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

Key takeaways

Data science involves studying and extracting meaning from data, whereas machine learning utilizes data to improve performance or inform predictions. 

• Data scientist and machine learning engineer have both been ranked among the top jobs in 2025 [1, 2].

• Data scientists typically develop strong skills in areas such as computer programming, structured and unstructured data analysis, and data visualization, while machine learning professionals build expertise in areas including computer science, systems design, and data modeling.

• You can work in either of these high-demand fields to further the creation and innovation of products, services, infrastructural systems, and more. 

Learn more about the differences (and similarities) between data science and machine learning and the skills and careers that define each field. Or, begin learning with the IBM Data Science Professional Certificate. In this twelve-course series, you can learn the tools, languages, and libraries used by professional data scientists, including Python and SQL. By the end, you’ll have had the opportunity to apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers.

What is data science vs. machine learning? 

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.

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.

Pursuing a career in either field can deliver high returns. According to US News, data scientists ranked as the eighth-best job in the US, while a machine learning engineer was named one of the top three AI jobs in 2025 [1, 2]. If you decide to learn programming and statistical skills, your knowledge will be useful in both careers.

Read more: 5 In-Demand Jobs Requiring AI Skills 

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 have the opportunity to 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 discover how to build and train machine learning models and neural networks.

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:

Data science skills

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

• Strong knowledge of programming languages Python, R, SAS, and more

• Familiarity with working with large amounts of structured and unstructured data

• Comfortable with processing and analyzing data for business needs

• Understanding of math, statistics, and probability

• Data visualization and data wrangling skills

• Knowledge of machine learning algorithms and models

• Good communication and teamwork skills

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

• Business intelligence analyst: Gathers, cleans, and analyzes sales and customer data, interprets it, and shares findings with business teams

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.

Machine learning skills 

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

• Expertise in computer science, including data structures, algorithms, and architecture

• Strong understanding of statistics and probability

• Knowledge of software engineering and systems design

• Programming knowledge, such as Python, R, and more

• Ability to conduct data modeling and analysis

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: Builds AI development and production infrastructure, and then implements it

• Cloud engineer: Builds and maintains cloud infrastructure

• Computational linguist: Develops and designs computers that deal with how human language works

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.

Explore trending topics in data science vs machine learning vs. AI with our free resources

Whether you’re considering a certificate or a degree, subscribe to our LinkedIn newsletter, Career Chat, to stay current on trending topics in emerging fields. You can also explore these free resources to build in-demand skills for data-driven positions:

• Earn a certificate: 6 machine learning certificates + how to choose the right one for you

• Build skills: R Programming: Data Manipulation with dplyr (Step-by-Step) 

• Learn from experts: 6 Questions with an IBM Data Scientist and AI Engineer 

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.