• For Individuals
  • For Businesses
  • For Universities
  • For Governments
Coursera
  • Online Degrees
  • Careers
  • Log In
  • Join for Free
    Coursera
    • Browse
    • Bayesian Statistics

    Bayesian Statistics Courses Online

    Understand Bayesian statistics for data analysis and decision making. Learn to apply Bayesian methods to real-world problems.

    Skip to search results

    Filter by

    Subject
    Required
     *

    Language
    Required
     *

    The language used throughout the course, in both instruction and assessments.

    Learning Product
    Required
     *

    Build job-relevant skills in under 2 hours with hands-on tutorials.
    Learn from top instructors with graded assignments, videos, and discussion forums.
    Learn a new tool or skill in an interactive, hands-on environment.
    Get in-depth knowledge of a subject by completing a series of courses and projects.
    Earn career credentials from industry leaders that demonstrate your expertise.
    Earn career credentials while taking courses that count towards your Master’s degree.
    Earn your Bachelor’s or Master’s degree online for a fraction of the cost of in-person learning.
    Complete graduate-level learning without committing to a full degree program.
    Earn a university-issued career credential in a flexible, interactive format.

    Level
    Required
     *

    Duration
    Required
     *

    Skills
    Required
     *

    Subtitles
    Required
     *

    Educator
    Required
     *

    Explore the Bayesian Statistics Course Catalog

    • Placeholder
      U

      University of California, Santa Cruz

      Bayesian Statistics

      Skills you'll gain: Time Series Analysis and Forecasting, Bayesian Statistics, R Programming, Forecasting, Statistical Inference, Statistical Modeling, Technical Communication, Data Analysis, Probability, Statistical Machine Learning, Statistical Methods, Statistical Analysis, Advanced Analytics, Microsoft Excel, Markov Model, Probability Distribution, Probability & Statistics, Unsupervised Learning, Regression Analysis, Predictive Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, Statistical Modeling, Statistical Methods, Statistical Inference, Statistical Analysis, Regression Analysis, Data Analysis, R Programming, Probability, Probability Distribution

      3.8
      Rating, 3.8 out of 5 stars
      ·
      796 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      Skills you'll gain: Bayesian Statistics, Statistical Inference, Data Analysis, Probability, Statistical Modeling, Statistical Analysis, Microsoft Excel, Probability Distribution, R Programming, Regression Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      Skills you'll gain: Bayesian Statistics, Statistical Modeling, Statistical Methods, Markov Model, Statistical Analysis, Regression Analysis, R Programming, Simulations, Statistical Inference, Probability, Probability Distribution, Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      487 reviews

      Intermediate · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      Probability & Statistics for Machine Learning & Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Data Science, Exploratory Data Analysis, Data Analysis, Statistical Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      542 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      S

      Stanford University

      Introduction to Statistics

      Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Probability Distribution, Correlation Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      4K reviews

      Beginner · Course · 1 - 3 Months

    • Placeholder
      Status: New
      New
      V

      Vanderbilt University

      ChatGPT + Excel: AI-Enhanced Data Analysis & Insight

      Skills you'll gain: Data Storytelling, ChatGPT, Excel Macros, Excel Formulas, Microsoft Excel, Data Visualization, Data Analysis, Presentations, Generative AI, Artificial Intelligence, Data Cleansing, Data Import/Export, Data Integration, Productivity Software, Data Transformation, Data Validation, Exploratory Data Analysis, Creative Thinking, Creativity, Natural Language Processing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      5.4K reviews

      Beginner · Specialization · 1 - 3 Months

    • Placeholder
      D

      Duke University

      Data Analysis with R

      Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Exploratory Data Analysis, Regression Analysis, Statistical Reporting, Probability Distribution, Statistical Methods, Statistical Software, R Programming, Bayesian Statistics, Statistical Analysis, Data Analysis, Statistical Modeling, Probability & Statistics, Probability, Statistics, Data Science, Descriptive Statistics, Predictive Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7.5K reviews

      Beginner · Specialization · 3 - 6 Months

    • Placeholder
      S

      Stanford University

      Probabilistic Graphical Models

      Skills you'll gain: Bayesian Network, Applied Machine Learning, Graph Theory, Probability Distribution, Statistical Modeling, Markov Model, Statistical Machine Learning, Decision Support Systems, Machine Learning, Network Model, Probability & Statistics, Network Analysis, Statistical Inference, Sampling (Statistics), Natural Language Processing, Statistical Methods, Algorithms, Machine Learning Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.5K reviews

