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Bayesian Statistics Courses Online

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

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Explore the Bayesian Statistics Course Catalog

  • Status: Free Trial
    Free Trial
    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 Presentation, Statistics, Probability, Statistical Analysis, Statistical Software, Advanced Analytics, R (Software), Data Analysis, Mathematical Modeling, Microsoft Excel, Markov Model, Statistical Methods, Data Science

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

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of California, Santa Cruz

    Bayesian Statistics: From Concept to Data Analysis

    Skills you'll gain: Bayesian Statistics, Statistical Inference, Statistics, 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

  • D

    Duke University

    Bayesian Statistics

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

    3.8
    Rating, 3.8 out of 5 stars
    ·
    798 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of California, Santa Cruz

    Bayesian Statistics: Techniques and Models

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

    4.8
    Rating, 4.8 out of 5 stars
    ·
    495 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    U

    University of Colorado Boulder

    Introduction to Bayesian Statistics for Data Science

    Skills you'll gain: Bayesian Statistics, Statistical Inference, Statistical Modeling, Predictive Analytics, Statistical Methods, Data Ethics, Data Science, Probability, Regression Analysis, Probability Distribution, Calculus

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of California, Santa Cruz

    Bayesian Statistics: Time Series Analysis

    Skills you'll gain: Time Series Analysis and Forecasting, Forecasting, R Programming, Bayesian Statistics, Advanced Analytics, Statistical Modeling, R (Software), Statistical Analysis, Statistical Inference, Data Analysis, Mathematical Modeling, Probability Distribution

    4.2
    Rating, 4.2 out of 5 stars
    ·
    17 reviews

    Intermediate · Course · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Statistics with Python

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

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

    Beginner · Specialization · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    I

    Illinois Tech

    Advanced Statistical Techniques for Data Science

    Skills you'll gain: Machine Learning Algorithms, Statistical Analysis, Bayesian Statistics, Data Visualization, Statistical Inference, Data Cleansing, Regression Analysis, Analytics, Data Analysis, Machine Learning, R Programming, Data Science, Statistical Modeling, Data Validation, Data Manipulation, Exploratory Data Analysis, R (Software), Data Mining, Statistical Programming, Predictive Modeling

    Build toward a degree

    4.5
    Rating, 4.5 out of 5 stars
    ·
    45 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Advanced Statistics for Data Science

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Regression Analysis, Bayesian Statistics, Statistical Analysis, Probability & Statistics, Statistical Inference, Statistical Methods, Statistical Modeling, Linear Algebra, Probability, R Programming, Biostatistics, Data Science, Statistics, Probability Distribution, Mathematical Modeling, Data Analysis, Applied Mathematics, Predictive Modeling

    4.4
    Rating, 4.4 out of 5 stars
    ·
    777 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    S

    Stanford University

    Probabilistic Graphical Models

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

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

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    Illinois Tech

    Bayesian Computational Statistics

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

    Build toward a degree

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of California, Santa Cruz

    Bayesian Statistics: Capstone Project

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

    Advanced · Course · 1 - 4 Weeks

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

  • Bayesian Statistics: University of California, Santa Cruz
  • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
  • Bayesian Statistics: Duke University
  • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
  • Introduction to Bayesian Statistics for Data Science: University of Colorado Boulder
  • Bayesian Statistics: Time Series Analysis: University of California, Santa Cruz
  • Statistics with Python: University of Michigan
  • Advanced Statistical Techniques for Data Science: Illinois Tech
  • Advanced Statistics for Data Science: Johns Hopkins University
  • Probabilistic Graphical Models: Stanford University

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.

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