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    • Bayesian Statistics
    Related topics:统计推论统计概率分布应用统计神经网络ETL

    筛选依据

    ''bayesian statistics'的 68 个结果

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      您将获得的技能: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Communication, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Data Management, Deep Learning, Entrepreneurship, General Statistics, Human Computer Interaction, Interactive Design, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.8

      (132.4k 条评论)

      Intermediate · Specialization · 3+ Months

    • Google Cloud

      Google Cloud

      Preparing for Google Cloud Certification: Machine Learning Engineer

      您将获得的技能: Agile Software Development, Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Business Psychology, Cloud API, Cloud Computing, Cloud Storage, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Management, 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

      (23.9k 条评论)

      Intermediate · Professional Certificate · 3+ Months

    • 免费

      Stanford University

      Stanford University

      Introduction to Statistics

      您将获得的技能: Data Analysis, Bayesian Statistics, Machine Learning, Econometrics, Regression, Analysis, Probability, Statistical Tests, Statistical Analysis, Experiment, Basic Descriptive Statistics, Probability & Statistics, General Statistics, Markov Model, Probability Distribution

      4.5

      (982 条评论)

      Beginner · Course · 1-3 Months

    • DeepLearning.AI

      DeepLearning.AI

      Neural Networks and Deep Learning

      您将获得的技能: Linear Algebra, Machine Learning, Artificial Neural Networks, Python Programming, Mathematics, Computer Programming, Theoretical Computer Science, Regression, Bayesian Statistics, General Statistics, Probability & Statistics, Machine Learning Algorithms, Algorithms, Deep Learning

      4.9

      (113.5k 条评论)

      Intermediate · Course · 1-4 Weeks

    • Duke University

      Duke University

      Data Analysis with R

      您将获得的技能: Algebra, Bayesian Statistics, Data Analysis, Data Mining, Econometrics, Experiment, General Statistics, Inference, Linearity, Machine Learning, Machine Learning Algorithms, Mathematics, Probability, Probability & Statistics, Probability Distribution, R Programming, Statistical Inference, Statistical Programming

      4.7

      (6.5k 条评论)

      Beginner · Specialization · 3+ Months

    • Google Cloud

      Google Cloud

      Machine Learning on Google Cloud

      您将获得的技能: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Business Psychology, Cloud API, Cloud Computing, Computational Thinking, Computer Architecture, Computer Programming, Data Management, Data Structures, Deep Learning, Econometrics, Entrepreneurship, Extract, Transform, Load, Feature Engineering, General Statistics, Geostatistics, Google Cloud Platform, Hardware Design, Machine Learning, Machine Learning Algorithms, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Tensorflow, Theoretical Computer Science

      4.5

      (9.1k 条评论)

      Intermediate · Specialization · 3+ Months

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      Imperial College London

      Imperial College London

      Statistical Analysis with R for Public Health

      您将获得的技能: Algebra, Analysis, Basic Descriptive Statistics, Bayesian Statistics, Business Analysis, Correlation And Dependence, Data Analysis, Econometrics, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Probability Distribution, Regression, Statistical Analysis, Statistical Programming, Statistical Tests, Supply Chain

      4.7

      (1.5k 条评论)

      Beginner · Specialization · 3+ Months

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      Duke University

      Duke University

      Entrepreneurial Finance: Strategy and Innovation

      您将获得的技能: Accounting, Algorithms, Bayesian Statistics, BlockChain, Business Analysis, Corporate Accouting, Cryptography, Data Analysis, Data Management, Data Structures, Digital Marketing, Econometrics, Entrepreneurial Finance, Entrepreneurship, FinTech, Finance, Financial Analysis, Investment Management, Marketing, Probability & Statistics, R Programming, Risk, Risk Management, Security Engineering, Statistical Programming, Statistical Tests, Theoretical Computer Science

      4.5

      (1.1k 条评论)

