9 skills you need to become a freelance data scientist in 2021

Whether you want to supplement your current income, need a job to fill a gap in your CV, or are looking to become your own boss, becoming a freelance data scientist can be the career path for you. The demand for data scientists across all industries has grown by 417% over the past year, making it easier to find good freelance opportunities for 2021 and beyond.

If you are a skilled data scientist, you can take advantage of these opportunities right away. However, if you don’t, you will need to learn or hone some technical and non-technical skills that are described as follows:

Math skills

You should have a thorough understanding of some of the key concepts in mathematics:

Statistics

Statistics provide the methods needed to obtain valuable information from data. The more statistics you know, the more you can analyze and quantify the uncertainty in a data set. Familiarize yourself with terms such as mean, median, mode, standard deviation, and distributions. You should also know when to apply multiple sampling techniques and what to do to avoid bias in the experiments. You should also know how descriptive and inferential statistics are used to generate and display predictions.

Probability

A firm grasp of key probability concepts 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 customer data.

Linear algebra and calculus

To build a machine learning model, you need to know a bit of math. Knowledge of linear algebra concepts such as matrices and vectors is essential when working with algorithms. This will allow you to make tiny improvements to an algorithm to drastically affect the end result. Businesses with large volumes of data typically need this skill. In organizations where data defines products, small adjustments in predictive performance and optimization of algorithms can lead to better productivity.

Multivariate calculus

Understanding of concepts such as mean value theorems, product and chain rules, gradient derivatives, Taylor series, and gamma functions is essential for running logistic regression algorithms.

Learn more: 6 pro tips to build your dream data science team

Statistical programming skills

Data scientists are supposed to source, cleanse, manage 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 for which you want to freelance.

However, when choosing, take into account that more than 50% of data scientists are proficient in at least one or both:

Python

Knowledge of Python is a key skill for any data scientist. It is a versatile object-oriented language used by programmers at every stage of data science processes, such as web service development, machine learning model building, data mining, and classifications. . In addition to the basics, you should be familiar with Python libraries such as Pandas, TensorFlow, and Matplotlib.

R

Primarily used for statistical analysis, R is another key programming language that data scientists need to know. Used in all major technology companies, financial institutions, analyst and consulting firms, research labs and academic institutions, R offers tools for presenting and communicating data-driven results.

Analytical skills

Analytical tools are essential for extracting relevant information from available data and providing valuable frameworks for performing in-depth data processing. The most popular analytical tools you should be familiar with include:

SQL

SQL is used to add, remove, extract, and process information in a relational database. It is useful for modifying database structures and performing analysis functions.

Apache Spark

Spark supports a wide range of data analysis functions, such as data loading, SQL queries, and calculations for machine learning and streaming. Data scientists use Spark for analytical tasks, including data collection and distributed computing.

Learn more: Data Science and Machine Learning: Top 10 Differences

Machine learning skills

Machine learning skills are needed when working with big data. Building skills around regression modeling, classification, decision tree, anomaly modeling, recommender systems, and time series prediction models can come in handy when working for clients with large data sets. Once you have mastered the 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 cannot present your findings to key decision makers in an understandable way. You can do this if you are familiar with data visualization tools, whether it’s open source tools like Matplotlib, Ggplot, and D3, or commercial ones like Microsoft’s Power BI and Tableau.

Data management skills

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

Industry knowledge

To build a successful career as a freelance data scientist, you need to become familiar with the industry you intend to work in, their key functions, and how they accumulate, evaluate, and mine data. This will help survey businesses, understand their issues, and identify the most relevant data. Many data scientists are establishing a niche in a particular industry and presenting themselves as experts in that industry, such as finance or e-commerce.

Learn more: Why almost half of all data science tasks will be automated

Storytelling skills

Independent data scientists typically have to present their findings to their clients’ non-technical teams, which primarily means sales and marketing. To make sure they’re on the same page and understand how your findings can help their business, you need to keep your presentations informative but 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 language that technical and non-technical decision makers can understand.

Marketing skills

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

Build a portfolio

Building a portfolio can increase your market value to potential clients. Your portfolio should showcase the practical application of your skills, the projects you have been involved in, notable accomplishments, the area of ​​data science you are most comfortable with, and the industries you prefer. The more targeted your presentation, the better your portfolio will be to market yourself to future clients.

Try to find a niche for yourself. Identify what you’re good at and what you love to do, and you’ll find a data science niche that works for you. Your niche can range from revenue modeling for SaaS businesses, to demographic analysis for e-commerce stores, to building recommendation engines for content websites and visualizing data in Tableau. This doesn’t mean you’ll only be working on projects in your niche, but it does help you get started.

Learn More: Why We’re Seeing an Increase in Data Science Architecture

Create a personal website

You need to build a personal website where you can introduce yourself as a freelance data scientist, showcase your portfolio, highlight your expertise, and sign up new clients. For accomplishments, you may want to consider offering to work for companies in exchange for testimonials that can be included in your portfolio. You can also add projects that you have created yourself using public datasets. Add visualizations, slides or other material to demonstrate your competence for the projects you have chosen.

Develop a strong online presence

Having a strong online presence can help establish yourself as an expert in a particular field. It would be best if you also have a full profile on GitHub and Medium with details of your previous projects, as well as links to blogs or articles that can bolster your image as an expert in data science and machine learning. . If you are writing to attract specific clients, be sure to explain how the concept you are writing about will solve their problems. Writing about your chosen field will reinforce your image as an expert in the eyes of potential clients.

Make sure to list your data science qualifications on your LinkedIn and Twitter profiles so businesses can find you there. Plus, answer questions more frequently on platforms like Quora and Stack Overflow.

Where to start your job search

Once you have the technical and marketing skills to be successful as a freelance data scientist, you can research clients on Upwork, Fiverr, Toptal, Data Science Stack Exchange, and Kaggle. Many freelancers find leads through the LinkedIn job site and company pages on LinkedIn that list a need for data scientists. If you want to get in touch with startups, check out the job postings on Angellist.

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Sean N. Ayres