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Duke University · on Coursera

Data Science Math Skills

4.5(20,000) on Coursera·580K enrolled
Beginner 20 hours English University Certificate
SkillsMathematicsProbability basicsSet theoryFunctionsData-science foundations

Is this course right for you?

Our take
A gentle way to shore up the specific maths that data science assumes before you dive in. It is free to audit and short.

Good for: beginners who want to shore up the basic maths before data science.

Skip if: you already have a solid maths background, or you want rigorous depth.

Duke keeps it to what you actually need — set theory, counting, probability rules, Bayes' theorem and random variables — without requiring calculus or linear algebra. It fills the gap between high-school maths and a statistics-for-data-science course, which is why over 580,000 learners have used it as a first step.

It is a refresher, not a full maths programme, so if you already have a solid maths background or want rigorous depth, skip ahead to a statistics or linear-algebra course. The certificate needs a paid Coursera subscription; the free audit covers the learning. The material is foundational and still relevant.

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About this course

Data Science Math Skills is Duke's accessible introduction to the mathematical concepts data science builds on — set theory, real line and interval notation, counting and combinatorics, probability rules, Bayes' theorem, and random variables. It's designed for learners who want to start a data science learning path but feel uncertain about their mathematical foundation.

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Instructor

DE
Daniel Egger / Paul Bendich
Coursera instructor
580K+ learners3 courses4.5 instructor rating

Taught by Daniel Egger and Paul Bendich, Duke University mathematics faculty, designed specifically for learners approaching data science from non-mathematical backgrounds.

Frequently asked questions

Very little, which is the whole reason the course exists. It assumes you have not touched algebra or pre-calculus in years and rebuilds from the ground up — the real number line, set notation, basic functions — introducing each new symbol one at a time rather than assuming you remember it. If you can handle everyday arithmetic and are willing to slow down for unfamiliar notation, you have enough to begin.

Only at an introductory level, and it is honest about that. You get a gentle first look at slopes and tangent lines, exponents and logarithms, and probability up to and including Bayes' theorem — the ideas that underpin later machine-learning maths. What you do not get is a full calculus or statistics course, so treat it as a confidence-building primer that makes those deeper courses approachable, not a replacement for them.

Completely normal, and worth expecting. Most learners glide through the early weeks and then hit the probability material, especially Bayes' theorem, as a distinct step up. The advice from people who finished is to slow right down there, rework the examples by hand, and rewatch rather than push ahead — that final stretch is really the part that tests whether the earlier ideas have landed.

No, and it never sets out to. Its job is narrower and genuinely useful: removing the maths anxiety that stops beginners from starting real data science or machine-learning courses in the first place. Think of it as the warm-up that lets the harder, career-relevant courses actually make sense — the real skills come from what you tackle after it, not from this alone.

You can audit all the video lessons and readings on Coursera at no cost, which is most of what the course offers. The graded quizzes and the shareable certificate sit behind a paid subscription. If cost is a barrier, Coursera offers financial aid — you apply, explain your circumstances, and wait a couple of weeks for a decision that, if approved, unlocks the graded track too.
Paid
Free to audit, paid certificate
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