9 Skills You Need to Become a Freelance Data Scientist in 2021

Whether you would like to supplement your current income, need a gig to fill in a gap in your resume, or looking to become your own boss, becoming a freelance data scientist may be the career path for you. The demand for data scientists across industries has increased by 417% over the past year, making it easier to find good freelance opportunities for 2021 and beyond. 

If you are a qualified data scientist, you can take advantage of these opportunities right away. However, if you don’t, you’ll need to acquire or brush up on some technical and non-technical skills that are outlined as follows:

Math Skills

You should have an extensive understanding of some of the key concepts of Maths:

Statistics

Statistics provide the necessary methods to gain valuable insights from data. The more statistics you know, the more you can analyze and quantify the uncertainty in a dataset. Make yourself familiar with terms such as mean, median, mode, standard deviation and distributions. You should also know when to apply the multiple sampling techniques and what to do to keep out bias in the experiments. You should also know how descriptive and inferential statistics are used to generate and display predictions. 

Probability

A firm grip on key concepts of probability such as Bayes theorem, Central Limit theorem, probability distribution functions, random variables and expected values can go a long way in identifying key trends in clients’ data.

Linear Algebra and Calculus 

To build a machine learning model, you should know a fair bit of calculus. Knowledge of linear algebra concepts such as matrices and vectors is essential when working with algorithms. It will allow you to make minute improvements in an algorithm to affect the end result significantly. Companies with high volumes of data typically require this skill. In businesses where data define products, small tweaks in predictive performance and algorithm optimization can lead to better productivity. 

Multivariate calculus

Having a grasp of concepts such as mean value theorems, product & chain rules, gradient derivatives, Taylor series and gamma functions is a must to run logistic regression algorithms.

Learn More: 6 Pro Tips To Build Your Dream Data Science Team

Statistical Programming Skills

Data scientists are expected to procure, clean, mung and organize data. To do this successfully, you must be able to use a statistical programming language. Before choosing the programming language, it is essential to familiarize yourself with the industry and company you want to freelance for.  

However, when choosing, consider that over 50% of data scientists are proficient in at least one or both of these:

Python

Knowledge of Python is a key skill for any data scientist. It is a multi-purpose object-oriented language used by programmers at every step of data science processes, such as web service development, building machine learning models, data mining, and classifications. In addition to the basics, you need to have a grasp of Python libraries such as Pandas, TensorFlow and Matplotlib. 

R

Primarily used for statistical analysis, R is another key programming language data scientists should be familiar with. Used at all major tech firms, financial institutions, analyst and consulting firms, research labs and academic institutions, R offers tools for showcasing and communicating data-backed results.  

Analytical Skills

Analytical tools are crucial for pulling out relevant insights from available data and providing valuable frameworks for performing extensive data processing. The most popular analytical tools you should become proficient in include:

SQL

SQL is used to add, delete, extract, and transact information in a relational database. It comes in handy for modifying database structures and carrying out analytical functions. 

Apache Spark

Spark supports a wide range of functions in data analytics, such as data loading, SQL queries and computations for machine learning and streaming. Data scientists use Spark for analytics tasks, including data intake and distributed computing. 

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Machine Learning Skills

Machine learning skills are necessary when working with big data. Building skills around regression modeling, classification, decision tree, anomaly modeling, recommendation systems and time series prediction models can come in handy when working for clients with large data sets. Once you have mastered machine learning algorithms, you will then need to learn cloud platforms like Google Cloud Platform, Azure, and AWS to deploy the models. 

Data Visualization Skills

Data visualization is an essential skill for understanding data. Your analysis is futile if you can’t showcase your findings to key decision-makers in an understandable manner. You can do this if you’re proficient in data visualization tools, be it open-source tools such as Matplotlib, Ggplot, and D3, or the commercial ones such as Microsoft’s Power BI and Tableau. 

Data Wrangling Skills

Another important data science skill is the ability to process and use data for analytics. More often than not, the data you analyze is confusing and challenging to deal with. It is therefore vital for you to know how to handle errors in a data set. Data wrangling is the process of dealing with imperfect data. Using data-wrangling, you can remove imperfect data. It can sort out various data imperfections, including missing values, string formatting and inconsistent date formatting. For example, using data wrangling, you can ensure all data formats are consistent by transforming two different date formats such as “2021/06/21” and “06-21-2021” into the same format before performing an analysis of the data.

Industry Knowledge

To build a successful career as an independent data scientist, you should familiarize yourself with the industry you intend to work in, their key functions and how they accumulate, evaluate and leverage data. This will help in interviewing companies, understanding their problems and identifying the most relevant data. Many data scientists establish a niche in a particular industry and market themselves as experts in that industry, such as finance or ecommerce.

Learn More: Why Almost Half of All Data Science Tasks Will Be Automated

Storytelling Skills

Freelance data scientists usually have to showcase their findings to their client’s non-technical teams, which primarily means sales and marketing. To ensure they are on the same page and understand how your findings can help their business, you should keep your presentations informative yet clear, both verbally and in writing.  To translate the quantitative results of your analysis, you will need to have storytelling skills to convey the results of your analysis in a language that both technical and non-technical decision-makers can understand.

Marketing Skills

Keeping in mind that your first contact with potential clients will most likely be online, you should pick up these marketing skills: 

Build a portfolio

Building a portfolio can boost your marketability in front of prospective clients.   Your portfolio should showcase the practical application of your skills, projects you have been part of, notable achievements, which domain of data science you’re most comfortable with and the industries you prefer. The more focused your presentation, the better your portfolio will be in marketing yourself to future clients. 

Try to find a niche for yourself. Identify what you’re good at and what you enjoy doing, and you will find a data science niche that works for you. Your niche can be anything from revenue modeling for SaaS companies, demographic analysis for eCommerce stores, building recommendation engines for content websites to data visualization in Tableau. That’s not saying you will work solely on projects within your niche, but it helps get you started. 

Learn More: Why We’re Seeing an Uptick in Data Science Architecting 

Build a Personal Website

You should set up a personal website where you can introduce yourself as a freelance data scientist, showcase your portfolio, underline your expertise and sign up new clients.  For accomplishments, you may want to consider offering to do work for companies in exchange for testimonials that can be included in your portfolio. You can also add projects that you built yourself using public datasets. Add visualizations, slides, or other documents to demonstrate your competency for your chosen projects. 

Develop a Strong Online Presence

Having a strong online presence can help in establishing you as an expert in a particular domain. It would be best if you also had a comprehensive profile on GitHub and Medium with particulars of your previous projects, along with links to blogs or articles that can boost your image as an expert in data science and machine learning. If you are writing to attract specific clients, ensure that you explain how the concept you are writing about will solve their problems. Writing about your chosen field will strengthen your image as an expert in the eyes of prospective clients.

Be sure to list your data science qualifications on your LinkedIn and Twitter profiles so companies will be able to find you there. Also, respond more frequently to questions on platforms such as Quora and Stack Overflow.

Where to Begin Your Job Search

Once you have the technical and marketing skills to succeed as a freelance data scientist, you can look for clients on Upwork, Fiverr, Toptal, Data Science Stack Exchange and Kaggle. Many freelancers find leads through the LinkedIn job board and company pages on LinkedIn that list a need for data scientists. If you are interested in connecting with startups, check out the job listings on Angellist. 

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