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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

    • K

      Kennesaw State University

      Six Sigma Tools for Improve and Control

      Skills you'll gain: Six Sigma Methodology, Process Improvement, Process Optimization, Correlation Analysis, Statistical Hypothesis Testing, Lean Six Sigma, Kaizen Methodology, Quality Improvement, Regression Analysis, Statistical Process Controls, Continuous Improvement Process, Process Capability, Quality Management, Project Management, Cost Benefit Analysis, Statistical Inference, Document Control

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.3K reviews

      Beginner · Course · 1 - 3 Months

    • N

      New York University

      Machine Learning and Reinforcement Learning in Finance

      Skills you'll gain: Supervised Learning, Reinforcement Learning, Applied Machine Learning, Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Derivatives, Scikit Learn (Machine Learning Library), Markov Model, Regression Analysis, Deep Learning, Market Liquidity, Financial Services

      3.7
      Rating, 3.7 out of 5 stars
      ·
      814 reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      AI for Medical Prognosis

      Skills you'll gain: Risk Modeling, Decision Tree Learning, Predictive Modeling, Feature Engineering, Applied Machine Learning, Random Forest Algorithm, Forecasting, Machine Learning, Statistical Methods, Statistical Analysis, Probability & Statistics, Data Analysis, Regression Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      786 reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of Pennsylvania

      A Crash Course in Causality: Inferring Causal Effects from Observational Data

      Skills you'll gain: R Programming, Statistical Analysis, Statistical Methods, Statistical Software, Statistical Modeling, Statistical Inference, Data Analysis, Probability & Statistics, Regression Analysis, Research Design, Graph Theory

      4.7
      Rating, 4.7 out of 5 stars
      ·
      564 reviews

      Intermediate · Course · 1 - 3 Months

    • I

      Imperial College London

      Foundations of Public Health Practice

      Skills you'll gain: Public Health, Health Equity, Health Disparities, Microbiology, Health Assessment, Sanitation, Program Evaluation, Epidemiology, Infectious Diseases, Health Policy, Preventative Care, Behavioral Economics, Community Health, Data Literacy, Behavioral Health, Intelligence Collection and Analysis, Emergency Response, Health And Safety Standards, Public Health and Disease Prevention, Health Systems

      4.8
      Rating, 4.8 out of 5 stars
      ·
      624 reviews

      Beginner · Specialization · 3 - 6 Months

    • M

      Microsoft

      Microsoft AI & ML Engineering

      Skills you'll gain: Unsupervised Learning, Generative AI, Large Language Modeling, Data Management, Natural Language Processing, MLOps (Machine Learning Operations), Supervised Learning, Microsoft Azure, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Infrastructure Architecture, Cloud Infrastructure, Generative AI Agents, Applied Machine Learning, Reinforcement Learning, Data Ethics, Prompt Engineering, Data Processing, Artificial Intelligence, Application Deployment

      4.6
      Rating, 4.6 out of 5 stars
      ·
      127 reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • U

      University of Colorado Boulder

      The Structured Query Language (SQL)

      Skills you'll gain: SQL, Database Management, Query Languages, Relational Databases, Database Design, Database Theory, Data Analysis, Data Access, Data Manipulation, Data Modeling

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      999 reviews

      Beginner · Course · 1 - 3 Months

    • S

      SAS

      SAS Visual Business Analytics

      Skills you'll gain: SAS (Software), Network Analysis, Trend Analysis, Data Manipulation, Data Analysis, Forecasting, Data Quality, Text Mining, Exploratory Data Analysis, Ad Hoc Reporting, Spatial Data Analysis, Data Visualization Software, Spatial Analysis, Dashboard, Time Series Analysis and Forecasting, Business Analytics, Interactive Data Visualization, Data-Driven Decision-Making, Predictive Analytics, Data Visualization

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.1K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • K

      Kennesaw State University

      Six Sigma Advanced Define and Measure Phases

      Skills you'll gain: Process Capability, Team Management, Statistical Process Controls, Exploratory Data Analysis, Six Sigma Methodology, Probability & Statistics, Process Analysis, Statistical Analysis, Lean Six Sigma, Process Mapping, Correlation Analysis, Data Analysis, Data Collection, Regression Analysis, Process Improvement, Quality Improvement, Business Process, Graphing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 3 Months

    • I

      Illinois Tech

      Statistical Learning

      Skills you'll gain: Statistical Analysis, Data Analysis, Data Science, Statistical Programming, Statistical Machine Learning, Statistical Methods, Statistical Modeling, Machine Learning Algorithms, Applied Machine Learning, Regression Analysis, Probability & Statistics, Advanced Analytics, Machine Learning, Bayesian Statistics, Statistical Inference, Supervised Learning, Predictive Modeling, Classification And Regression Tree (CART), Unsupervised Learning, Feature Engineering

      Build toward a degree

      Intermediate · Course · 1 - 3 Months

    • 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, Social Media Marketing, Customer Analysis, Search Engine Marketing, Marketing Strategies, Social Media Strategy, Customer experience strategy (CX), Performance Reporting, Search Engine Optimization, Predictive Analytics, MarTech

      4.7
      Rating, 4.7 out of 5 stars
      ·
      232 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • F

      Fractal Analytics

      Fractal Data Science

      Skills you'll gain: Data Storytelling, Decision Making, Critical Thinking, Database Design, Data Manipulation, Data Presentation, Power BI, Data Visualization, Exploratory Data Analysis, Feature Engineering, Interactive Data Visualization, Data Analysis Expressions (DAX), Human Centered Design, Storyboarding, SQL, Applied Machine Learning, Problem Solving, Data Modeling, Machine Learning, Machine Learning Algorithms

      4.5
      Rating, 4.5 out of 5 stars
      ·
      337 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    Bayesian Statistics learners also search

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    1…192021…106

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

    • Six Sigma Tools for Improve and Control: Kennesaw State University
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • AI for Medical Prognosis : DeepLearning.AI
    • A Crash Course in Causality: Inferring Causal Effects from Observational Data: University of Pennsylvania
    • Foundations of Public Health Practice: Imperial College London
    • Microsoft AI & ML Engineering: Microsoft
    • The Structured Query Language (SQL): University of Colorado Boulder
    • SAS Visual Business Analytics: SAS
    • Six Sigma Advanced Define and Measure Phases: Kennesaw State University
    • Statistical Learning: 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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