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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 Illinois Urbana-Champaign

      Text Mining and Analytics

      Skills you'll gain: Text Mining, Data Mining, Unstructured Data, Statistical Analysis, Natural Language Processing, Analytics, Data Analysis, Unsupervised Learning, Probability & Statistics, Regression Analysis, Predictive Modeling, Supervised Learning, Machine Learning Algorithms

      4.5
      Rating, 4.5 out of 5 stars
      ·
      735 reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      E

      Emory University

      Foundations of Marketing Analytics

      Skills you'll gain: Pivot Tables And Charts, Customer Analysis, Probability Distribution, Customer Demand Planning, Marketing Analytics, Forecasting, Descriptive Statistics, Microsoft Excel, Social Media Marketing, Exploratory Data Analysis, Probability, Statistical Analysis, Marketing Effectiveness, Target Market, Social Media, Revenue Forecasting, Time Series Analysis and Forecasting, Statistics, Predictive Modeling, Marketing

      4.3
      Rating, 4.3 out of 5 stars
      ·
      516 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of California San Diego

      Machine Learning With Big Data

      Skills you'll gain: Exploratory Data Analysis, Apache Spark, Big Data, Regression Analysis, Data Mining, Applied Machine Learning, Statistical Analysis, Machine Learning, Data Analysis, Unsupervised Learning, Data Transformation, Predictive Modeling, Data Cleansing, Supervised Learning, Decision Tree Learning

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.5K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      Imperial College London

      TensorFlow 2 for Deep Learning

      Skills you'll gain: Tensorflow, Generative AI, Data Pipelines, Keras (Neural Network Library), Deep Learning, Image Analysis, Computer Programming, Bayesian Statistics, Supervised Learning, Natural Language Processing, Data Processing, Computer Vision, Machine Learning Methods, Artificial Neural Networks, Machine Learning, Unsupervised Learning, Probability & Statistics, Time Series Analysis and Forecasting, Jupyter, Dimensionality Reduction

      4.8
      Rating, 4.8 out of 5 stars
      ·
      709 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Y

      Yale University

      Rocket Science for Everyone

      Skills you'll gain: Global Positioning Systems, Geospatial Information and Technology, Geographic Information Systems, Physical Science, Spatial Analysis, Mechanics, Engineering, Physics, Electrical Engineering

      4.9
      Rating, 4.9 out of 5 stars
      ·
      545 reviews

      Beginner · Course · 1 - 3 Months

    • T

      The State University of New York

      Solar Energy System Design

      Skills you'll gain: Electrical Systems, Electrical Power, Basic Electrical Systems, Equipment Design, Energy and Utilities, Electrical Equipment, Survey Creation, System Requirements, Engineering Calculations, Performance Testing, Physical Science, Spatial Analysis, Estimation, Physics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      452 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University at Buffalo

      Computer Vision Basics

      Skills you'll gain: Computer Vision, Image Analysis, Computer Graphics, Visualization (Computer Graphics), Data Processing, Digital Design, Artificial Intelligence, Matlab, Linear Algebra, Algorithms, Calculus, Probability & Statistics

      4.2
      Rating, 4.2 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Machine Learning: Classification

      Skills you'll gain: Classification And Regression Tree (CART), Applied Machine Learning, Supervised Learning, Predictive Modeling, Text Mining, Machine Learning Algorithms, Data Cleansing, Scalability, Machine Learning, Natural Language Processing, Big Data, Probability & Statistics, Algorithms

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.7K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      Unilever

      Unilever Digital Marketing Analyst

      Skills you'll gain: Data Storytelling, Marketing Automation, Web Analytics, Marketing Effectiveness, Marketing Analytics, Customer Insights, Digital Marketing, Social Media Campaigns, Market Analysis, Google Analytics, Digital Advertising, Social Media Marketing, Customer Analysis, Search Engine Marketing, Marketing Strategies, Social Media Strategy, Customer experience strategy (CX), Performance Reporting, Predictive Analytics, MarTech

      4.7
      Rating, 4.7 out of 5 stars
      ·
      245 reviews

      Beginner · Professional Certificate · 3 - 6 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, Statistical Inference, Data Visualization, Data Analysis, Data Presentation, Regression Analysis, Statistical Methods, Data Cleansing, Applied Machine Learning, Analytics, Machine Learning, Statistical Modeling, R Programming, Data Science, Probability & Statistics, Data Validation, Feature Engineering, Exploratory Data Analysis

      Build toward a degree

      4.5
      Rating, 4.5 out of 5 stars
      ·
      37 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Sports Performance Analytics

      Skills you'll gain: Forecasting, Statistical Methods, Regression Analysis, Data Cleansing, Scikit Learn (Machine Learning Library), Supervised Learning, Data Processing, Statistical Hypothesis Testing, Correlation Analysis, Predictive Analytics, Predictive Modeling, Matplotlib, Applied Machine Learning, Kinesiology, Injury Prevention, Statistical Machine Learning, Analytics, Data Analysis, Advanced Analytics, Statistical Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      256 reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      Duke University

      The Brain and Space

      Skills you'll gain: Spatial Analysis, Neurology, Human Learning, Experimentation, Laboratory Research, Physics, Biology, General Science and Research, Magnetic Resonance Imaging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      652 reviews

      Beginner · Course · 1 - 3 Months

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    1…202122…106

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

    • Text Mining and Analytics: University of Illinois Urbana-Champaign
    • Foundations of Marketing Analytics: Emory University
    • Machine Learning With Big Data: University of California San Diego
    • TensorFlow 2 for Deep Learning: Imperial College London
    • Rocket Science for Everyone: Yale University
    • Solar Energy System Design: The State University of New York
    • Computer Vision Basics: University at Buffalo
    • Machine Learning: Classification: University of Washington
    • Unilever Digital Marketing Analyst: Unilever
    • Advanced Statistical Techniques for Data Science: Illinois Tech

    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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