How to Become an Data Scientist Without a Degree

Written by Coursera Staff • Updated on

Learn about how to become a data scientist without a degree, including ways to develop the necessary skills and other important steps to take.

[Featured Image] A data scientist wearing glasses and a striped shirt working in front of several computer monitors.

Data science is growing as companies look for ways to make the most out of the data they collect. The value of data science spreads far beyond tech companies alone. In fact, virtually all industries can benefit from implementing data science strategies to make data-driven decisions. If a company has access to data, chances are they can benefit from data science. Some of the specific industries where you can find data science applications in action include retail, financial services, health care, manufacturing, energy, and many more. In this article, you will learn what you can do to start a data science career, even without a degree.

What does a data scientist do?

As a data scientist, you will help the business or organization you work for analyze and better understand their data to develop valuable insights, ultimately helping to fuel positive outcomes and drive important business decisions. Working as a data scientist requires a diverse skill set with technical abilities in several key areas. However, strong business acumen is also important to this position since you will be using your findings from the data to make recommendations. Although similar to data analysts, data scientists separate themselves by their ability to build the models that collect and analyze data.

Duties and responsibilities

As a data scientist, you will have a wide range of responsibilities that can vary based on the industry you work in and the stage of the project you’re working on. Here’s a look at some common job duties you can anticipate in this role:

  • Collecting data from several sources and processing it into a usable format that you can then use for analysis

  • Creating new strategies and methods for data collection, often with tools to help automate this process for you

  • Using techniques to analyze the data and extract insights, such as artificial intelligence, machine learning, and different algorithms

  • Developing visualizations to effectively communicate and present insights, as well as make recommendations based on the data 

Salary and job outlook

Not only do data scientists earn a high income, but the overall outlook for this position is also strong. The median yearly salary for a data scientist is $103,500, with huge growth in the number of opportunities available as the position is projected to grow 35 percent between 2022 and 2032 [1]. 

Factors that can impact your earning potential as a data scientist include the industry you work in and the location of your job. According to data from the US Bureau of Labor Statistics, some of the leading industries for data scientists from a standpoint of salary and opportunities available include scientific research, computer systems design, business management, and insurance. Each of these fields offers an above-average income for data scientists and leads in employment numbers as well. States where data scientists tend to earn especially high incomes include California, Washington, and Virginia [2].

How to become a data scientist without a degree

While some employers will prefer a data scientist with a degree in an area such as computer science, math, IT, or a related field, this isn’t always a requirement. If you take the proper steps to equip yourself with the skills and experience, you can become a data scientist even without a degree. Here’s how you can do it.

Step 1: Develop the essential skills.

Data scientists possess several technical skills. You will need to learn to program in languages such as Python and R and understand advanced math and statistics, algorithms, data analysis, and how to build visualizations. Valuable workplace skills also include communication, critical thinking, and interpersonal skills.

Step 2: Self-study.

If you aren’t in a degree program, you must find other ways to learn data science skills and concepts. You have several options here: completing online courses, enrolling in a data science bootcamp, and self-studying through resources like books or online study guides.

Step 3: Get certified. 

Data scientist certifications effectively demonstrate to an employer that you have the necessary skills for the job. Certification options include the SAS Data Science Certification, Microsoft Certified: Azure Data Scientist Associate, or the Data Science Council of America (DASCA) Principal Data Scientist certification.

Step 4: Create a portfolio.

Another way to highlight your abilities is by developing a portfolio. This is a great way to show off projects, skills, and data science knowledge. Areas worth featuring in your portfolio include artificial intelligence and machine learning, data analysis, programming, and other relevant skills you have experience in.

Step 5: Get experience. 

You may not get to begin your career immediately with the title of data scientist, and that’s okay. You can gain experience in other ways to help make the transition. Experience in entry-level roles as a data analyst or experience working on projects, including projects from bootcamps, are all valuable and will help fill your portfolio.

Step 6: Network.

Leveraging your connections is a great way to land a data science job. Ideally, you can do this in person by attending events, conferences, and job fairs. However, if you can’t find any in your area, virtual events or online forums where you can connect with professionals work as well.

Step 7: Create your resume.

A quality data science resume should highlight your technical skills and certifications. When listing your previous work experience, do your best to describe how that position helped prepare you with relevant skills.

Entry-level positions in data science

When first starting out, you can find an entry-level role to build your skills and gain valuable work experience before moving up the ranks. One way of doing this is through a data science internship. While it’s common for data scientists to get their start as data analysts, you can find many relevant related positions and career paths in this field. Here are some below:

  • Junior data scientist

  • Marketing analyst

  • Data architect

  • Business intelligence analyst

  • Database administrator

  • Computer systems analyst

Getting started

On Coursera, you can find highly rated courses to take the next step in preparing for data scientist jobs. The Applied Data Science with Python Specialization from the University of Michigan can help you develop in-demand skills in areas such as text mining, machine learning, and Python toolkits.

Data Science Math Skills from Duke University will help you learn more about the important math concepts you will need as a data scientist, including probability, Bayes’ theorem, and graphing functions.

Article sources


US Bureau of Labor Statistics. “Data Scientists,” Accessed February 18, 2024.

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