For people with a strong analytical mindset, a flair for data, and a desire to make bold contributions to the companies they work for, becoming a data scientist is a smart option. While some professionals work as employees, others choose to become freelance data scientists. If you choose to take this path, you can enjoy a potentially higher level of income and the flexibility to set your own work hours. In addition, you are the primary decision-maker in your career, so you can choose which clients to work for and which projects to spend your time working on. First, however, you must walk through the important steps to become a freelance data scientist.
What Do Data Scientists Do?
A data scientist draws on expertise in mathematics, statistics and machine learning to extract meaningful insights. Data is initially pulled from a variety of sources, such as customer records, sales records and many other sources. This data usually must be cleaned, which means that issues related to incomplete, incorrect, corrupted or duplicate information are removed or corrected. After doing so, the data scientist completes a detailed analysis of the data and extracts valuable insights from it. This is done through the use of technologies, machine learning and critical thinking. The data and insights are then prepared for presentation. Numerous visualization programs may be used to clearly present the insights to key decision-makers in the company.
Steps to Become a Freelance Data Scientist
Becoming a freelance data scientist is a process that could take years to do. However, if you have already developed the necessary skills and have been working in the field, you may be closer to living your dream as a freelance data scientist than you think. What does it take to become a freelance data scientist?
1. Learn Technical Data Skills to Become a Freelance Data Scientist
Computer science is an important aspect of working as a freelance data scientist. The technical skills needs to become a freelance data scientist are:
Data Mining: Data mining involves identifying both patterns and outliers in data sets to identify correlations and insights. This information may then be used to predict future outcomes, identify trends and more. You can learn more about data mining through certification programs at the University of Texas, Udemy and others. You can also follow blogs like Orange and Deep Data Mining to get yourself updated with best practices and techniques when it comes to data mining.
Natural Language Processing: Natural language processing is a type of artificial intelligence that enables computers to understand human language text and voice data. This includes identifying sentiment and intent. Coursera and Stanford University offer natural language processing educational programs that you learn the fundamentals.
Computer Vision: Computer vision is similar to natural language processing in that it allows computers to extract data via artificial intelligence. With computer vision, however, meaning is pulled from videos and images. You can expand your knowledge of computer vision through courses at Udacity and Udemy.
Data Visualization: Data visualization describes the visual presentation of trends, patterns and insights from data sets. This may be in the form of charts, graphs and other types of visual representations. You can expand your knowledge of data visualization through courses at Udemy and Boston University. Storytelling with Data and Information Is Beautiful are blogs that can help you to expand your knowledge as well. Learning to use visualization softwares like Tableau, Excel or Power BI also helps because they are widely used in many companies to transform and understand data better.
To support these main domains in data, it also helps to obtain a good baseline knowledge of relevant programming languages. What are the best data programming languages to learn to start working as a freelance data scientist? Focus your attention on popular and in-demand ones like Python, R, Java and SQL. If you prefer easy-to-follow tracks, learning platforms like Datacamp, Codecademy, or Audacity offer lots of data courses with lessons and use cases for you to practice.
2. Create a Data Science Portfolio
Before you start actively searching for clients, you should assemble a portfolio showcasing your professional experiences. As you explore ideas to create a freelance data science portfolio, consider using an intuitive format. For example, list your general skills or knowledge areas as a broad category, and create sub-listings for each project that applies to that category.
You can also consider how to create a freelance data science portfolio that stands out. While a black-and-white paper presentation offers basic information, you could consider using a color presentation or even a digital layout. For example, you can use resources like Github, Wix and Weebly to create an online portfolio detailing some of your best work. Keep in mind that part of your work as a data scientist will be to present the results of your analysis, so the ability to create a winning portfolio will speak volumes about this aspect of your skills.
