- 浏览
- Bayesian Statistics

- Excel to MySQL: Analytic Techniques for Business: Duke University
- Natural Language Processing with Classification and Vector Spaces: DeepLearning.AI
- Data Analysis with R: Duke University
- Introduction to Discrete Mathematics for Computer Science: University of California San Diego
- Statistics with Python: University of Michigan
- Advanced Data Science with IBM: IBM Skills Network
- Statistical Analysis with R for Public Health: Imperial College London
- Six Sigma Green Belt: University System of Georgia
- Sports Performance Analytics: University of Michigan
- Machine Learning: University of Washington

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。

- Google 数据分析师
- Google Digital Marketing & E-commerce Professional Certificate
- Google IT Automation with Python Professional Certificate
- Google IT 支持
- Google 项目管理
- Google UX 设计
- 备考 Google Cloud 认证：云架构师
- IBM Cybersecurity Analyst
- IBM 数据分析师
- IBM 数据工程
- IBM 数据科学
- IBM 全栈云开发人员
- IBM Machine Learning
- 直觉簿记
- Meta Front-End Developer
- DeepLearning.AI TensorFlow Developer Professional Certificate
- SAS Programmer Professional Certificate
- 开启您的职业生涯
- 准备证书
- 开拓职业生涯
- How to Identify Python Syntax Errors
- How to Catch Python Exceptions
- See all Programming Tutorials

- 免费课程
- Artificial Intelligence Courses
- Blockchain Courses
- Computer Science Courses
- Cursos Gratis
- Cybersecurity Courses
- Data Analysis Courses
- Data Science Courses
- English Speaking Courses
- Full Stack Web Development Courses
- Google Courses
- Human Resources Courses
- IT Courses
- Learning English Courses
- Microsoft Excel Courses
- Product Management Courses
- Project Management Courses
- Python Courses
- SQL Courses
- Agile Certifications
- CAPM Certification
- CompTIA A+ Certification
- Data Analytics Certifications
- Scrum Master Certifications
- 查看全部课程

- Free online courses you can finish in a day
- Popular Free Courses
- Business Jobs
- Cybersecurity Jobs
- Entry-Level IT Jobs
- Data Analyst Interview Questions
- Data Analytics Projects
- How to Become a Data Analyst
- How to Become a Project Manager
- IT Skills
- Project Manager Interview Questions
- Python 程序设计技能
- Strength and Weakness in Interview
- What Does a Data Analyst Do
- What Does a Software Engineer Do
- What Is a Data Engineer
- What Is a Data Scientist
- What Is a Product Designer
- What Is a Scrum Master
- What Is a UX Researcher
- How to Get a PMP Certification
- PMI Certifications
- 热门网络安全证书
- 热门 SQL 证书
- Read all Coursera Articles

- Google Professional Certificates
- 专业证书
- 查看所有证书
- 学士学位
- 硕士学位
- Computer Science Degrees
- Data Science Degrees
- MBA 和商学学位
- 数据分析师学位
- 公共卫生学位
- 社会科学学位
- 管理学位
- BA vs BS Degree
- 什么是学士学位？
- 11 Good Study Habits to Develop
- How to Write a Letter of Recommendation
- 10 In-Demand Jobs You Can Get with a Business Degree
- Is a Master's in Computer Science Worth it?
- See all degree programs
- Coursera India
- Coursera UK
- Coursera Mexico