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

Data science and machine learning are two concepts within the technology field. They rely on data to advance the creation and innovation of products, services, infrastructural systems, and more. Both correspond with in-demand, high-earning career paths.

The two relate to each other similarly, in 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 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 Glassdoor’s Best Jobs in the UK survey, data scientists rank fifth, while machine learning engineers are in Reed’s top five paying jobs in AI [1, 2]. If you decide to learn programming and statistical skills, your knowledge will be helpful in both careers.

Read on to explore the difference between data science and machine learning and gain a better understanding of each.

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

Data science is the field focused on studying data and ways to extract meaning from it. In contrast, machine learning is devoted to understanding and building methods that use 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 analysing large amounts of data on specific populations. Models and algorithms make this happen.

What is data science?

Data science is a field that studies data and how to extract meaning from it. It uses methods, algorithms, systems, and tools to extract insights from structured and unstructured data. This knowledge applies 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:

Skills needed

Gaining programming and data analytics skills is essential for a career in data science, such as becoming a data scientist. Other capabilities you’ll need 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 analysing data for business needs

• Understanding of maths, statistics, and probability

• Data visualisation 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 programme strengthened my portfolio and helped me in my career.— Mo R.

Careers in data science

With the proper foundation of knowledge and a robust skill set, you can build a successful career in this field. In addition to the obvious career as a data scientist, you can also choose from various data science jobs. Take a quick look at the options below: 

• Data scientist: A person who uses data to understand and explain the phenomena around them to help organisations 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 analyses an organisation’s data infrastructure to plan databases and implement solutions to store and manage data.

• Business intelligence analyst: Gathers, cleans, and analyses 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 predict future trends. Engineers rely on the resulting models to conduct statistical analysis to understand patterns in the data. 

For example, we all know that social media platforms like Facebook, X, Instagram, YouTube, and TikTok gather users' information. Based on previous behaviour, they predict interests and needs and recommend products, services, or articles relevant to your search.

Machine learning is a set of tools and concepts that 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 analysing trends.

Skills needed

Solid computer science expertise, including data structures, algorithms, and architecture, is essential to build a successful career as a machine learning engineer. You’ll also need to be well-versed in the following:

• 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 modelling and analysis

Careers in machine learning

After developing your skill set, you can pursue multiple careers in this specialised field. A few of the options to choose from include: 

• 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 dealing with human language.

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 everyday life that many people barely notice they’re using them (or 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.

Data science and machine learning are distinct yet intertwined disciplines. While data science focuses on extracting meaning from data, machine learning leverages algorithms to predict future trends and enhance performance.

Whether you pursue data science or machine learning, you’ll need technical skills in programming and statistics to land a job. The skills and knowledge you’ll cultivate in a programme like IBM’s Data Science Professional Certificate on Coursera can help you begin or advance your career in data science or a related field. If you prefer to specialise in a specific area, consider the Machine Learning for All course offered by the University of London.