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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

    • E

      Emory University

      Math for MBA and GMAT Prep

      Skills you'll gain: Regression Analysis, Data Visualization, Business Mathematics, Descriptive Statistics, Microsoft Excel, Statistics, Business Analytics, Excel Formulas, Algebra, Calculus, Arithmetic

      4.2
      Rating, 4.2 out of 5 stars
      ·
      44 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Illinois Urbana-Champaign

      Exploring and Producing Data for Business Decision Making

      Skills you'll gain: Descriptive Statistics, Sampling (Statistics), Probability Distribution, Business Analytics, Statistics, Microsoft Excel, Analytics, Statistical Inference, Data Analysis, Exploratory Data Analysis, Probability & Statistics, Statistical Analysis, Histogram, Data Collection, Data Presentation, Graphing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Renewable Energy Futures

      Skills you'll gain: Energy and Utilities, Electrical Power, Electrical Systems, Electric Power Systems, Market Dynamics, Emerging Technologies, Market Trend, Trend Analysis, Market Opportunities, Environmental Issue, Forecasting, Mathematical Modeling, Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      235 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      Introduction to Business Analysis Using Spreadsheets: Basics

      Skills you'll gain: Google Sheets, Spreadsheet Software, Data Presentation, Statistical Visualization, Data Analysis, Data Visualization Software, Business Analytics, Productivity Software, Business Analysis, Data Manipulation, Descriptive Statistics, Analysis, Excel Formulas, Data Cleansing, Mathematical Software

      4.3
      Rating, 4.3 out of 5 stars
      ·
      999 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Medical Technology and Evaluation

      Skills you'll gain: Payment Systems, Healthcare Industry Knowledge, Medical Equipment and Technology, Cost Benefit Analysis, Clinical Trials, Health Technology, Clinical Data Management, Financial Regulations, Program Evaluation, Pharmaceuticals, Health Policy, Risk Analysis, Health Care Procedure and Regulation, Claims Processing, Health Care, Regulatory Affairs, Quality Assessment, Medicare, Probability & Statistics

      4.5
      Rating, 4.5 out of 5 stars
      ·
      288 reviews

      Beginner · Course · 1 - 4 Weeks

    • K

      King Abdullah University of Science and Technology

      Fundamental Skills in Bioinformatics

      Skills you'll gain: Statistical Analysis, Bioinformatics, Unix, Scientific Visualization, Statistical Methods, R Programming, Rmarkdown, Unix Commands, Data Analysis, Data Quality, Statistical Hypothesis Testing, Exploratory Data Analysis, Data Visualization, Programming Principles, Pandas (Python Package), Python Programming, NumPy, Data Manipulation, Data Structures

      4.4
      Rating, 4.4 out of 5 stars
      ·
      59 reviews

      Beginner · Course · 1 - 4 Weeks

    • C

      Caltech

      Pricing Options with Mathematical Models

      Skills you'll gain: Derivatives, Financial Market, Risk Modeling, Mathematical Modeling, Financial Modeling, Credit Risk, Risk Management, Portfolio Management, Probability, Differential Equations, Applied Mathematics, Probability Distribution, Calculus

      4.7
      Rating, 4.7 out of 5 stars
      ·
      36 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Machine Learning Capstone

      Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Data Analysis, Applied Machine Learning, Statistical Analysis, Data Presentation, Technical Communication, Machine Learning, Scikit Learn (Machine Learning Library), Python Programming, Tensorflow, Regression Analysis, Keras (Neural Network Library), Artificial Neural Networks

      4.6
      Rating, 4.6 out of 5 stars
      ·
      134 reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Amsterdam

      Unraveling the Cycling City

      Skills you'll gain: Sociology, Systems Thinking, Economics, Policy, and Social Studies, Cultural Diversity, Policy Analysis, Geographic Information Systems, Environmental Science, Spatial Analysis, Public Policies, Qualitative Research, Environment and Resource Management, European History

      4.9
      Rating, 4.9 out of 5 stars
      ·
      248 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Surveillance Systems: The Building Blocks

      Skills you'll gain: Epidemiology, Public Health, Public Health and Disease Prevention, Health Systems, Health Policy, Program Evaluation, Infectious Diseases, Surveys, Data Collection, Trend Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      489 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      V

      Vanderbilt University

      ChatGPT + Excel: Master Data, Make Decisions, Tell Stories

      Skills you'll gain: Data Storytelling, Data Presentation, Data Synthesis, Microsoft Excel, Infographics, Data Analysis, Data Cleansing, Data Import/Export, ChatGPT, Statistical Reporting, Data Integration, Data Transformation, Data Validation, Exploratory Data Analysis, Prompt Engineering, Data Quality, Generative AI

      4.8
      Rating, 4.8 out of 5 stars
      ·
      67 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      Universidad Austral

      Estadística aplicada a los negocios

      Skills you'll gain: Regression Analysis, Statistical Inference, Descriptive Statistics, Business Risk Management, Risk Analysis, Business Analytics, Statistics, Sampling (Statistics), Microsoft Excel, Data Analysis, Probability, Statistical Analysis, Data-Driven Decision-Making, Probability Distribution, Statistical Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      756 reviews

      Beginner · Course · 1 - 4 Weeks

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    1…353637…107

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

    • Math for MBA and GMAT Prep: Emory University
    • Exploring and Producing Data for Business Decision Making: University of Illinois Urbana-Champaign
    • Renewable Energy Futures: University of Colorado Boulder
    • Introduction to Business Analysis Using Spreadsheets: Basics: Coursera Project Network
    • Medical Technology and Evaluation: University of Minnesota
    • Fundamental Skills in Bioinformatics: King Abdullah University of Science and Technology
    • Pricing Options with Mathematical Models: Caltech
    • Machine Learning Capstone: IBM
    • Unraveling the Cycling City: University of Amsterdam
    • Surveillance Systems: The Building Blocks: Johns Hopkins 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)
    Bayesian Statistics (6)
    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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