizer. Toptal only accepts freelancers with extensive experience, so it might be better to build your skill set on another platform first. The Toptal screening process includes a language, personality, and communications interview; in-depth skill review; live screening; and test projects.

Coding Ninjas runs a fairly judicious selection process that includes a 15-minute English test, a live interview, and a test project. Coding Ninjas matches developers, data scientists, and designers to projects based on skill set, availability, and length of project—and they do not take a cut of payments to freelancers.

AngelList is oriented toward the tech and startup community. You can browse through different remote job opportunities and contract positions. Your AngelList profile includes many personal website presences and allows you to link to other projects and companies you’ve worked for in the past. You can then submit applications and messages directly to hiring managers. AngelList also provides information on various business contacts that you can reach out to in order to propose your own projects.

Kaggle is an online community specific to data science. You can join different competitions, ranging from earthquake prediction to audio tagging for soundbites, for a chance to win credibility and cash prizes.

Additionally, there are many Facebook groups today for digital nomads, freelancers, and remote workers. By joining online communities and reading recent posts from members, you can stay up to date on new opportunities. Since some platforms charge a fee for posting jobs, Facebook is often a cheaper alternative for small businesses to connect with talent. And you can get in touch instantly through direct messages.

Once you have selected a few platforms and uploaded your profile, starting a blog on data analytics and participating in online forums can help with personal branding. You can also network with other freelance data analysts who may be able to refer you to business opportunities or provide advice and emotional support.

When communicating with clients, make sure to spend your time crafting each proposal to present why your skill set and experience are well suited for their needs. It is a competitive process and you will need rave reviews from your first few clients to improve your placement in search results and gain more credibility. The freelance job market is heavily based on trust. Many future jobs may come from past clients for whom you have exceeded expectations.

You may also want to generate leads and send prospecting emails to businesses, both inside and outside of your current network, to let them know of your services. One of the top niches for freelance data analysts is SMBs, small to midsize businesses, that need for data for marketing and growth purposes, but may not have the budget to hire top data analysts in-house.

 

What Will You Earn in Freelance Data Analysis?

As a freelancer, you can set your own rate, but you will want to price competitively in order to sustain your lifestyle and receive a consistent flow of projects.

When budgeting for specific projects, you can look at what other freelancers are charging, what the market rate is, and how much time it will take you to complete the project.

With some clients, there may be more room for negotiation. You should also try to figure out what a client’s budget is for a given project, as you can lose opportunities by unknowingly pricing outside of their ballpark.

According to Glassdoor, the average data scientist salary is $113,000, with a range of $85,000 to $170,000. Data analysts earn less at the entry level, from $50,000 to $75,000. Big data engineer salaries usually start at $70,000 and can increase up to $165,000 for a domain expert. Over time, top freelancers can earn more on a monthly or annual basis than W2 workers.

On Upwork, freelance data scientists earn a wide range: $36 to $200 per hour or $400 per project. Many projects may be completing ad hoc analyses or queries, building a data pipeline, or creating an engine to generate data-driven recommendations. If you know more specific languages, like Scala, Spark, and Hadoop, you can charge higher rates. Your rates should increase as you gain knowledge, experience, and more specialized skills.

On Fiverr, data analysis services start at $5 to $10 and go up to $100 to $200 for more complex services like geospatial data analysis with visualization and machine learning/deep learning with computer vision.

So what exactly should you charge? As mentioned earlier, your earnings as a freelancer are entirely up to you. Charge as much as you feel comfortable with charging, based on your needs and the relevant data you collect.

 

When Is the Best Time to Make the Jump?

Before handing in your resignation at your day job, you will probably want to make sure certain boxes are checked: off, saving up for living expenses for a few months in case workflow is slower than expected, setting up a comfortable home office, and finding an accountant. Many freelancers share that it is much harder to do taxes, so you will probably want to seek professional help to navigate working with clients across different countries.

If you want to test the waters before switching entirely to freelancing, you can try signing up for a freelance platform and take on a few side gigs in the evenings or on weekends to get used to the pace of work, frequency of job offers, and your own skills at independent time management.

If this is the right time for you, consider Springboard’s Data Analytics Career Track. You’ll learn both the technical and business thinking skills to get hired—job guaranteed!