      Intermediate · Specialization · 3+ Months

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      Stanford University

      Stanford University

      Probabilistic Graphical Models

      您将获得的技能: Advertising, Algebra, Algorithms, Bayesian Network, Bayesian Statistics, Behavioral Economics, Business Psychology, Communication, Computer Architecture, Computer Programming, Data Analysis, Decision Making, Distributed Computing Architecture, Entrepreneurship, Feature Engineering, General Statistics, Graph Theory, Leadership and Management, Machine Learning, Marketing, Mathematics, Modeling, Other Programming Languages, Probability, Probability & Statistics, Probability Distribution, Theoretical Computer Science

      4.6

      (1.5k 条评论)

      Advanced · Specialization · 3+ Months

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      IBM

      IBM

      Advanced Data Science with IBM

      您将获得的技能: Algorithms, Apache, Apache Spark, Applied Machine Learning, Artificial Neural Networks, Basic Descriptive Statistics, Bayesian Statistics, Big Data, Change Management, Cloud Computing, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Correlation And Dependence, Data Analysis, Data Management, Data Model, Data Structures, Data Visualization, Deep Learning, Dimensionality Reduction, Distributed Computing Architecture, Econometrics, Estimation, Experiment, General Statistics, IBM Cloud, Leadership and Management, Machine Learning, Machine Learning Algorithms, Mathematics, Natural Language Processing, Probability & Statistics, Probability Distribution, Programming Principles, Python Programming, Regression, Signal Processing, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Strategy and Operations, Theoretical Computer Science

      4.3

      (2.9k 条评论)

      Advanced · Specialization · 3+ Months

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      Johns Hopkins University

      Johns Hopkins University

      Advanced Statistics for Data Science

      您将获得的技能: Algebra, Artificial Neural Networks, Bayesian Statistics, Biostatistics, Business Analysis, Calculus, Communication, Data Analysis, Dimensionality Reduction, Econometrics, Experiment, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Marketing, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Statistical Tests

      4.4

      (654 条评论)

      Advanced · Specialization · 3+ Months

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      University of Illinois at Urbana-Champaign

      University of Illinois at Urbana-Champaign

      Data Mining

      您将获得的技能: Accounting, Algorithms, Bayesian Statistics, Big Data, Bioinformatics, Business Analysis, Calculus, Computational Logic, Computer Architecture, Computer Graphics, Computer Programming, Data Analysis, Data Clustering Algorithms, Data Management, Data Mining, Data Structures, Data Visualization, Databases, Distributed Computing Architecture, Financial Analysis, General Statistics, Geovisualization, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Probability & Statistics, SQL, Statistical Analysis, Statistical Programming, Theoretical Computer Science, Visualization (Computer Graphics)

      4.5

      (2.7k 条评论)

      Intermediate · Specialization · 3+ Months

    与 bayesian statistics 相关的搜索

    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

    总之,这是我们最受欢迎的 bayesian statistics 门课程中的 10 门

    • Deep Learning: DeepLearning.AI
    • Preparing for Google Cloud Certification: Machine Learning Engineer: Google Cloud
    • Introduction to Statistics: Stanford University
    • Neural Networks and Deep Learning: DeepLearning.AI
    • Data Analysis with R: Duke University
    • Machine Learning on Google Cloud: Google Cloud
    • Statistical Analysis with R for Public Health: Imperial College London
    • Entrepreneurial Finance: Strategy and Innovation: Duke University
    • Probabilistic Graphical Models: Stanford University
    • Advanced Data Science with IBM: IBM

    您可以在 Probability And Statistics 中学到的技能

    R 语言程序设计(中文版) (19)
    推断 (16)
    线性回归 (12)
    统计分析 (12)
    统计推断 (11)
    回归分析 (10)
    生物统计学 (9)
    贝叶斯定理 (7)
    逻辑回归 (7)
    概率分布 (7)
    贝叶斯统计 (6)
    医学统计 (6)

    关于 贝叶斯统计 的常见问题

    • 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.‎

    此常见问题解答内容仅供参考。建议学生多做研究,确保所追求的课程和其他证书符合他们的个人、专业和财务目标。
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