# 6 Best + Free Bayesian Statistics Courses & Classes [2021 AUGUST]

Some statistical problems can only be solved with probability, and Bayesian Statistics is the best approach to apply probability to statistical issues. It gives you access to various mathematical tools that enable you to see new data or evidence about random events. If you’re involved in any role that requires solving complex problems, it is crucial to know Bayesian Statistics. Here, we help individuals gain essential skills in Bayesian Statistics by offering useful resources. After thorough research, our global experts have gathered a list of some of the Best Bayesian Statistics Courses, Tutorials, Training Programs, Classes, and Certification programs available online for 2021. The list covers both free and paid courses offered by some of the best institutions and e-learning platforms. You may like to have a look at our list of Best Statistics Courses.

## 6 Best + Free Bayesian Statistics Courses & Classes [2021 AUGUST]

### 1. Bayesian Statistics: From Concept to Data Analysis by the University of California Santa Cruz (Coursera)

Coursera offers a complete package of the Bayesian Statistics course that begins with the basics of accountability and portability and then takes you through data analysis. In this course, you will learn all the concepts of data analysis and portability, uncertainty, Frequentist approach, and Bayesian approach. Besides, you will also learn about the Bayesian approach’s philosophies and its benefits with real-world applications. These concepts will help to understand every major and minor aspect of data analysis used to implement Bayesian statistics in accounting. The course offers a combined learning experience with video lectures, computer demonstrations, assignments, and reading exercises that enable you to reach perfection at the end of the course.

Key USPs –

– An overview of the basic concepts included in the Bayesian statistical approach used to analyze data in accounting and mathematical development.

– Learn and understand the concepts of portability of data for different statistical purposes while having a more intuitive understanding.

– Understanding and adapt the course materials to practice mathematical approaches in Bayesian statistics and the Frequentist approach.

– Experiencing the working of Bayesian Statistics approach along with the accounting data used to manipulate mathematical distributions.

Duration: 12 Hours

Rating: 4.6 out of 5

Review: Good intro to Bayesian Statistics. Covers the basic concepts. The workload is reasonable, and quizzes/exercises are helpful. Could include more exercises and additional background/future reading materials. – Georgi S.

### 2. Bayesian Statistics: Techniques and Models by University of California Santa Cruz (Coursera)

This is another practical course available on Coursera that elaborates on the concepts of Bayesian statistics. It is a level up to the previous course on Bayesian statistics: From concepts to data analysis. Taking up this curriculum will introduce you to the concepts of Markov chain Monte Carlo (MCMC) methods along with the posterior distributions. Moreover, you will get the experience of using open-source and free software applications like R and JAGS to learn the utilization of these methods. The course instructor, Mathew Heiner, is a professional trainer studying at the University of California, who will offer expert assistance throughout the classes with his years of experience. Check out our compilation of Best Multivariable Calculus Courses.

Key USPs –

– Learning the concepts of statistical modeling, Bayesian modeling, Monte Carlo estimation methods, and other approaches required to solve complex problems.

– Learn how to utilize and implement different statistical methods with varying concepts like linear aggression and logistic regression.

– Get hands-on experience in open-source application software to understand the working of statistical modeling techniques.

– Practical revision and exercises through computer demonstrations that offer a unique experience and analytical walkthroughs.

Duration: 30 Hours

Rating: 4.8 out of 5

Review: The second course of the great series. The knowledge and skills gained in this course allow us to actually do statistical analysis on scientific data. The course is very clear, systematic, and well presented. Thank you! – Georgy M.

### 3. Bayesian Statistics by Duke University (Coursera)

If you want to get deeper into the learning of Bayesian statistics, this course provides core insights into parameters and hypotheses. It elaborates on Bayes’ rule’s core concepts that can help transform prior probabilities into posterior probabilities. Students will also learn the utilization of paradigms included in the Bayesian statistical modeling. The team of professional instructors will also help to utilize the open-source software R for implementing posterior distribution. By the end of this course, you will gain practical knowledge of different statistical models related to Bayesian predictions and regression.

Key USPs –

– An overview of the specialization and the course, including prerequisites, basic knowledge, and future scope on an industrial level.

