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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
      G

      Google Cloud

      Machine Learning with TensorFlow Google Cloud 日本語版

      Skills you'll gain: Feature Engineering, Tensorflow, Google Cloud Platform, Data Quality, Data Cleansing, Machine Learning, Keras (Neural Network Library), Applied Machine Learning, Exploratory Data Analysis, Machine Learning Algorithms, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Data Pipelines, PyTorch (Machine Learning Library), Data Validation, Jupyter, Dataflow, Artificial Neural Networks, Performance Tuning, Data Transformation

      4.4
      Rating, 4.4 out of 5 stars
      ·
      152 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Social Media Advertising

      Skills you'll gain: Social Media, Online Advertising, Digital Advertising, Social Media Marketing, Social Media Strategy, Advertising, Advertising Campaigns, Web Analytics, Content Performance Analysis, Facebook, Paid media, Instagram, Marketing Communications, Target Audience, Business Ethics, Brand Awareness, Customer Engagement

      4.6
      Rating, 4.6 out of 5 stars
      ·
      322 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IE Business School

      Trade, Immigration and Exchange Rates in a Globalized World

      Skills you'll gain: International Finance, Economics, Financial Policy, International Relations, Economics, Policy, and Social Studies, Economic Development, Global Marketing, Socioeconomics, Supply And Demand, Demography, Analysis, Trend Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      296 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      Illinois Tech

      Statistical Learning

      Skills you'll gain: Statistical Analysis, Data Analysis, Data Science, Statistical Programming, Statistical Methods, Statistical Machine Learning, Regression Analysis, Supervised Learning, Statistical Inference, Machine Learning, Unsupervised Learning, Predictive Modeling, Classification And Regression Tree (CART), Feature Engineering

      Build toward a degree

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      A

      Arizona State University

      Factorial and Fractional Factorial Designs

      Skills you'll gain: Experimentation, Research Design, Statistical Analysis, Statistical Methods, Statistical Hypothesis Testing, Variance Analysis, Data Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      76 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      W

      Wesleyan University

      Machine Learning for Data Analysis

      Skills you'll gain: Classification And Regression Tree (CART), Decision Tree Learning, Predictive Modeling, Random Forest Algorithm, Applied Machine Learning, Predictive Analytics, Unsupervised Learning, Machine Learning, Supervised Learning, Data Analysis, Data Mining, Feature Engineering, Exploratory Data Analysis, Regression Analysis, Statistical Analysis, Statistical Methods

      4.2
      Rating, 4.2 out of 5 stars
      ·
      324 reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Colorado System

      Computational Thinking with Beginning C Programming

      Skills you'll gain: Computational Thinking, Data Collection, Simulations, Data Analysis, Microsoft Visual Studio, C (Programming Language), Statistical Analysis, Automation, Program Development, Data Structures, Programming Principles, Algorithms, Computer Programming, Theoretical Computer Science, Development Environment, Descriptive Statistics, Problem Management, File Management, Debugging, Data Storage

      4.6
      Rating, 4.6 out of 5 stars
      ·
      432 reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Amsterdam

      Methods and Statistics in Social Science - Final Research Project

      Skills you'll gain: Statistical Analysis, Research, Research Reports, Data Analysis, Research Design, Data Collection, Research Methodologies, Surveys, Qualitative Research, Experimentation, Descriptive Statistics, Survey Creation

      4.4
      Rating, 4.4 out of 5 stars
      ·
      63 reviews

      Beginner · Course · 1 - 3 Months

    • T

      The State University of New York

      Strategic Career Self-Management

      Skills you'll gain: Job Analysis, Adaptability, Professional Development, Personal Development, Lifelong Learning, Brand Management, Talent Management, Branding, Gap Analysis, Competitive Analysis, Self-Awareness, Proactivity, Dashboard, Goal Setting

      4.5
      Rating, 4.5 out of 5 stars
      ·
      304 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Data Mining Pipeline

      Skills you'll gain: Data Mining, Data Warehousing, Data Pipelines, Data Processing, Data Integration, Data Modeling, Data Cleansing, Data Transformation, Data Quality, Data Analysis, Data Visualization, Descriptive Analytics, Exploratory Data Analysis

      Build toward a degree

      3.9
      Rating, 3.9 out of 5 stars
      ·
      88 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      A

      Alberta Machine Intelligence Institute

      Data for Machine Learning

      Skills you'll gain: Feature Engineering, Data Quality, Data Processing, Supervised Learning, Data Validation, Data Cleansing, Data Transformation, Verification And Validation, Applied Machine Learning, Machine Learning, Unsupervised Learning, Machine Learning Algorithms, Exploratory Data Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      98 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      M

      Microsoft

      AI and Machine Learning Algorithms and Techniques

      Skills you'll gain: Unsupervised Learning, Generative AI, Large Language Modeling, Supervised Learning, Deep Learning, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Reinforcement Learning, Statistical Machine Learning, Predictive Modeling, Machine Learning Algorithms, Artificial Neural Networks, Feature Engineering, Decision Tree Learning, Business Logic, Dimensionality Reduction, Data Modeling, Performance Metric

      4.7
      Rating, 4.7 out of 5 stars
      ·
      30 reviews

      Intermediate · Course · 1 - 3 Months

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    1…464748…107

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

    • Machine Learning with TensorFlow Google Cloud 日本語版: Google Cloud
    • Social Media Advertising: University of Colorado Boulder
    • Trade, Immigration and Exchange Rates in a Globalized World: IE Business School
    • Statistical Learning: Illinois Tech
    • Factorial and Fractional Factorial Designs: Arizona State University
    • Machine Learning for Data Analysis: Wesleyan University
    • Computational Thinking with Beginning C Programming: University of Colorado System
    • Methods and Statistics in Social Science - Final Research Project: University of Amsterdam
    • Strategic Career Self-Management: The State University of New York
    • Data Mining Pipeline: 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)
    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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