In your first semester you will take Mathematics for Data Science, covering the mathematical pillars essential for the field: linear algebra, probability theory, statistics and optimisation. Note that the course is not an introduction to these disciplines, but a review and expansion of their core concepts for application in modern machine learning and deep learning.

For the full picture of what the course covers and assumes, see the syllabus - a good way to gauge how much of the preparation below you’ll want to focus on.

  • Syllabus

    Mathematics for Data Science

    The topics the course reviews and expands on, and what it assumes you arrive with.

    Open syllabus (PDF)

Is it necessary to learn maths for data science?

Modern libraries let you build models with very little maths - but a strong theoretical foundation is what lets you develop intuition, quickly grasp new methods, and understand fundamental research. Think of it as three layers:

Libraries

Libraries, packages and frameworks that simplify the coding process.

Source code

Transforming mathematical equations into code and abstracting the theory.

Theories & equations

Equations that model and approximate the problems of interest.

A strong theoretical foundation in mathematics helps you cultivate the intuition behind machine learning and deep learning models, and the ability to quickly grasp new methods, approaches and fundamental research.

Two ways to approach maths

Everyone learns differently. Both of these approaches work - pick whichever suits you, or mix them.

Theories then practice

Bottom-up: build a strong mathematical foundation and intuition before attempting complex concepts and projects.

Practice and theories

Top-down: tackle a project or concept head-on, reverse-engineer the concepts and code, and gradually build your mathematical intuition as you go.

Your Brilliant classroom

You have access to a premium Brilliant classroom with structured content covering the fundamentals of core data-science mathematics. It is a self-learning platform with text- and image-based challenges to help you catch up or review maths and science quickly. If you are not yet familiar with the disciplines above, work through the courses below in the recommended order.

A visual companion: 3Blue1Brown

During or after your maths review, we highly recommend watching the “Essence of” series on 3Blue1Brown’s YouTube channel. It gets to the heart of these two disciplines in a visual and intuitive way, in one binge-watchable series of lessons.

  • Essence of calculus

    The ideas behind derivatives and integrals, built up visually.

    Watch series

  • Essence of linear algebra

    Vectors, matrices and transformations, seen geometrically.

    Watch series

If you prefer other formats, want more practice, or just want to explore further, the optional Further resources page lists a few extras - but everything you need to arrive prepared is already on this page.