For IndividualsFor BusinessesFor UniversitiesFor Governments
Coursera
  • All DegreesExplore Bachelor’s & Master’s degrees
  • Computer Science & EngineeringExplore Computer Science & Engineering degrees
  • BusinessExplore MBA & Business degrees
  • Bachelor’s DegreesExplore master’s degrees from leading universities
  • MasterTrack™Earn credit towards a Master’s degree
  • University CertificatesAdvance your career with graduate-level learning
Find your New Career
  • Browse
  • Top Courses
  • Log In
  • Join for Free
    Coursera
    • Browse
    • Bayesian Statistics

    Filter by

    114 results for "bayesian statistics"

    • Placeholder
      University of California, Santa Cruz

      Bayesian Statistics

      Skills you'll gain: Probability & Statistics, Bayesian Statistics, General Statistics, Probability Distribution, Data Science, Statistical Programming, R Programming, Regression, Forecasting, Machine Learning, Markov Model, Statistical Machine Learning, Bayesian Network, Basic Descriptive Statistics, Estimation, Experiment, Correlation And Dependence, Data Visualization, Machine Learning Algorithms, Statistical Tests, Statistical Visualization, Advertising, Business Analysis, Communication, Data Analysis, Graph Theory, Marketing, Mathematics, Statistical Analysis

      4.6

      (3.3k reviews)

      Intermediate · Specialization · 3-6 Months

    • Placeholder
      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, General Statistics, Probability & Statistics, Regression, Mathematics, Statistical Programming, R Programming, Probability Distribution

      3.8

      (787 reviews)

      Intermediate · Course · 1-3 Months

    • Placeholder
      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      Skills you'll gain: Data Science, General Statistics, Probability & Statistics, Bayesian Statistics, Probability Distribution, R Programming, Statistical Programming, Basic Descriptive Statistics, Experiment, Regression, Estimation

      4.6

      (3.1k reviews)

      Intermediate · Course · 1-4 Weeks

    • Placeholder
      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      Skills you'll gain: Probability & Statistics, Bayesian Statistics, Probability Distribution, R Programming, Statistical Programming, Regression, General Statistics, Machine Learning, Estimation, Markov Model, Basic Descriptive Statistics, Correlation And Dependence, Data Visualization, Experiment, Machine Learning Algorithms, Statistical Machine Learning, Statistical Tests, Statistical Visualization, Business Analysis, Data Analysis, Graph Theory, Mathematics, Statistical Analysis

      4.8

      (456 reviews)

      Intermediate · Course · 1-3 Months

    • Placeholder
      Databricks

      Introduction to Bayesian Statistics

      Skills you'll gain: General Statistics, Probability & Statistics, Probability Distribution, Bayesian Statistics, Python Programming

      3.5

      (31 reviews)

      Beginner · Course · 1-4 Weeks

    • Placeholder
      University of California, Santa Cruz

      Bayesian Statistics: Mixture Models

      Skills you'll gain: Bayesian Statistics, General Statistics, Probability & Statistics, Probability Distribution, Bayesian Network, Machine Learning, Markov Model, Statistical Machine Learning, Statistical Programming, Advertising, Communication, Marketing, R Programming

      4.6

      (39 reviews)

      Intermediate · Course · 1-3 Months

    • Placeholder
      Placeholder
      Stanford University

      Probabilistic Graphical Models

      Skills you'll gain: Probability & Statistics, Machine Learning, Bayesian Network, General Statistics, Markov Model, Bayesian Statistics, Probability Distribution, Computer Architecture, Distributed Computing Architecture, Leadership and Management, Other Programming Languages, Computer Programming, Machine Learning Algorithms, Statistical Machine Learning, Applied Machine Learning, Correlation And Dependence, Behavioral Economics, Business Psychology, Data Analysis, Graph Theory, Mathematics, Algebra, Geovisualization

      4.6

      (1.5k reviews)

      Advanced · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      Johns Hopkins University

      Statistical Inference

      Skills you'll gain: Bayesian Statistics, General Statistics, Probability & Statistics, Statistical Analysis, Statistical Programming, Statistical Tests, Data Analysis, Basic Descriptive Statistics, Business Analysis, Probability Distribution

      4.2

      (4.4k reviews)

      Mixed · Course · 1-4 Weeks

    • Placeholder

      Free

      Placeholder
      Eindhoven University of Technology

      Improving your statistical inferences

      Skills you'll gain: Probability & Statistics, Statistical Tests, General Statistics, R Programming, Statistical Programming, Bayesian Statistics, Data Analysis, Probability Distribution, Statistical Analysis, Bayesian Network, Business Analysis, Experiment, Machine Learning

      4.9

      (752 reviews)

      Intermediate · Course · 1-3 Months

    • Placeholder
      Placeholder
      Duke University

      Introduction to Probability and Data with R

      Skills you'll gain: General Statistics, Probability & Statistics, Probability Distribution, Statistical Tests, Data Analysis, Statistical Analysis, Correlation And Dependence, Experiment, R Programming, Basic Descriptive Statistics, Bayesian Statistics, Data Mining, Plot (Graphics), Statistical Visualization, Data Analysis Software, Data Visualization, Exploratory Data Analysis, Statistical Programming

      4.7

      (5.4k reviews)

      Beginner · Course · 1-3 Months

    • Placeholder
      Placeholder
      Stanford University

      Probabilistic Graphical Models 1: Representation

      Skills you'll gain: Probability & Statistics, General Statistics, Machine Learning, Bayesian Network, Markov Model, Leadership and Management, Probability Distribution, Bayesian Statistics, Computer Programming, Behavioral Economics, Business Psychology, Data Analysis, Graph Theory, Machine Learning Algorithms, Mathematics, Other Programming Languages, Statistical Machine Learning

      4.6

      (1.4k reviews)

      Advanced · Course · 1-3 Months

    • Placeholder
      Placeholder
      Johns Hopkins University

      Data Science: Statistics and Machine Learning

      Skills you'll gain: R Programming, Statistical Programming, General Statistics, Statistical Analysis, Data Analysis, Machine Learning, Probability & Statistics, Statistical Tests, Data Science, Machine Learning Software, Basic Descriptive Statistics, Bayesian Statistics, Correlation And Dependence, Econometrics, Estimation, Linear Algebra, Regression, Exploratory Data Analysis, Theoretical Computer Science, Data Visualization, Interactive Data Visualization, Natural Language Processing, Probability Distribution, Plot (Graphics), Machine Learning Algorithms, Algorithms, Applied Machine Learning, Business Analysis

      4.4

      (7.1k reviews)

      Intermediate · Specialization · 3-6 Months

    Searches related to bayesian statistics

    bayesian statistics: techniques and models
    bayesian statistics: from concept to data analysis
    bayesian statistics: time series analysis
    bayesian statistics: mixture models
    bayesian statistics: capstone project
    introduction to bayesian statistics
    1234…10

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

      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.‎

      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

      Learn Something New

      • Learn a Language
      • Learn Accounting
      • Learn Coding
      • Learn Copywriting
      • Learn HR
      • Learn Public Relations
      • Boulder MS Data Science
      • Illinois iMBA
      • Illinois MS Computer Science
      • UMich MS in Applied Data Science

      Popular Data Science Topics

      • Artificial Intelligence
      • Data Analysis
      • Data Engineering
      • Data Science
      • Excel
      • Machine Learning
      • Python
      • Power BI
      • R Programming
      • SQL

      Popular Computer Science & IT Topics

      • Blockchain
      • Coding
      • Computer Science
      • Cybersecurity
      • Full Stack Web Development
      • IT
      • Java
      • Software Engineering
      • Web Design
      • Web Development

      Popular Business Topics

      • Accounting
      • Business Finance
      • Communication Skills
      • Leadership & Management
      • Marketing
      • Product Management
      • Project Management
      • UX Design
      • UX Research
      • Writing

      Coursera

      • About
      • What We Offer
      • Leadership
      • Careers
      • Catalog
      • Coursera Plus
      • Professional Certificates
      • MasterTrack® Certificates
      • Degrees
      • For Enterprise
      • For Government
      • For Campus
      • Become a Partner
      • Coronavirus Response
      • Free Courses
      • All Courses

      Community

      • Learners
      • Partners
      • Beta Testers
      • Translators
      • Blog
      • Tech Blog
      • Teaching Center

      More

      • Press
      • Investors
      • Terms
      • Privacy
      • Help
      • Accessibility
      • Contact
      • Articles
      • Directory
      • Affiliates
      • Modern Slavery Statement
      Learn Anywhere
      Placeholder
      Placeholder
      Placeholder
      © 2023 Coursera Inc. All rights reserved.
      • Placeholder
      • Placeholder
      • Placeholder
      • Placeholder
      • Placeholder
      • Placeholder