OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
edX logo

Applied Bayesian for Analytics

Pulak Ghosh

Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.

Read more

Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.

This course is divided into two parts i.e. Theoretical and Empirical part of Bayesian Analytics. First three weeks cover the Theoretical part which includes how to form a prior, how to calculate a posterior and several other aspects. Rest of the weeks will cover the empirical part which explains how to compute Bayesian modelling. Completion of this course will provide you with an understanding of the Bayesian approach, the primary difference between Bayesian and Frequentist approaches and experience in data analyses.

What you'll learn

  • Understand the necessary Bayesian concepts from practical point of view for better decision making.
  • Learn Bayesian approach to estimate likely event outcomes, or probabilities using datasets.
  • Gain “hands on” experience in creating and estimating Bayesian models using R and OPENBUGS.
Enroll now

What's inside

Learning objectives

  • Understand the necessary bayesian concepts from practical point of view for better decision making.
  • Learn bayesian approach to estimate likely event outcomes, or probabilities using datasets.
  • Gain “hands on” experience in creating and estimating bayesian models using r and openbugs.

Syllabus

Week 01: What is Bayesian Statistics and How it is different than Classical Statistics
Foundations of Bayesian Inference
Bayes theorem
Advantages of Bayesian models
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Builds a strong foundation for beginners in Bayesian statistics
Provides hands-on experience with Bayesian modeling using R and OPENBUGS
Develops an understanding of the Bayesian approach to data analysis and probability estimation
Useful for learners interested in applying Bayesian statistics to real-world problems
Taught by Pulak Ghosh, who is recognized for his work in Bayesian statistics
Explored through a mix of theoretical and empirical components for a comprehensive understanding

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Applied bayesian for analytics: foundational & practical

According to learners, "Applied Bayesian for Analytics" is a largely positive course that provides a solid foundation in Bayesian concepts, distinguishing them from traditional methods. Students highly praise the instructor's ability to clarify complex topics, making an otherwise intimidating subject approachable. The course emphasizes practical application, offering invaluable hands-on practice with R and OpenBUGS assignments that reinforce learning. While many found it an excellent introduction suitable for data scientists and those transitioning into the field, one reviewer noted it might be too superficial for advanced learners with existing statistical backgrounds. Overall, it's considered a challenging but rewarding experience for those serious about analytics.
Best for beginners, potentially superficial for advanced learners.
"A fantastic introduction to Bayesian statistics. Definitely a must-take for data scientists."
"As someone transitioning into data science, this course was exactly what I needed."
"Highly recommend for intermediate learners looking to specialize."
"This course was far too superficial for anyone with a background in statistics, feeling more like a very basic introduction... Not suitable for intermediate or advanced learners."
Hands-on R and OpenBUGS assignments build practical skills.
"The hands-on practice, particularly with R and OpenBUGS, was invaluable."
"The R and OpenBUGS assignments were incredibly helpful for hands-on learning."
"The hands-on work in R was particularly useful for building practical skills."
"The practical exercises solidified my understanding."
Provides a clear, structured introduction to Bayesian concepts.
"This course provides a solid foundation, differentiating it from traditional methods..."
"Excellent course for understanding Bayesian concepts and their application. The material is well-structured..."
"A fantastic introduction to Bayesian statistics. The pace was just right, allowing time to absorb the concepts."
"It fills a significant gap in many data science curricula by providing a solid grounding in Bayesian thinking."
"It provided the foundational knowledge in Bayesian methods that is often overlooked."
Focuses on practical application and real-world implementation.
"This course provides the tools to apply them practically."
"I appreciated the practical examples and the clear explanations of how Bayesian methods are relevant in real-world analytics."
"The emphasis on practical application over purely theoretical discussions was a big plus."
"What stood out was how the course tied theoretical concepts to actual implementation using software."
"It presents Bayesian statistics not just as a theoretical construct but as a powerful tool for real-world problems."
Instructor clarifies complex concepts, making them accessible.
"The professor in this course is incredible, explaining complex concepts related to Bayesian statistics in a way that is easily understandable."
"The lecturer made the otherwise intimidating subject approachable. I appreciated the practical examples and the clear explanations..."
"The instructor's dedication to simplifying complex ideas was evident throughout."
"The instructor's pedagogical approach is top-notch, making difficult concepts accessible."
"The instructor's ability to simplify without oversimplifying is commendable."

