Probability & Statistics for Machine Learning & Data Science
Probability & Statistics for Machine Learning & Data Science is an intermediate-level online course from Coursera in Machine Learning. It is structured in 4 modules with 1 stated learning outcome, over 33h 28m. Learners rate it 4.6/5 from 702 ratings on Coursera.
Part of our Machine Learning courses collection, where we compare it against 20 other courses. See all Machine Learning courses on Coursera.
Course highlights
| Provider | Coursera |
| Duration | 33h 28m |
| Level | Intermediate |
| Mode | Self-paced |
| Language | English |
| Certificate | Yes — shareable certificate |
| Rating | 4.6★ (702 reviews) |
| Price | Check price |
What you’ll learn
Level and time commitment
- High school math (functions, basic algebra) and some programming (loops, functions, if/else, lists/dictionaries, libraries, debugging) is recommended
Skills you’ll gain
What this course covers
4 modulesThe 4-part outline Coursera publishes for this course, across 33h 28m.
Module 1 · Week 1 - Introduction to Probability and Probability Distributions
Module 2 · Week 2 - Describing probability distributions and probability distributions with multiple variables
Module 3 · Week 3 - Sampling and Point estimation
Module 4 · Week 4 - Confidence Intervals and Hypothesis testing
What learners rate it
Rated 4.6/5 by 702 learners on Coursera (checked 8 Nov 2026).
This rating is collected by Coursera from its own enrolled learners. We reproduce it as reported and do not accept paid or incentivised reviews. See it on Coursera →
Compiled by the CoursesGlobal editorial desk. Fees, ratings, duration and certificate details for the 1 course shown are read directly from Coursera course pages — we never estimate a price or a rating. Last verified 8 November 2026.
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Compare with alternatives
Same topic, different trade-offs — here's who runs each one and what it's best at.
| Course | Provider | Rating | Price | Duration | Why pick this one | |
|---|---|---|---|---|---|---|
| Probability & Statistics for Machine Learning & Data Science This |
 Coursera
|
4.6★ | Check price | 33h 28m | Most popular — 0 learners | |
| Machine Learning |
Coursera
|
4.9★ | Check price | 2 months | Highest rated (4.9★ from 39.5K reviews) | View ↗ |
| Machine Learning with Python |
Coursera
|
4.7★ | Check price | 20h 26m | Quickest to finish (20h 26m) | View ↗ |
| Machine Learning: Classification |
Coursera
|
4.7★ | Check price | 21h 25m | All Levels · 21h 25m · certificate | View ↗ |
Where Machine Learning leads
Roles we map to Machine Learning on CoursesGlobal. This is our own mapping of subject to job, not a placement claim by Coursera.
Browse every course we track for one of these — courses for data scientists.
Common questions
How much does Probability & Statistics for Machine Learning & Data Science cost?
Coursera does not publish a price we can read on the course page, so we do not show one. Check the fee on the provider site — we would rather say nothing than quote a number we did not collect.
Does Probability & Statistics for Machine Learning & Data Science come with a certificate?
Yes — Coursera lists a certificate on completion. It is a course certificate, not a formal qualification or university credit.
How long does Probability & Statistics for Machine Learning & Data Science take?
Coursera lists it at 33h 28m. It is self-paced, so that is the volume of material rather than a deadline — how long it actually takes depends on the hours you put in each week.
Is Probability & Statistics for Machine Learning & Data Science suitable for beginners?
Coursera lists it at Intermediate level, so it assumes some grounding in the subject already. If you are starting from zero, a beginner-level course in the same topic is the better entry point.
What does Probability & Statistics for Machine Learning & Data Science cover?
It is organised into 4 modules, starting with Week 1 - Introduction to Probability and Probability Distributions and going on to Week 2 - Describing probability distributions and probability distributions with multiple variables, Week 3 - Sampling and Point estimation, Week 4 - Confidence Intervals and Hypothesis testing. The full outline is on this page.