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    • Bayesian Statistics
    Related topics:StatistiquesStatistiques déductives Distribution de probabilitéStatistiques appliquéesRéseaux de neuronesetl

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    74 résultats pour ''bayesian statistics'

    • University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics

      Compétences que vous acquerrez : Bayesian, Bayesian Statistics, Econometrics, Forecasting, General Statistics, Graph Theory, Machine Learning, Markov Model, Mathematics, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science

      4.6

      (3.2k avis)

      Intermediate · Specialization

    • University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      Compétences que vous acquerrez : General Statistics, Bayesian Statistics, Statistical Programming, Bayesian, Probability Distribution, Inference, Probability & Statistics, R Programming, Probability

      4.6

      (2.9k avis)

      Intermediate · Course

    • Duke University

      Duke University

      Bayesian Statistics

      Compétences que vous acquerrez : Bayesian Statistics, General Statistics, Bayesian, Probability Distribution, Inference, Probability & Statistics

      3.8

      (771 avis)

      Intermediate · Course

    • Gratuit

      Eindhoven University of Technology

      Eindhoven University of Technology

      Improving your statistical inferences

      Compétences que vous acquerrez : Bayesian Statistics, General Statistics, Inference, Bayesian Network, Bayesian, Statistical Inference, Machine Learning, Experiment, Probability & Statistics, Statistical Tests, Interpretation

      4.9

      (718 avis)

      Intermediate · Course

    • Microsoft

      Microsoft

      Microsoft Azure Data Scientist Associate - DP-100 Test Prep

      Compétences que vous acquerrez : Algorithms, Apache, Artificial Neural Networks, Bayesian Statistics, Big Data, Cloud Computing, Communication, Computer Programming, Computer Vision, Data Management, Deep Learning, General Statistics, Machine Learning, Marketing, Microsoft Azure, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science

      4.5

      (70 avis)

      Intermediate · Specialization

    • Google Cloud

      Google Cloud

      Preparing for Google Cloud Certification: Machine Learning Engineer

      Compétences que vous acquerrez : Agile Software Development, Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Bigquery, Business Psychology, Cloud API, Cloud Computing, Cloud Storage, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Analysis, Data Management, Data Model, Data Structures, Databases, Deep Learning, DevOps, Distributed Computing Architecture, Econometrics, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Full-Stack Web Development, General Statistics, Geostatistics, Google Cloud Platform, Hardware Design, Kubernetes, Machine Learning, Machine Learning Algorithms, Network Security, Performance Management, Probability & Statistics, Python Programming, Regression, Security Engineering, Security Strategy, Software Architecture, Software Engineering, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Tensorflow, Theoretical Computer Science, Web Development

      4.6

      (24k avis)

      Intermediate · Professional Certificate

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      Databricks

      Databricks

      Bayesian Inference with MCMC

      Compétences que vous acquerrez : Bayesian Statistics, General Statistics, Probability & Statistics

      3.0

      (10 avis)

      Beginner · Course

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      DeepLearning.AI

      DeepLearning.AI

      Neural Networks and Deep Learning

      Compétences que vous acquerrez : General Statistics, Algorithms, Python Programming, Bayesian Statistics, Computational Logic, Mathematics, Artificial Neural Networks, Regression, Mathematical Theory & Analysis, Computer Programming, Markov Model, Deep Learning, Computer Architecture, Linear Algebra, Hardware Design, Machine Learning Algorithms, Machine Learning, Probability & Statistics, Theoretical Computer Science

      4.9

      (113.9k avis)

      Intermediate · Course

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      Databricks

      Databricks

      Introduction to Bayesian Statistics

      Compétences que vous acquerrez : Bayesian Statistics, General Statistics, Probability Distribution, Probability & Statistics

      3.2

      (22 avis)

      Beginner · Course

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      Compétences que vous acquerrez : Regression, Bayesian Statistics, General Statistics, Statistical Machine Learning, Mathematics, Statistical Programming, Econometrics, Probability & Statistics, Bayesian, Probability Distribution, Graph Theory, Modeling, Markov Model, Machine Learning

      4.8

      (441 avis)

      Intermediate · Course

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      Gratuit

      University of Zurich

      University of Zurich

      An Intuitive Introduction to Probability

      Compétences que vous acquerrez : General Statistics, Chi-Squared Distribution, Bayesian Statistics, Data Analysis, Probability Distribution, Studentized Residual, Basic Descriptive Statistics, Probability, Machine Learning, Probability & Statistics, Bayesian Network

      4.8

      (1.3k avis)

      Beginner · Course

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Time Series Analysis

      Compétences que vous acquerrez : Forecasting, General Statistics, Probability & Statistics

      Intermediate · Course

    Recherches liées à bayesian statistics

    bayesian statistics: techniques and models
    bayesian statistics: from concept to data analysis
    bayesian statistics: time series analysis
    bayesian statistics: mixture models
    bayesian statistics: capstone project
    introduction to bayesian statistics
    1234…7

    En résumé, voici 10 de nos cours bayesian statistics les plus populaires

    • Bayesian Statistics: University of California, Santa Cruz
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
    • Bayesian Statistics: Duke University
    • Improving your statistical inferences: Eindhoven University of Technology
    • Microsoft Azure Data Scientist Associate - DP-100 Test Prep: Microsoft
    • Preparing for Google Cloud Certification: Machine Learning Engineer: Google Cloud
    • Bayesian Inference with MCMC: Databricks
    • Neural Networks and Deep Learning: DeepLearning.AI
    • Introduction to Bayesian Statistics: Databricks
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz

    Compétences que vous avez acquises en Probability And Statistics

    La Programmation En R (19)
    Inférence (16)
    Régression Linéaire (12)
    Analyse Statistique (12)
    Inférence Statistique (11)
    Analyse De Régression (10)
    Biostatistiques (9)
    Bayésien (7)
    Régression Logistique (7)
    Distribution De Probabilité (7)
    Statistiques Médicales (6)

    Questions fréquentes sur Statistiques bayésiennes

    • 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.‎

    Le contenu de cette FAQ a été mis à disposition à des fins d'information uniquement. Il est conseillé aux étudiants d'effectuer des recherches supplémentaires afin de s'assurer que les cours et autres qualifications suivis correspondent à leurs objectifs personnels, professionnels et financiers.
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