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    • Bayesian Statistics

    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

    • 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 Methods, Statistical Analysis, Advanced Analytics, Markov Model, Mathematical Modeling, Microsoft Excel, 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, Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      489 reviews

      Intermediate · Course · 1 - 3 Months

    • 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

    • S

      Stanford University

      Probabilistic Graphical Models

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

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

      Advanced · Specialization · 3 - 6 Months

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      I

      Illinois Tech

      Bayesian Computational Statistics

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

      Build toward a degree

      Intermediate · Course · 1 - 3 Months

    • Placeholder
      D

      Duke University

      Data Analysis with R

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

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

      Beginner · 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 Software, Probability & Statistics, Statistical Analysis, Jupyter, Statistical Programming, Regression Analysis, Data Visualization Software, Predictive Modeling, Exploratory Data Analysis, Descriptive Statistics, Data Analysis

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

      Beginner · Specialization · 1 - 3 Months

    • Placeholder
      U

      University of Colorado Boulder

      Statistical Modeling for Data Science Applications

      Skills you'll gain: Statistical Inference, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, R Programming, Data Ethics, Experimentation, Research Design, Statistical Methods, Data Science, Data Analysis, Statistical Analysis, Data Modeling, Predictive Modeling, Predictive Analytics, Probability & Statistics, Mathematical Modeling, Forecasting, A/B Testing, Quantitative Research

      Build toward a degree

      4.1
      Rating, 4.1 out of 5 stars
      ·
      52 reviews

      Intermediate · Specialization · 3 - 6 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

    • Placeholder
      Status: Free
      Free
      E

      Eindhoven University of Technology

      Improving your statistical inferences

      Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Quantitative Research, Bayesian Statistics, Statistical Analysis, Sample Size Determination, Data Analysis, Research, Research Design, Research Methodologies, Data Sharing, Probability

      4.9
      Rating, 4.9 out of 5 stars
      ·
      793 reviews

      Intermediate · Course · 1 - 3 Months

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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: Duke University
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
    • Introduction to Statistics: Stanford University
    • Probabilistic Graphical Models: Stanford University
    • Bayesian Computational Statistics: Illinois Tech
    • Data Analysis with R: Duke University
    • Statistics with Python: University of Michigan
    • Statistical Modeling for Data Science Applications: University of Colorado Boulder

    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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