- All DegreesExplore Bachelor’s & Master’s degrees
- Computer Science & EngineeringExplore Computer Science & Engineering degrees
- BusinessExplore MBA & Business degrees
- Bachelor’s DegreesExplore master’s degrees from leading universities
- MasterTrack™Earn credit towards a Master’s degree
- University CertificatesAdvance your career with graduate-level learning

- Browse
- Bayesian Statistics

- Study Designs in Epidemiology: Imperial College London
- Machine Learning in the Enterprise: Google Cloud
- Moneyball and Beyond: University of Michigan
- Data Science for Business Innovation: EIT Digital
- Empowering Yourself in a Post-Truth World: The State University of New York
- Logistic Regression in R for Public Health: Imperial College London
- Introduction to PyMC3 for Bayesian Modeling and Inference: Databricks
- Usar fórmulas y funciones básicas en Microsoft Excel: Coursera Project Network
- Razonamiento artificial: Universidad Nacional Autónoma de México
- Mathematical Biostatistics Boot Camp 2: Johns Hopkins University

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.

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Other topics to explore