Probability Courses and Certifications

Career Certificate

Statistical Methods for Computer Science

5 ratings at Coursera

Build advanced probability and statistical modeling skills: joint distributions, expectation, simulation, exponential random graph models, probabilistic graphical models, and hypothesis testing with R.

• Coursera
• 12 weeks, 5 hours a week
• Self-Paced
• Paid Course
• 3 courses

New Syllabus PD: Conditional Probability

This course introduces Conditional Probability, which is section MA-S2 from the New Stage 6 Advanced Mathematics syllabus.

• OpenLearning
• Self-Paced
• Paid Course

What are the Chances? Probability and Uncertainty in Statistics

18 ratings at Coursera

Learn to quantify uncertainty in statistical findings: apply probability rules and distributions, run hypothesis tests with test statistics and confidence intervals, and judge significance of regression coefficients and polls.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

IIT Delhi;  NPTEL

Introduction to Probability Theory and Stochastic Processes (Tamil)

தமிழில் நிகழ்தகவு கோட்பாடு மற்றும் சீரற்ற செயல்முறைகளின் அடிப்படைகளை அறிமுகப்படுத்தி, மார்கோவ் சங்கிலிகள் மற்றும் வரிசை மாதிரிகளை விளக்கும் பாடநெறி.

• Swayam
• 12 weeks
• Available until Oct 9, 2026
• Free Online Course

Advanced Probability and Statistical Methods

Work through joint distributions, expectation and covariance, the Markov and Chebyshev inequalities, the central limit theorem, hypothesis testing and Markov chains, implementing methods in R.

• Coursera
• 6 weeks, 8 hours a week
• Self-Paced
• Paid Course

Honors Algebra 2: Series, Trigonometry, and Probability

Analyze arithmetic and geometric sequences and series, master sine, cosine and tangent on the unit circle, and compute probabilities using two-way tables and Venn diagrams.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course

Basic Statistics

13 ratings

Learn to compute and interpret means, standard deviations, Pearson's r and OLS regression, then work through probability distributions, sampling distributions, confidence intervals and significance tests.

• Coursera
• 9 weeks, 2 hours a week
• Self-Paced
• Paid Course

Probability and Statistics for Business and Data Science

8003 ratings at Udemy

Learn how to apply probability and statistics to real data science and business applications!

• Udemy
• 5 hours 14 minutes
• Self-Paced
• Paid Course

Workshop in Probability and Statistics

4863 ratings at Udemy

This workshop will teach you the fundamentals of statistics in order to give you a leg up at work or in school.

• Udemy
• 22 hours 13 minutes
• Self-Paced
• Paid Course

Mathematical Biostatistics Boot Camp 1

17 ratings

Learn the probability behind biostatistics: mass and density functions, expectations, Bayes' rule, likelihood, asymptotics, confidence intervals, bootstrapping, and binomial proportions.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Discrete Math for Computer Science - Counting & Probability

Count arrangements with permutations, combinations, binomial coefficients and inclusion-exclusion, then compute probabilities using sample spaces, Bayes' theorem, random variables, expectation and variance.

• Coursera
• 8 weeks, 2 hours a week
• Self-Paced
• Paid Course

BiteSize Stats: Key Probability Distributions

Build probability models from random variables and PMFs: compute binomial, Poisson, and Normal probabilities, standardize with Z-scores, and apply the Central Limit Theorem to sample means.

• Coursera
• 5 weeks, 17 hours a week
• Self-Paced
• Paid Course

TrevTutor

Discrete Mathematics - Counting, Recurrence Relations, Generating Functions, Probability, Graph Theory

2 ratings

Master essential discrete mathematics concepts including combinatorics, probability, recurrence relations, generating functions, and graph theory through comprehensive examples.

• YouTube
• 10 hours 49 minutes
• Self-Paced
• Free Video

Bayesian Statistics: From Concept to Data Analysis

10 ratings

Learn Bayesian statistics from probability and Bayes' theorem to conjugate models for Bernoulli, Poisson, and normal data, plus Bayesian linear regression, using R or Excel.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Coursera

Using probability distributions for real world problems in R

37 ratings at Coursera

Apply Binomial, Poisson, Normal, Exponential and Chi-square distributions to solve practical problems in R, and visualize the resulting data distributions.

• Coursera
• 1 hour 45 minutes
• Self-Paced
• Paid Course