Bayesian Statistics Courses and Certifications

Foundations of Probability in R

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Fundamentals of Bayesian Data Analysis in R

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Bayesian Regression Modeling with rstanarm

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Applied Statistics in Python

Explore Python-based statistical analysis to gain essential decision-making skills such as A/B testing and Bayesian models.

• DataCamp
• 16 hours
• Self-Paced
• Free Trial Available

AI IN CLINICAL PHARMACY

Explore AI tools in clinical pharmacy, mastering CDSS evaluation, pharmacogenomics, Bayesian dosing, and antimicrobial stewardship while developing critical override skills to ensure patient-safe, AI-integrated decisions.

• OpenLearning
• Self-Paced
• Paid Course

Eindhoven University of Technology

Improving your statistical inferences

802 ratings at Coursera

Enhance statistical inference skills: interpret p-values, design experiments, control false positives, determine sample sizes, and apply Open Science principles for robust empirical research.

• Coursera
• 1 day 3 hours 36 minutes
• Self-Paced
• Paid Course

Clinical Trials Analysis, Monitoring, and Presentation

95 ratings at Coursera

Learn advanced skills for clinical trial management, including sample size calculation, statistical monitoring, result reporting, and the analyst's role throughout the process.

• Coursera
• 4 hours 42 minutes
• Self-Paced
• Paid Course

Introduction to Uncertainty Quantification

10 ratings at Coursera

Master the fundamentals of Uncertainty Quantification through probability theory, uncertainty propagation, surrogate modeling, reliability analysis, and Bayesian methods for mathematical and engineering systems.

• Coursera
• 1 day 5 hours 57 minutes
• Self-Paced
• Paid Course

Career Certificate

Data Analysis with R

Comprehensive R-based data analysis program covering probability, inference, regression, and Bayesian statistics. Develop practical skills for data visualization, wrangling, and reproducible reporting.

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

Meta

Statistics Foundations

405 ratings at Coursera

Comprehensive exploration of statistical foundations for data analytics, covering descriptive and inferential statistics, hypothesis testing, and data modeling using Tableau.

• Coursera
• 21 hours 17 minutes
• Self-Paced
• Paid Course

Bayesian Machine Learning in Python: A/B Testing

7963 ratings at Udemy

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

• Udemy
• 10 hours 27 minutes
• Self-Paced
• Paid Course

Recommender Systems and Deep Learning in Python

6243 ratings at Udemy

The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques

• Udemy
• 12 hours 49 minutes
• Self-Paced
• Paid Course

Career Certificate

Modern Statistics for Data-Driven Decision-Making

6 ratings at Coursera

Master statistical methods for data-driven decisions across business, industrial, and social environments with probability, regression, Bayesian concepts, and classification techniques.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 4 courses

Time, Change, and Decisions for Marketing

Revolutionize marketing strategies with advanced analytics, optimizing decision-making through Bayesian networks, time series forecasting, and causal modeling for data-driven, adaptive campaigns.

• Coursera
• 22 hours 53 minutes
• Self-Paced
• Paid Course

Statistics for Applications

1 rating

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications.

• MIT OpenCourseWare
• 28 hours
• Self-Paced
• Free Video