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    • Bayesian Statistics

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    「bayesian statistics」の67件の結果

    • University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics

      習得できるスキル: 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件のレビュー)

      Intermediate · Specialization · 3+ Months

    • University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      習得できるスキル: General Statistics, R Programming, Inference, Probability & Statistics, Probability, Probability Distribution, Bayesian Statistics, Bayesian, Statistical Programming

      4.6

      (2.9k件のレビュー)

      Intermediate · Course · 1-4 Weeks

    • Duke University

      Duke University

      Bayesian Statistics

      習得できるスキル: General Statistics, Probability & Statistics, Bayesian Statistics, Bayesian, Probability Distribution

      3.8

      (771件のレビュー)

      Intermediate · Course · 1-3 Months

    • University of Michigan

      University of Michigan

      Statistics with Python

      習得できるスキル: Bayesian Statistics, Business Analysis, Communication, Computer Programming, Confidence, Data Analysis, Data Visualization, Econometrics, Experiment, General Statistics, Hypothesis, Inference, Machine Learning, Machine Learning Algorithms, Marketing, Modeling, Probability & Statistics, Programming Principles, Python Programming, Regression, Statistical Analysis, Statistical Hypothesis Testing, Statistical Inference, Statistical Programming, Statistical Tests, Statistical Visualization

      4.6

      (2.7k件のレビュー)

      Beginner · Specialization · 1-3 Months

    • 無料

      University of Zurich

      University of Zurich

      An Intuitive Introduction to Probability

      習得できるスキル: Probability & Statistics, General Statistics, Chi-Squared Distribution, Basic Descriptive Statistics, Data Analysis, Machine Learning, Bayesian Statistics, Bayesian Network, Probability, Studentized Residual, Probability Distribution

      4.8

      (1.3k件のレビュー)

      Beginner · Course · 1-3 Months

    • Databricks

      Databricks

      Introduction to Bayesian Statistics

      習得できるスキル: General Statistics, Probability & Statistics, Probability Distribution, Bayesian Statistics

      3.3

      (21件のレビュー)

      Beginner · Course · 1-4 Weeks

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      無料

      Duke University

      Duke University

      Data Science Math Skills

      習得できるスキル: Algebra, Theoretical Computer Science, Probability & Statistics, General Statistics, Factorial, Mathematical Theory & Analysis, Computational Logic, Graph Theory, Probability Distribution, Bayesian Statistics, Mathematics, Probability

      4.5

      (10.2k件のレビュー)

      Beginner · Course · 1-3 Months

    • Placeholder

      無料

      Eindhoven University of Technology

      Eindhoven University of Technology

      Improving your statistical inferences

      習得できるスキル: Inference, Experiment, Statistical Inference, General Statistics, Bayesian Network, Probability & Statistics, Interpretation, Bayesian Statistics, Statistical Tests, Bayesian, Machine Learning

      4.9

      (715件のレビュー)

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Mixture Models

      習得できるスキル: Theoretical Computer Science, Statistical Machine Learning, General Statistics, Probability & Statistics, Bayesian Statistics, Machine Learning

      4.6

      (28件のレビュー)

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      習得できるスキル: General Statistics, Econometrics, Probability & Statistics, Machine Learning, Graph Theory, Regression, Statistical Programming, Statistical Machine Learning, Bayesian, Bayesian Statistics, Markov Model, Probability Distribution, Mathematics

      4.8

      (438件のレビュー)

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Time Series Analysis

      習得できるスキル: Forecasting, General Statistics, Probability & Statistics

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Capstone Project

      Advanced · Course · 1-4 Weeks

    bayesian statisticsに関連する検索

    bayesian statistics: techniques and models
    bayesian statistics: time series analysis
    bayesian statistics: from concept to data analysis
    bayesian statistics: mixture models
    bayesian statistics: capstone project
    introduction to bayesian statistics
    1234…6

    要約して、bayesian statistics の人気コース10選をご紹介します。

    • Bayesian Statistics: University of California, Santa Cruz
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
    • Bayesian Statistics: Duke University
    • Statistics with Python: University of Michigan
    • An Intuitive Introduction to Probability: University of Zurich
    • Introduction to Bayesian Statistics: Databricks
    • Data Science Math Skills: Duke University
    • Improving your statistical inferences: Eindhoven University of Technology
    • Bayesian Statistics: Mixture Models: University of California, Santa Cruz
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz

    Probability And Statisticsで学べるスキル

    Rプログラミング (19)
    推論 (16)
    線形回帰 (12)
    統計解析 (12)
    統計的推論 (11)
    回帰分析 (10)
    生物統計学 (9)
    ベイズ (7)
    ロジスティック回帰 (7)
    確率分布 (7)
    医療統計 (6)

    ベイズ統計に関するよくある質問

    • 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.‎

    このFAQの内容は、情報提供のみを目的としています。受講生は、自分の個人的、職業的、経済的な目標に合ったコースやその他の資格を取得するために、さらに調べることをお勧めします。
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