      Advanced · Specialization · 3 - 6 Months

    • Placeholder
      U

      University of Michigan

      Statistics with Python

      Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Modeling, Statistical Inference, Statistical Methods, Bayesian Statistics, Data Visualization, Statistics, Matplotlib, Statistical Visualization, Statistical Software, Probability & Statistics, Statistical Analysis, Jupyter, Statistical Programming, Regression Analysis, Data Visualization Software, Predictive Modeling, Exploratory Data Analysis, Data Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.3K reviews

      Beginner · Specialization · 1 - 3 Months

    • Placeholder
      M

      Meta

      Statistics Foundations

      Skills you'll gain: Bayesian Statistics, Descriptive Statistics, Statistical Hypothesis Testing, Statistical Inference, Sampling (Statistics), Data Modeling, Statistics, Probability & Statistics, Statistical Analysis, Statistical Methods, Statistical Modeling, Marketing Analytics, Tableau Software, Data Analysis, Spreadsheet Software, Quantitative Research, Analytics, Descriptive Analytics, Time Series Analysis and Forecasting, Regression Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      316 reviews

      Beginner · Course · 1 - 3 Months

    • Placeholder
      U

      University of California, Santa Cruz

      Bayesian Statistics: Capstone Project

      Skills you'll gain: Bayesian Statistics, Technical Communication, R Programming, Statistical Analysis, Statistical Modeling, Data Analysis, Time Series Analysis and Forecasting, Markov Model, Statistical Methods, Predictive Modeling, Statistical Inference, Sampling (Statistics), Probability Distribution

      Advanced · Course · 1 - 4 Weeks

    Bayesian Statistics learners also search

    R Statistics
    Applied Statistics
    Beginner Statistics
    Statistics Projects
    Advanced Statistics
    Statistics
    Basic Statistics
    Statistics for Data Science
    1234…110

    In summary, here are 10 of our most popular bayesian statistics courses

    • Bayesian Statistics: University of California, Santa Cruz
    • Bayesian Statistics: Duke University
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
    • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
    • Introduction to Statistics: Stanford University
    • ChatGPT + Excel: AI-Enhanced Data Analysis & Insight: Vanderbilt University
    • Data Analysis with R: Duke University
    • Probabilistic Graphical Models: Stanford University
    • Statistics with Python: University of Michigan

    Skills you can learn in Probability And Statistics

    R Programming (19)
    Inference (16)
    Linear Regression (12)
    Statistical Analysis (12)
    Statistical Inference (11)
    Regression Analysis (10)
    Biostatistics (9)
    Bayesian (7)
    Logistic Regression (7)
    Probability Distribution (7)
    Medical Statistics (6)

    Frequently Asked Questions about Bayesian Statistics

    Bayesian Statistics is an approach to statistics based on the work of the 18th century statistician and philosopher Thomas Bayes, and it is characterized by a rigorous mathematical attempt to quantify uncertainty. The likelihood of uncertain events is unknowable, by definition, but Bayes’s Theorem provides equations for the statistical inference of their probability based on prior information about an event - which can be updated based on the results of new data.

    While its origins lie hundreds of years in the past, Bayesian statistical approaches have become increasingly important in recent decades. The calculations at the heart of Bayesian statistics require intensive numerical integrations to solve, which were often infeasible before low-cost computing power became more widely accessible. But today, statisticians can evaluate integrals by running hundreds of thousands of simulation iterations with Markov chain Monte Carlo methods on an ordinary laptop computer.

    This new accessibility of computational power to quantify uncertainty has enabled Bayesian statistics to showcase its strength: making predictions. This capability is critical to many data science applications, and especially to the training of machine learning algorithms to create predictive analytics that assist with real-world decision-making problems. As with other areas of data science, statisticians often rely on R programming and Python programming skills to solve Bayesian equations.‎

    Bayesian statistical approaches are essential to many data science and machine learning techniques, making an understanding of Bayes’ Theorem and related concepts essential to careers in these fields.

    If you wish to dive more deeply into the theoretical aspects of Bayesian statistics and the modeling of probability more generally, you can also pursue a career as a statistician. These experts may work in academia or the private sector, and usually have at least a master’s degree in mathematics or statistics. According to the Bureau of Labor Statistics, statisticians earn a median annual salary of $91,160.‎

    Absolutely. Coursera gives you opportunities to learn about Bayesian statistics and related concepts in data science and machine learning through courses and Specializations from top-ranked schools like Duke University, the University of California, Santa Cruz, and the National Research University Higher School of Economics in Russia. You can also learn from industry leaders like Google Cloud, or through Coursera’s own exclusive Guided Projects, which let you build skills by completing step-by-step tutorials taught by expert instructors.

