Introduction to Bayesian Statistics Using R from edX

Basics of Bayesian Data Analysis Using R is part one of the Bayesian Data Analysis in R professional certificate.

Bayesian approach is becoming increasingly popular in all fields of data analysis, including but not limited to epidemiology, ecology, economics, and political sciences. It also plays an increasingly important role in data mining and deep learning. Let this course be your first step into Bayesian statistics.

Here, you will find a practical introduction to applied Bayesian data analysis with the emphasis on formulating and answering real life questions. You will learn how to combine the data generating mechanism, likelihood, with prior distribution using Bayes’ Theorem to produce the posterior distribution. You will investigate the underlying theory and fundamental concepts by way of simple and clear practical examples, including a case of linear regression.

You will be introduced to the Gibbs sampler – the simplest version of the powerful Markov Chain Monte Carlo (MCMC) algorithm. And you will see how the popular R-software can be used in this context, and encounter some Bayesian R packages .

A facility in basic algebra and calculus as well as programming in R is recommended.

What you'll learn

• Bayes’ Theorem. Differences between classical (frequentist) and Bayesian inference.

• Posterior inference: summarizing posterior distributions, credible intervals, posterior probabilities, posterior predictive distributions and data visualisation.

• Gamma-poisson, beta-binomial and normal conjugate models for data analysis.

• Bayesian regression analysis and analysis of variance (ANOVA).

• Use of simulations for posterior inference. Simple applications of Markov chain-Monte Carlo (MCMC) methods and their implementation in R.

• Bayesian cluster analysis.

• Model diagnostics and comparison.

• Ensuring you answer the actual research question rather than “apply methods to the data”

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Enhance the understanding of data analysis techniques such as Bayes’ Theorem, posterior inference, and Bayesian regression

Suitable for learners with a basic understanding of algebra, calculus, and R programming

Emphasizes on practical applications of Bayesian data analysis, making it relevant to real-world scenarios

Includes real-life examples to illustrate the principles and concepts of Bayesian statistics

Employs the Gibbs sampler as an accessible method for posterior inference, promoting hands-on learning

Requires existing knowledge of Bayesian statistics, which may limit accessibility to complete novices

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Bayesian Statistics Using R with these activities:

Refresh Probability and Statistics

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Reviewing foundational concepts ensures a strong basis for understanding advanced Bayesian methods.

Browse courses on Probability

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Review notes or textbooks on basic probability concepts.
Go through a statistics refresher course or tutorial.
Take practice quizzes to test understanding.

Seek Guidance from a Bayesian Expert

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Mentorship provides personalized guidance, accelerates learning, and expands professional connections.

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Identify potential mentors in the field of Bayesian statistics.
Reach out to them via email or LinkedIn, expressing interest in their work and seeking mentorship.
Meet with your mentor regularly to discuss progress, challenges, and career advice.

Repetition to Reinforce Understanding

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Practice problems reinforce Bayesian inference techniques and solidify understanding of statistical concepts.

Browse courses on Bayes' Theorem

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Solve 10 practice problems on Bayes' Theorem.
Work through 5 examples of posterior inference.
Complete 3 exercises on Bayesian regression analysis.

Six other activities

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Collaboration and Knowledge Sharing

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Engaging with peers provides diverse perspectives and opportunities to clarify concepts.

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Join a study group of 3-5 classmates.
Meet once a week to discuss course material, share insights, and work on assignments together.
Facilitate one session by presenting on a chosen topic.

Bayesian Modeling with Stan

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Additional tutorials complement course materials and provide hands-on experience with Bayesian modeling tools.

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Complete the Stan tutorial.
Replicate one of the examples presented in the course using Stan.
Use Stan to build a Bayesian model for a personal project.

Bayesian Explained in Your Own Words

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Articulating concepts in one's own words helps deepen understanding and identify areas that need clarification.

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Choose a Bayesian concept covered in the course.
Write a 500-word explanation of the concept.
Record yourself explaining the concept in a 5-minute video.

Advanced Bayesian Modeling for Beginners

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Expanding knowledge beyond the course material strengthens the foundations of Bayesian statistics.

View Introduction to Statistical Methods for... on Amazon

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Read chapters 1-5 of the book.
Work through the examples and exercises at the end of each chapter.
Summarize key concepts and their implications for Bayesian data analysis.

Bayesian Applications in Practice

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Real-world experiences solidify learning and demonstrate the practical value of Bayesian methods.

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Identify a community organization or research project that uses Bayesian analysis.
Inquire about volunteer opportunities.
Assist with data collection, analysis, or interpretation under the guidance of a mentor.

Bayesian Analysis Portfolio

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A personal project demonstrates the practical application of Bayesian methods and serves as a valuable portfolio piece.

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Identify a research question or problem that can be addressed using Bayesian analysis.
Collect or curate data relevant to the question.
Build a Bayesian model to analyze the data and answer the research question.
Write a report summarizing the project, including data analysis, results, and implications.
Present the project to peers or mentors.

