What is Bayesian Statistics, and why is it important to learn about?

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.

What careers can I pursue with a background in Bayesian statistics?

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.

Can I learn about Bayesian statistics by taking online courses in Coursera?

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.

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Bayesian Statistics

Cursos de Bayesian Statistics

Explore 100% das graduações e certificados on-line na Coursera

Master of Data ScienceHSE University

Master of Machine Learning and Data ScienceImperial College London

IBM Machine LearningIBM

Bayesian Statistics: From Concept to Data Analysis
University of California, Santa Cruz
Curso
Avaliado como 4.6 de 5 estrelas. 2462 análises
110k Alunos
Intermediate
Bayesian Statistics: Techniques and Models
University of California, Santa Cruz
Curso
Avaliado como 4.8 de 5 estrelas. 357 análises
36k Alunos
Intermediate
Bayesian Statistics: Mixture Models
University of California, Santa Cruz
Curso
Avaliado como 4.8 de 5 estrelas. 9 análises
2.1k Alunos
Advanced
Probabilistic Graphical Models
Stanford University
Programa de cursos integrados
Avaliado como 4.6 de 5 estrelas. 1367 análises
75k Alunos
Advanced
Statistics with R
Duke University
Programa de cursos integrados
Avaliado como 4.7 de 5 estrelas. 5520 análises
270k Alunos
Beginner
Bayesian Statistics
Duke University
Curso
Avaliado como 3.8 de 5 estrelas. 716 análises
61k Alunos
Intermediate
Bayesian Methods for Machine Learning
National Research University Higher School of Economics
Curso
Avaliado como 4.5 de 5 estrelas. 593 análises
56k Alunos
Advanced
Advanced Machine Learning
National Research University Higher School of Economics
Programa de cursos integrados
Avaliado como 4.5 de 5 estrelas. 3433 análises
280k Alunos
Advanced
Improving your statistical inferences
Eindhoven University of Technology
Curso
Avaliado como 4.9 de 5 estrelas. 609 análises
46k Alunos
Intermediate
Statistics with Python
University of Michigan
Programa de cursos integrados
Avaliado como 4.6 de 5 estrelas. 1814 análises
68k Alunos
Beginner
Probabilistic Graphical Models 1: Representation
Stanford University
Curso
Avaliado como 4.7 de 5 estrelas. 1292 análises
72k Alunos
Advanced
Applied Machine Learning in Python
University of Michigan
Curso
Avaliado como 4.6 de 5 estrelas. 6596 análises
210k Alunos
Intermediate
An Intuitive Introduction to Probability
University of Zurich
Curso
Avaliado como 4.7 de 5 estrelas. 976 análises
35k Alunos
Beginner
Data Science Math Skills
Duke University
Curso
Avaliado como 4.5 de 5 estrelas. 7829 análises
210k Alunos
Beginner
Fitting Statistical Models to Data with Python
University of Michigan
Curso
Avaliado como 4.4 de 5 estrelas. 421 análises
17k Alunos
Intermediate
IBM Machine Learning
IBM
PROFESSIONAL CERTIFICATE
Avaliado como 4.8 de 5 estrelas. 22 análises
1.9k Alunos
Intermediate
IBM Introduction to Machine Learning
IBM
Programa de cursos integrados
Avaliado como 4.8 de 5 estrelas. 22 análises
1.8k Alunos
Intermediate
Inferential Statistical Analysis with Python
University of Michigan
Curso
Avaliado como 4.6 de 5 estrelas. 568 análises
20k Alunos
Intermediate
Exploratory Data Analysis for Machine Learning
IBM
Curso
Avaliado como 4.8 de 5 estrelas. 20 análises
Intermediate
Create Interactive Dashboards with Streamlit and Python
Coursera Project Network
Novos
projeto guiado
Avaliado como 4.7 de 5 estrelas. 246 análises
5.7k Alunos
Intermediate

Searches related to bayesian statistics

bayesian statistics: techniques and models

bayesian statistics: from concept to data analysis

bayesian statistics: mixture models

Em resumo, aqui estão 10 dos nossos cursos mais populares bayesian statistics

Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
Bayesian Statistics: Mixture Models: University of California, Santa Cruz
Probabilistic Graphical Models: Stanford University
Statistics with R: Duke University
Bayesian Statistics: Duke University
Bayesian Methods for Machine Learning: National Research University Higher School of Economics
Advanced Machine Learning: National Research University Higher School of Economics
Improving your statistical inferences: Eindhoven University of Technology
Statistics with Python: University of Michigan

Habilidades que você pode aprender em Probability And Statistics

Linguagem R (19)

Inferênciainferência (16)

Regressão Linear (12)

Análise Estatística (12)

Perguntas frequentes sobre 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.

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Cursos de Bayesian Statistics

Explore 100% das graduações e certificados on-line na Coursera

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Searches related to bayesian statistics

bayesian statistics: techniques and models

bayesian statistics: from concept to data analysis

bayesian statistics: mixture models

Em resumo, aqui estão 10 dos nossos cursos mais populares bayesian statistics

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Perguntas frequentes sobre Bayesian Statistics

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