Data Science vs. Machine Learning: What’s the Difference?

Written by Coursera Staff • Updated on

What is the difference between data science and machine learning? Which potential career path is right for you? Find out more here.

[Feature image] Person examining data on two separate computers

Data science and machine learning are two concepts that fall within the field of technology that use data to further how we create and innovate products, services, infrastructural systems, and more. Both correspond with career paths that are in-demand and high-earning.

The two relate to each other in a similar way that squares are rectangles, but rectangles are not squares. Data science is the all-encompassing rectangle, while machine learning is a square that is its own entity. They are both often used by data scientists in their work and are rapidly being adopted by nearly every industry.

Pursuing a career in either field can deliver high returns. According to US News, data scientists ranked as third-best among technology jobs, while a machine learning engineer was named the best job in 2019 [1, 2]. If you decide to learn programming and statistical skills, your knowledge will be useful in both careers.

In this article, you'll learn more about the differences (and similarities) between data science and machine learning and the skills and careers that define each field.

Build data science and machine learning skills today

Already interested in building your data science or machine learning skills? Consider enrolling in one of these specializations or Professional Certificates on Coursera:

To prepare for a career as a data scientist, consider enrolling in IBM's Data Science Professional Certificate, where you'll master the most up-to-date practical skills and knowledge that data scientists use in their daily roles, like Python and SQL.

To master fundamental AI concepts and develop practical machine learning skills, consider enrolling in Stanford and DeepLearning.AI's Machine Learning Specialization, where you'll learn to build and train machine learning models and neural networks.


Data science vs. machine learning: What’s the difference?

Data science studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions. Machine learning is a branch of artificial intelligence.

[Featured image] Venn diagram comparing Data Science vs Machine Learning

In recent years, machine learning and artificial intelligence (AI) have dominated parts of data science, playing a critical role in data analytics and business intelligence. Machine learning automates the process of data analysis and goes further to make predictions based on collecting and analyzing large amounts of data on certain populations. Models and algorithms are built to make this happen.

What is data science?

Data science is a field that studies data and how to extract meaning from it. It uses a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured data. This knowledge is applied to business, government, and other industries to drive profits, innovate products and services, build better infrastructure and public systems, and more.

Learn more about data science in this lecture from IBM's What is Data Science? course:

Read more: What is Data Science?

Skills needed

Gaining programming and data analytics skills is essential for building a career in data science, such as becoming a data scientist.

I liked that the [IBM Data Science Professional Certificate] had introductory courses covering a wide range of topics with practical assignments, engaging and clear video lectures, and easy-to-understand explanations ... this program strengthened my portfolio and helped me in my career.

Mo R.

Careers in data science

Besides the obvious career as a data scientist, there are plenty of other data science jobs to choose from.

  • Data scientist: Uses data to understand and explain the phenomena around them, to help organizations make better decisions.

  • Data analyst: Gathers, cleans, and studies data sets to help solve business problems.

  • Data engineer: Build systems that collect, manage, and transform raw data into information for business analysts and data scientists.

  • Data architect: Reviews and analyzes an organization’s data infrastructure to plan databases and implement solutions to store and manage data.

Read more: Your Guide to Data Science Careers (+ How to Get Started)

What is machine learning?

Machine learning is a branch of artificial intelligence that uses algorithms to extract data and then predict future trends. Software is programmed with models that allow engineers to conduct statistical analysis to understand patterns in the data. 

As an example, we all know that social media platforms like Facebook, Twitter, Instagram, YouTube, and TikTok gather users' information. Based on previous behavior, they predict interests and needs and recommend products, services, or articles that are relevant to what you've searched for before.

As a set of tools and concepts, machine learning is applied in data science, but also appears in fields beyond it. Data scientists often incorporate machine learning in their work where appropriate to help gather more information faster or to assist with trend analysis.

Read more: How Much Does a Machine Learning Engineer Make?

Skills needed

To become a successful machine learning engineer, you’ll need to be well-versed in the following:

Read more: Machine Learning Skills: Your Guide to Getting Started

Careers in machine learning

If you decide to pursue a career in machine learning and artificial intelligence, you have several options.

  • Machine learning engineer: Researches, builds, and designs the AI responsible for machine learning, and maintaining or improving AI systems

  • AI engineer: Build AI development and production infrastructure, and then implement it

Dive into machine learning

Learn how self-driving cars, speech recognition, and Google searches work with this deep dive into Machine Learning at Stanford University. Machine learning and AI are so pervasive in our lives that we barely notice we are using them (or that they are tracking our data!). You’ll learn about some of Silicon Valley’s best practices in innovation and solving problems.


Build your data science and machine learning skills today

Whether you decide to pursue data science or machine learning, you’ll need technical skills in programming and statistics to land a job.

In IBM’s Data Science professional certificate, you'll develop in-demand data science skills like importing and cleaning data sets, using data science libraries, and programming in Python and SQL. Start today and get job-ready in as little as five months. Stanford and DeepLearning.AI's Machine Learning Specialization offers a broad introduction to modern machine learning, including supervised learning, unsupervised learning, and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation.

Article sources


US News. "What is a Data Scientist?," Accessed March 26, 2024.

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