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

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

The two relate to each other much like squares and rectangles—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. Data scientists use both in their work, and nearly every industry is adopting both data science and machine learning.

Pursuing a career in either field can deliver high returns. If you decide to learn programming and statistical skills, your knowledge will be helpful in both careers.

Read on to learn the difference between data science and machine learning.

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

Data science is a field that studies data and how to extract meaning from it. In contrast, 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 specific populations. Data scientists build models and algorithms to make this happen.

What is data science?

Data science is a field that studies data and how to extract meaning from it, using a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured data. Businesses, governments, and other industries apply this knowledge to drive profits, innovate products and services, build better infrastructure and public systems, and more.

To gain a better understanding of data science, watch this video:

Skills needed

Gaining programming and data analytics skills is essential while building a career in data science, such as becoming a data scientist. Other helpful capabilities include the following:

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

• Familiarity 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, you can explore plenty of other data science jobs. Discover more about roles as a data scientist, data analyst, or business intelligence analyst.

Data scientist

Average annual base salary in Canada: $97,206 CAD [1]

Job outlook: Good to moderate

As a data scientist, you will use data to answer questions and solve problems, helping organizations make better decisions or advancing research. In this role, you will track and collect raw data and create analyses, eventually presenting your findings to senior leadership. 

Data analyst

Average annual base salary in Canada: $63,844 CAD [2]

Job outlook: Good to moderate

As a data analyst, you will gather, clean, and study data sets to help solve business problems. This role is similar to that of a data scientist, although you may work in a team with other data analysts under the direction of a more senior analyst. 

Business intelligence analyst

Average annual base salary in Canada: $75,584 CAD [3]

Job outlook (projected): Good to very good

As a business intelligence analyst, you will gather, clean, and analyze business intelligence data, interpret it, and share findings with business teams to empower data-driven decision-making. In this role, you will also prepare briefing notes and work with other departments to disseminate information and analytics.

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 machine learning engineers to conduct statistical analysis to understand patterns in the data. 

For example, you already know that social media platforms like Facebook, X, Instagram, YouTube, and TikTok gather users' information. Based on previous behavior, it predicts interests and needs and recommends products, services, or articles relevant to what you've searched before.

As a set of tools and concepts, machine learning is applied in data science and 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.

Skills needed

A solid foundation in computer science is just the beginning. 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 can choose from several options, such as machine learning engineer, AI engineer, cloud engineer, or computational linguist.

Machine learning engineer

Average annual base salary in Canada: $106,278 CAD [4]

Job outlook: Good to moderate

As a machine learning engineer, you will research, build, and design the AI responsible for machine learning and maintaining or improving AI systems. You will work with a data science team and use machine learning to solve problems for real-world applications.

AI engineer

Average annual base salary in Canada: $100,565 CAD [5]

Job outlook: Good to moderate

AI engineers and machine learning engineers often have similar job duties. Still, as machine learning is a subfield of artificial intelligence, you may work with a broader range of artificial intelligence systems or algorithms.

Cloud engineer

Average annual base salary in Canada: $105,570 CAD [6]

Job outlook: Good to moderate

As a cloud engineer, you will help companies migrate their systems into cloud infrastructure. You may also work to create new cloud systems. In this role, you may also maintain cloud systems after deployment. 

Dive into machine learning

Learn how self-driving cars, speech recognition, and Google searches work with this deep dive into Supervised Machine Learning: Regression and Classification Course from 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.

Get started in data science

Both machine learning and data science are fields that can help you find a job at the cutting edge of technology or research. Whether you pursue data science or machine learning, you’ll need technical skills in programming and statistics to land a job. IBM’s Data Science Professional Certificate is designed to help you land a job as a data scientist or a related career. No degree or experience is required. Start today and you could earn your certificate in 11 months or less.