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 Applied Bayesian for Analytics with these activities:
Review concepts from linear algebra and calculus
Revisit concepts from linear algebra and calculus to strengthen your mathematical foundation for Bayesian Statistics.
Browse courses on Linear Algebra
Show steps
  • Review notes or textbooks on linear algebra and calculus.
  • Solve practice problems to refresh your understanding.
  • Consider taking a refresher course or workshop.
Review Probability Theory
Reinforces the foundational concepts of probability theory necessary for understanding Bayesian Statistics.
Browse courses on Probability Theory
Show steps
  • Revisit the definitions and axioms of probability.
  • Review concepts such as conditional probability, independence, and Bayes theorem.
  • Solve practice problems to strengthen understanding.
Review probability theory
Refresh your understanding of probability theory to strengthen your foundation for Bayesian Statistics.
Browse courses on Probability Theory
Show steps
  • Review notes or textbooks on probability theory.
  • Practice solving probability problems.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Read Bayesian Data Analysis by Andrew Gelman et al.
Engage with one of the foundational texts on Bayesian Statistics to deepen your understanding and broaden your perspective.
View Bayesian Data Analysis (Chapman & Hall/CRC... on Amazon
Show steps
  • Obtain a copy of the book.
  • Set aside dedicated time for reading.
  • Read one chapter at a time, taking notes and highlighting important concepts.
  • Complete the exercises and problems in the book.
  • Discuss the book with peers or the course instructor.
Review Precalculus
This course builds heavily on precalculus and higher. Reviewing precalculus concepts will provide a stronger foundation.
Browse courses on Precalculus
Show steps
  • Review your class notes and materials
  • Complete practice problems
  • Take mock practice exams
Review and compile resources on Bayesian Statistics
Gather and organize relevant resources to deepen your understanding and expand your knowledge base.
Browse courses on Bayesian Statistics
Show steps
  • Search for online articles, books, and videos on Bayesian Statistics.
  • Review the resources and select those that are most informative and relevant.
  • Organize the resources into a collection or bibliography.
Discuss Bayesian concepts with peers
Engage in discussions with peers to clarify concepts, share insights, and receive feedback.
Browse courses on Bayesian Statistics
Show steps
  • Join or form a study group with other students in the course.
  • Schedule regular meetings to discuss course materials and exchange ideas.
  • Prepare for discussions by reviewing materials and preparing questions.
Practice Bayesian Inference
Strengthens understanding of Bayesian inference techniques through repeated practice.
Browse courses on Bayesian Inference
Show steps
  • Solve exercises involving Bayes theorem and posterior probability calculations.
  • Use online resources or textbooks for additional problems.
Learn R and OPENBUGS for Bayesian analysis
Follow tutorials to gain practical experience in using R and OPENBUGS for Bayesian analysis.
Browse courses on R
Show steps
  • Find tutorials on R and OPENBUGS.
  • Follow the tutorials step-by-step.
  • Complete the exercises or projects provided in the tutorials.
Discuss Bayesian Applications
Fosters collaboration and exchange of ideas on practical applications of Bayesian Statistics.
Show steps
  • Participate in online forums or discussion groups focused on Bayesian Statistics.
  • Share experiences and insights related to Bayesian modeling and analysis.
Bayesian modeling exercises
Solve Bayesian modeling exercises to solidify your understanding of the concepts and techniques.
Browse courses on Bayesian Modeling
Show steps
  • Find practice problems or exercises online or in textbooks.
  • Attempt to solve the problems independently.
  • Check your solutions against provided solutions or consult with the course instructor.
Create a Bayesian Model
Provides hands-on experience in applying Bayesian concepts by creating a custom model.
Browse courses on Bayesian Analysis
Show steps
  • Define the problem and gather necessary data.
  • Choose appropriate priors and likelihood functions.
  • Implement the model using a programming language or software.
  • Analyze the results and evaluate the model's performance.
Create a Bayesian analysis project
Apply your skills to develop a comprehensive Bayesian analysis project, demonstrating your understanding of the concepts.
Browse courses on Bayesian Statistics
Show steps
  • Identify a suitable dataset and research question.
  • Develop a Bayesian model to address the research question.
  • Implement the model in R or OPENBUGS and perform the analysis.
  • Interpret the results and draw conclusions.
  • Present the project in a report or presentation.
Explore Markov Chain Monte Carlo Methods
Deepens knowledge of Markov Chain Monte Carlo methods used in Bayesian computations.
Browse courses on Markov Chain Monte Carlo
Show steps
  • Follow online tutorials or read research papers on MCMC.
  • Implement MCMC algorithms in a programming language.
  • Analyze the convergence and efficiency of MCMC simulations.

Career center

Learners who complete Applied Bayesian for Analytics will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists are in high demand as organizations seek to gain insights from their data in order to make better decisions. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Data Scientist.
See salaries and explore the career path for Data Scientist
Statistician
Statisticians use data to solve problems and make informed decisions. They work in a variety of fields, including finance, healthcare, and marketing. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Statistician.
See salaries and explore the career path for Statistician
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve problems in business and industry. They work in a variety of fields, including manufacturing, transportation, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as an Operations Research Analyst.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They work in a variety of fields, including banking, insurance, and asset management. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. They work in a variety of fields, including insurance, pensions, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as an Actuary.
See salaries and explore the career path for Actuary
Market Researcher
Market Researchers use data to understand consumer behavior. They work in a variety of fields, including marketing, advertising, and product development. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Market Researcher.
See salaries and explore the career path for Market Researcher
Data Analyst
Data Analysts use data to solve problems and make informed decisions. They work in a variety of fields, including business, healthcare, and government. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Data Analyst.
See salaries and explore the career path for Data Analyst
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including technology, finance, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Software Engineer.
See salaries and explore the career path for Software Engineer
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to analyze financial data. They work in a variety of fields, including investment banking, hedge funds, and asset management. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Quantitative Analyst.
See salaries and explore the career path for Quantitative Analyst
Risk Analyst
Risk Analysts assess and manage risk for organizations. They work in a variety of fields, including finance, insurance, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Risk Analyst.
See salaries and explore the career path for Risk Analyst
Business Analyst
Business Analysts use data to solve problems and make informed decisions for businesses. They work in a variety of industries, including technology, finance, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Business Analyst.
See salaries and explore the career path for Business Analyst