There are other ways to create an excellent portfolio for prospective clients to review. Through Kaggle, for example, you can connect with other data scientists for collaboration, publishing and more. You can find project ideas to participate in through various hackathons. Some of these include Food Demand Forecasting, HR Analytics and more. These can then be assembled into your portfolio. What are some great data science portfolio ideas? Consider analyzing female representation in Hollywood, customer segmentation with Python, understanding browsing patterns, identifying college preferences among students and more.
Best Ways to Find Your Clients as a Freelance Data Scientist
It can be challenging to find clients as a freelance data scientist initially, but the good news is that there are several avenues that you can take advantage of. For example, you could reply to job postings on major marketplaces and present yourself as a freelancer. Euro Tech Jobs, Angel and Techno Jobs are some of the platforms that you can use to locate professional work.
While this may be a bread-and-butter option for you, you should not stop there. For example, you can also create a professional website. Use LinkedIn to promote your website, and drive traffic to it through a blog. A blog also enables you to showcase your expertise and can be a great marketing tool. You can document and write about your projects on sites like Medium and Kaggle, and you can link your project descriptions back to your website.
You can also attend meetups and join professional organizations. Networking enables you to meet decision-makers and to establish solid relationships. These relationships can open up opportunities for you and may give you a leg up during the hiring process. In addition to attending meetups, you should plan to attend special data science events. A few of these are the International Conference on Mass Data Analysis of Images and Signals, the Industrial Conference on Data Mining and the Business of Data Festival.
You can also pay for marketing. For example, you can and should develop social media profiles specifically for your business. These will be separate from your personal profiles. Consider using platforms like Facebook, Twitter and others. You can then pay to promote these profiles through the platforms. This increases your visibility as a professional freelancer and could drive traffic to your website and blog posts.
Before getting involved in the process of becoming a freelance data scientist, you should assess your professional expertise. Many of your potential clients will prefer to hire a freelance data scientist with some experience rather than an aspiring freelancer with minimal experience. Your learning and experiences may have provided you with some experience up to this point, but you should determine if your experience is well-rounded enough to qualify for open jobs. If not, look for suitable entry-level positions that will help you to refine the skills and expertise that you may be lacking.
Soft Skills You Need to Succeed as a Freelance Data Scientist
As soon as you embark on an educational path to become a freelance data scientist, you should look for opportunities to hone your soft skills. There are a number of softs skills needed to start working as a freelance data scientist, and these will be evident to those in a hiring position as you apply for projects. For example, a data scientist needs to have excellent written and verbal communication skills. The ability to present concepts visually is also essential.
A successful data scientist has a passion for problem-solving and the personal motivation to continue working until the challenge at hand has been resolved. You may need to demonstrate to a prospective client that you can work well independently. However, some projects may require you to collaborate with a team of professionals, so you also need to be a team player.
There are also soft skills needed to be successful as a freelance entrepreneur. For example, you need to set up effective business management systems to track income and expenses. You must be well-organized and able to complete projects on a deadline. Marketing and sales are critical as you will need to drum up new clients. Some of your business will come from established clients, so you also need to be skilled in relationship building.
How to Invoice and Get Paid as a Freelance Data Scientist
As a freelance data scientist, you must decide how you will get paid. For example, will you collect a lump sum at the end of the project or require payments once specific milestones in the project are completed. Some freelance data scientists collect an upfront deposit before they begin working on a project.
Regardless of the system that you choose, you will need to create and submit invoices to collect funds payable to you. Invoices are also essential for VAT management. In addition to creating invoices, you need to offer a convenient payment method to your clients. Most will prefer to pay via a debit or credit card transaction, so you need to set up a credit card payment portal. In addition, you must keep impeccable financial records. This facilitates your decision-making processes as an entrepreneur, and it is essential for tax purposes. Financial management includes tracking expenses, accounts payable and income received.
You can make light work out of all aspects of financial management through Xolo Go, a solution that allows you to invoice your cross-border clients with full compliance. Together with an instant invoice generator and automatic VAT calculator, you’ll also have your own business bank account and full control of your finances with an easy-to-use business dashboard. Issue your first invoice within 10 minutes here.
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