– An introduction and learning of basics in Bayesian statistics that helps in the working of conditional probabilities and prior decisions.

– Understand the core concepts of the Bayesian paradigm and discover the different methods to implement statistical models.

– Introduction and learning of multiple models in Bayesian inference, regression, comparisons of means and proportions, along with Bayesian prediction.

Duration: 35 Hours

Rating: 3.8 out of 5

Review: A very good introduction to Bayesian Statistics. Very interactive with Labs in Rmarkdown. Definitely requires thinking, and a good math/analytic background is helpful. – Wesley E.

### 4. Bayesian Statistics: Mixture Models by University of California Santa Cruz (Coursera)

This is another excellent course from Coursera that elaborates on the mixture models Bayesian Statistics. It includes the learning of every statistical model used to manipulate and analyze data while implementing them effectively. The instructor of the course, Abel Rodriguez, will provide you with core insights about Bayesian Statistics via different learning resources like presentations, discussions, and hands-on assignments. Moreover, students will get the opportunity to improve their statistical skills using several modeling methods using calculus-based probability, estimation models, and data analysis concepts. The course is perfect to succeed as a professional mathematical data analyst in the industry and stabilize your career effectively. Have a look at our curation of Best Geometry Courses.

Key USPs –

– Learn and understand the basic elements of Bayesian Statistics models, including regression, estimation, and probability models.

– Get introduced to credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference with multiple models, and Bayesian prediction.

– Learn how to utilize and implement the maximum likelihood estimation for mixture models along with their benefits.

– Be able to apply Bayesian methods to several practical problems to show end-to-end Bayesian analysis that moves from framing the question to building models.

– Hands-on experience with live discussions, video conferencing, short quizzes, and peer-reviewed assignments for qualitative revisions.

Duration: 21 Hours

Rating: 4.8 out of 5

### 5. Bayesian Statistics Courses (Udemy)

Udemy is a well-known e-learning platform for professionals as well as students, offering a variety of courses. It provides individuals with a comprehensive list of Bayesian Statistics courses and tutorials. One of the fundamental programs in the list is Bayesian Statistics, which includes basic statistical modeling, Monte Carlo methods, probabilistic programming, and a lot more. There are several professional tutors enrolled to provide industry-based expertise along with hands-on experience of the open-source software applications. Moreover, students will get to work on various live projects and assignments to know the utilization of Bayesian statistical concepts and different modeling methods.

Key USPs –

– Get access to some of the best Bayesian Statistics courses that focus on various concepts like Machine Learning, Computational Analysis, Programming with Python, etc.

– Learn how to improve A/B testing performance with adaptive algorithms while understanding the difference between Bayesian and Frequentist statistics.

– Get introduced to all the essential courses related to Bayesian statistics and mathematical modeling techniques used in the concepts of data analysis.

– Hands-on experience with live projects on every major and minor course used to train the students and boost their computational programming skills.

Duration:  Hours

Rating:  4.5 out of 5

### 6. Bayesian Statistics (Duke Online)

Created by experienced instructors of Duke University, this professional course in the specialization of Bayesian Statistics will provide you with an overview of parameters and hypotheses. In this program, you will learn to utilize Bayes’ rule that can help to convert prior probabilities into posterior probabilities. Besides, the course offers end-to-end Bayesian analyses that will help you learn how to implement qualitative models. It is ideal for beginners, intermediate learners, and professionals who want to boost their mathematical modeling skills. Students will get practical revision materials, on-spot assignments, and recorded live sessions from the experts at the end of the course. Don’t forget to check our list of Best Logic Courses.

Key USPs –

– Learn and understand Bayesian statistics along with the core concepts and modeling methods used in their implementation.

– Understand how to utilize different statistical models and implement them under various proportions to solve complex problems.

– Learn to utilize Bayesian estimation models along with the practical optimization of statistics used to analyze data.

– Be able to use Bayes’ rule to transform prior probabilities into posterior probabilities while learning the underlying theory and evaluation of the Bayesian paradigm.

– Professional certification and experience from Duke University in Bayesian Statistics along with live discussions on core concepts.

Duration: Variable

Rating: 4.4 out of 5