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Applied Bayesian for Analytics.
Cover image
Bayesian Data Analysis, Third Edition
Save
An essential reference for Bayesian statistics, this book provides comprehensive coverage of Bayesian modeling and analysis techniques. It is commonly used as a textbook for graduate-level courses and serves as a valuable supplement to the course syllabus.
Bayesian Data Analysis (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$$
Cover image
Doing Bayesian Data Analysis
Save
A practical guide to Bayesian data analysis, this book provides hands-on tutorials using R, JAGS, and Stan. It is an excellent resource for gaining practical experience in Bayesian modeling and valuable supplement to the course's empirical component.
Doing Bayesian Data Analysis: A Tutorial with R,...
Hardcover
$$$
Cover image
Bayesian Hierarchical Models
Save
Provides a practical guide to Bayesian statistical modeling. It covers various topics such as model building, parameter estimation, and model comparison. It valuable resource for learners seeking a comprehensive understanding of Bayesian modeling.
Bayesian Hierarchical Models
Paperback
$$$
Bayesian Hierarchical Models: With Applications...
Kindle Edition
$$
Cover image
Statistical Rethinking
Save
Provides an accessible introduction to Bayesian statistics, with a focus on understanding the underlying concepts and practical applications. It is written in a clear and engaging style, making it a great choice for learners new to Bayesian statistics.
Statistical Rethinking
Paperback
Check
Statistical Rethinking
Kindle Edition
Check
Cover image
Bayesian Analysis with Python
Save
Provides a hands-on guide to Bayesian analysis using Python. It covers essential concepts and techniques, with a focus on practical applications. It valuable resource for learners seeking to implement Bayesian models in Python.
Bayesian Analysis with Python - Third Edition: A...
Paperback
$$
Bayesian Analysis with Python - Second Edition:...
Paperback
$$
Bayesian Analysis with Python: Click here to enter...
Kindle Edition
$$
Cover image
Monte Carlo Statistical Methods
Save
Provides a comprehensive introduction to Monte Carlo methods, a crucial aspect of Bayesian computation. It covers various techniques and algorithms, making it a valuable reference for learners seeking to understand the theoretical foundations of Bayesian modeling and sampling.
Monte Carlo Statistical Methods (Springer Texts in...
Hardcover
$$$
Cover image
Bayesian Thinking in Biostatistics
Save
Specifically tailored to the field of biostatistics, this book provides a concise introduction to Bayesian statistics. It covers essential concepts and methods, making it a suitable supplement for learners interested in applying Bayesian statistics in healthcare and medical research.
Bayesian Thinking in Biostatistics (Chapman &...
Hardcover
$$$$
Bayesian Thinking in Biostatistics (Chapman &...
Kindle Edition
$$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Applied Bayesian for Analytics.
Pre-MBA Statistics
Most relevant
Simulation Models for Decision Making
Most relevant
Financial Statement Analysis
Most relevant
Introduction to Bayesian Statistics
Most relevant
Negotiation skills: Negotiate and resolve conflict
Most relevant
Critical thinking: reasoned decision making
Most relevant
Hypotheses Testing in Econometrics
Most relevant
Operations Research (1): Models and Applications
Most relevant
Probability and Statistics: To p or not to p?
Most relevant

edX Buyer Protection & Guarantee

Verified partner policies & purchase protections

World-Class University Partner
Free Trial & Audit

Free Audit Track Available

Free audit track available for most courses, giving open access to video lectures, course readings, and assignments at no charge.

Return & Refund Policy

14-Day Full Refund Window

14-day full refund available up to 14 days after course start or 14 days after payment (whichever comes second, up to 6 months max), provided verified certificate has not yet been earned.

Buyer Protections

Verified Academic & Industry Partner

University-backed credentials authored by elite institutions like Harvard, MIT, and Oxford, with tamper-proof certificate verification.

Key Purchase Protections
Free audit track for open learning access
14-day money-back guarantee on verified certificates
Prestigious university credentials (Harvard, MIT)
Automatic unenrollment refund process via dashboard
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Effort
2 to 3 hours per week for 6 weeks
Level
Introductory
Via
edX
Institution
Indian Institute of Management Bangalore
Instructor
Pulak Ghosh
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Builds a strong foundation for beginners in Bayesian statistics
Provides hands-on experience with Bayesian modeling using R and OPENBUGS
Develops an understanding of the Bayesian approach to data analysis and probability estimation
Useful for learners interested in applying Bayesian statistics to real-world problems
Taught by Pulak Ghosh, who is recognized for his work in Bayesian statistics
Explored through a mix of theoretical and empirical components for a comprehensive understanding
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Applied Bayesian for Analytics.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser