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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

    • 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, Statistics, Probability, Statistical Machine Learning, Statistical Analysis, Statistical Methods, Markov Model, Data Analysis, Advanced Analytics, Mathematical Modeling, Microsoft Excel, Data Science, Probability Distribution, Probability & Statistics

      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

    • Status: New
      New
      Status: Free Trial
      Free Trial
      M

      Macquarie University

      Statistics and Data Analysis with Excel

      Skills you'll gain: Interactive Data Visualization, Pivot Tables And Charts, Data Visualization, Data Presentation, Data-Driven Decision-Making, Microsoft Excel, Correlation Analysis, Forecasting, Regression Analysis, Excel Formulas, Statistics, Statistical Hypothesis Testing, Descriptive Statistics, Data Analysis, Business Analytics, Statistical Inference, Box Plots, Statistical Analysis, Statistical Modeling, Statistical Visualization

      4.9
      Rating, 4.9 out of 5 stars
      ·
      18 reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: New
      New
      Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Foundations of Probability and Statistics

      Skills you'll gain: Probability, Statistical Inference, Markov Model, Estimation, Statistical Methods, Probability & Statistics, Probability Distribution, Statistical Analysis, Sampling (Statistics), Bayesian Statistics, Statistics, Applied Mathematics, Mathematical Modeling, Statistical Modeling, Data Science, Data Analysis, Machine Learning Algorithms, Generative AI Agents, Artificial Intelligence

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      309 reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, Statistical Hypothesis Testing, Statistical Modeling, Statistical Methods, Statistical Inference, Statistical Analysis, Regression Analysis, Data Analysis, R Programming, Probability, Data-Driven Decision-Making, 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, Statistical Methods, 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

    What brings you to Coursera today?

    • 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, Predictive Modeling, Probability & Statistics, Data Science, Probability, Regression Analysis, Analytical Skills, Probability Distribution

      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, Statistical Modeling, Statistical Analysis, Statistical Inference, Regression Analysis, Data Analysis, Mathematical Modeling, Probability Distribution

      4.2
      Rating, 4.2 out of 5 stars
      ·
      17 reviews

      Intermediate · Course · 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 Presentation, Regression Analysis, Data Cleansing, Applied Machine Learning, Analytics, Data Analysis, Machine Learning, Statistical Methods, R Programming, Data Science, Statistical Modeling, Data Manipulation, Data Mining, Data Validation, Exploratory Data Analysis

      Build toward a degree

      4.5
      Rating, 4.5 out of 5 stars
      ·
      44 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Statistics with Python

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

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

      Beginner · Specialization · 1 - 3 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, Statistical Modeling, Bayesian Statistics, 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

      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 Methods, 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

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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
    • Statistics and Data Analysis with Excel: Macquarie University
    • Foundations of Probability and Statistics: University of Colorado Boulder
    • 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
    • Advanced Statistical Techniques for Data Science: Illinois Tech
    • 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.

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