Career center

Learners who complete Introduction to Bayesian Statistics Using R will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect, analyze, and interpret data to provide insights and inform decision-making. The Introduction to Bayesian Statistics Using R provides a solid foundation in Bayesian data analysis, a powerful approach for making inferences and predictions from data. This course helps develop skills in statistical modeling and analysis, which are essential for Statisticians.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists analyze data using advanced techniques to uncover trends and patterns. They interpret and present findings to assist businesses in making informed decisions. The Introduction to Bayesian Statistics Using R lays the foundation for understanding Bayesian data analysis, a powerful technique for making inferences and predictions from data. This course helps develop skills in formulating and answering real-world questions using Bayesian methods, which are becoming increasingly popular in data science.

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Financial Analyst

Financial Analysts use statistical and economic models to evaluate investments and make recommendations. The Introduction to Bayesian Statistics Using R provides a solid understanding of Bayesian inference, which is a powerful approach for updating beliefs in light of new evidence. This course builds a foundation in Bayesian methods, enabling Financial Analysts to make more informed and accurate investment decisions.

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Biostatistician

Biostatisticians apply statistical methods to solve biomedical problems. The Introduction to Bayesian Statistics Using R provides a solid foundation in Bayesian data analysis, a powerful approach for making inferences and predictions from data. This course builds a foundation in Bayesian methods, enabling Biostatisticians to make more informed decisions and gain a deeper understanding of biomedical data.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Introduction to Bayesian Statistics Using R provides a solid understanding of Bayesian inference, a powerful approach for updating beliefs in light of new evidence. This course builds a foundation in Bayesian methods, enabling Quantitative Analysts to make more informed and accurate investment decisions.

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Market Researcher

Market Researchers gather and analyze data to understand consumer behavior and market trends. The Introduction to Bayesian Statistics Using R introduces Bayesian data analysis, a powerful technique for drawing inferences from data. This course provides a foundation in Bayesian methods, enabling Market Researchers to make more informed decisions and gain a deeper understanding of market dynamics.

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Data Analyst

Data Analysts collect, clean, and analyze data to extract meaningful insights and trends. The Introduction to Bayesian Statistics Using R introduces Bayesian data analysis, a powerful technique for drawing inferences from data. This course provides a foundation in Bayesian methods, enabling Data Analysts to make more informed decisions and gain a deeper understanding of data.

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Actuary

Actuaries use mathematical and statistical techniques to assess and manage risk. The Introduction to Bayesian Statistics Using R provides a foundation in Bayesian data analysis, a powerful tool for making decisions under uncertainty. This course helps develop skills in modeling and analyzing complex systems, which is essential for Actuaries.

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Epidemiologist

Epidemiologists investigate the causes and patterns of disease and injury. The Introduction to Bayesian Statistics Using R provides a foundation in Bayesian data analysis, a powerful tool for making decisions under uncertainty. This course helps develop skills in modeling and analyzing complex systems, which is essential for Epidemiologists.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to optimize processes and systems. The Introduction to Bayesian Statistics Using R provides a foundation in Bayesian data analysis, which is a powerful tool for making decisions under uncertainty. This course helps develop skills in modeling and analyzing complex systems, which is essential for Operations Research Analysts.

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Business Analyst

Business Analysts use data and analytical techniques to solve business problems and improve decision-making. The Introduction to Bayesian Statistics Using R provides a solid foundation in Bayesian data analysis, a powerful approach for making inferences and predictions from data. This course builds a foundation in Bayesian methods, enabling Business Analysts to make more informed decisions and gain a deeper understanding of business dynamics.

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Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models to solve complex problems. The Introduction to Bayesian Statistics Using R provides a foundation in Bayesian data analysis, a powerful approach for making inferences and predictions from data. This course helps develop skills in modeling and analyzing complex systems, which is essential for Machine Learning Engineers.

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Risk Analyst

Risk Analysts assess and manage risks to organizations. The Introduction to Bayesian Statistics Using R provides a foundation in Bayesian data analysis, which is a powerful tool for making decisions under uncertainty. This course helps develop skills in modeling and analyzing complex systems, which is essential for Risk Analysts.

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Data Engineer

Data Engineers build and maintain data infrastructure and pipelines to support data analysis and machine learning. The Introduction to Bayesian Statistics Using R introduces Bayesian data analysis, a powerful technique for drawing inferences from data. This course provides a foundation in Bayesian methods, enabling Data Engineers to make more informed decisions and build more robust and reliable data infrastructure.

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Software Engineer

Software Engineers design, develop, and maintain software applications. The Introduction to Bayesian Statistics Using R introduces Bayesian data analysis, a powerful technique for drawing inferences from data. This course provides a foundation in Bayesian methods, enabling Software Engineers to make more informed decisions and build more robust and reliable software applications.

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Introduction to Bayesian Statistics Using R

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