    Regardless of your needs, the combination of high-equality education, a flexible schedule, and low tuition costs leaves no uncertainty about the value of learning about Bayesian statistics on Coursera.‎

    A background in statistics and certain areas of math, like algebra, can be extremely helpful when learning Bayesian statistics. This includes knowledge of and experience with statistical methods and statistical software. Any type of experience working with data, especially on a large scale, can also help. Classes, degrees, or work experience in biostatistics, psychometrics, analytics, quantitative psychology, banking, and public health can also be beneficial, especially if you plan to enter a career that centers around one of these topics or a related field. However, they aren't necessary for learning about Bayesian statistics in general.‎

    People who aspire to work in roles that use Bayesian statistics should have analytical minds and a passion for using data to help other businesses and other people. You'll need good computer skills and a passion for statistics. You'll also need to be a good multitasker with excellent time management skills as well as someone who is highly organized. Good problem-solving skills are a must, as is flexibility. There are times when you may have total autonomy over your job and others when you're working with a team. That means you'll also need great interpersonal skills and the ability to communicate well, both verbally and in writing.‎

    Anyone who works with data or seeks a career working with data may be interested in learning Bayesian statistics. Many companies that seek employees to work in fields involving statistics or big data prefer someone who understands and can implement the theories of Bayesian statistics to someone who can't. These companies typically offer competitive salaries and benefits and room for career advancement. Careers that may use Bayesian statistics also tend to have a good outlook for the future. Best of all, learning about this topic can open you up to jobs in numerous industries, ranging from banking and finance to health care and biostatistics.‎

    Online Bayesian Statistics courses offer a convenient and flexible way to enhance your existing knowledge or learn new Bayesian Statistics skills. With a wide range of Bayesian Statistics classes, you can conveniently learn at your own pace to advance your Bayesian Statistics career skills.‎

    When looking to enhance your workforce's skills in Bayesian Statistics, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

    Other topics to explore

    Placeholder
    Arts and Humanities
    338 courses
    Placeholder
    Business
    1095 courses
    Placeholder
    Computer Science
    668 courses
    Placeholder
    Data Science
    425 courses
    Placeholder
    Information Technology
    145 courses
    Placeholder
    Health
    471 courses
    Placeholder
    Math and Logic
    70 courses
    Placeholder
    Personal Development
    137 courses
    Placeholder
    Physical Science and Engineering
    413 courses
    Placeholder
    Social Sciences
    401 courses
    Placeholder
    Language Learning
    150 courses

    Coursera Footer

    Technical Skills

    • ChatGPT
    • Coding
    • Computer Science
    • Cybersecurity
    • DevOps
    • Ethical Hacking
    • Generative AI
    • Java Programming
    • Python
    • Web Development

    Analytical Skills

    • Artificial Intelligence
    • Big Data
    • Business Analysis
    • Data Analytics
    • Data Science
    • Financial Modeling
    • Machine Learning
    • Microsoft Excel
    • Microsoft Power BI
    • SQL

    Business Skills

    • Accounting
    • Digital Marketing
    • E-commerce
    • Finance
    • Google
    • Graphic Design
    • IBM
    • Marketing
    • Project Management
    • Social Media Marketing

    Career Resources

    • Essential IT Certifications
    • High-Income Skills to Learn
    • How to Get a PMP Certification
    • How to Learn Artificial Intelligence
    • Popular Cybersecurity Certifications
    • Popular Data Analytics Certifications
    • What Does a Data Analyst Do?
    • Career Development Resources
    • Career Aptitude Test
    • Share your Coursera Learning Story

    Coursera

    • About
    • What We Offer
    • Leadership
    • Careers
    • Catalog
    • Coursera Plus
    • Professional Certificates
    • MasterTrack® Certificates
    • Degrees
    • For Enterprise
    • For Government
    • For Campus
    • Become a Partner
    • Social Impact
    • Free Courses
    • ECTS Credit Recommendations

    Community

    • Learners
    • Partners
    • Beta Testers
    • Blog
    • The Coursera Podcast
    • Tech Blog
    • Teaching Center

    More

    • Press
    • Investors
    • Terms
    • Privacy
    • Help
    • Accessibility
    • Contact
    • Articles
    • Directory
    • Affiliates
    • Modern Slavery Statement
    • Manage Cookie Preferences
    Learn Anywhere
    Placeholder
    Placeholder
    Placeholder
    © 2025 Coursera Inc. All rights reserved.
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder