Applied Bayesian for Analytics from edX

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.

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.

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

Why Bayesian approach is so important in Analytics

Major densities and their applications

Week 02: Bayesian analysis of Simple Models

Likelihood theory and Estimation

Parametrizations and priors

Learning from binary models

Learning from Normal Distribution

Week 03: Monte Carlo Methods

Basics of Monte carol integration

Basics of Markov chain Monte Carlo

Gibs Sampling

Week 04: Computational Bayes

Examples of Bayesian Analytics

Introduction to R and OPENBUGS for Bayesian analysis

Week 05: Bayesian Linear Models

Context for Bayesian Regression Models

Normal Linear regression

Logistic regression

Week 06: Bayesian Hierarchical Models

Introduction to Multilevel models

Exchangeability

Computation in Hierarchical Models

Good to know

Know what's good

, what to watch for

, 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

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

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Revisit concepts from linear algebra and calculus to strengthen your mathematical foundation for Bayesian Statistics.

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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

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Reinforces the foundational concepts of probability theory necessary for understanding Bayesian Statistics.

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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

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Refresh your understanding of probability theory to strengthen your foundation for Bayesian Statistics.

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Review notes or textbooks on probability theory.
Practice solving probability problems.

11 other activities

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Read Bayesian Data Analysis by Andrew Gelman et al.

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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

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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

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This course builds heavily on precalculus and higher. Reviewing precalculus concepts will provide a stronger foundation.

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Review your class notes and materials
Complete practice problems
Take mock practice exams

Review and compile resources on Bayesian Statistics

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Gather and organize relevant resources to deepen your understanding and expand your knowledge base.

Browse courses on Bayesian Statistics

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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

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Engage in discussions with peers to clarify concepts, share insights, and receive feedback.

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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

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Strengthens understanding of Bayesian inference techniques through repeated practice.

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Solve exercises involving Bayes theorem and posterior probability calculations.
Use online resources or textbooks for additional problems.

Learn R and OPENBUGS for Bayesian analysis

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Follow tutorials to gain practical experience in using R and OPENBUGS for Bayesian analysis.

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Find tutorials on R and OPENBUGS.
Follow the tutorials step-by-step.
Complete the exercises or projects provided in the tutorials.

Discuss Bayesian Applications

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Fosters collaboration and exchange of ideas on practical applications of Bayesian Statistics.

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Participate in online forums or discussion groups focused on Bayesian Statistics.
Share experiences and insights related to Bayesian modeling and analysis.

Bayesian modeling exercises

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Solve Bayesian modeling exercises to solidify your understanding of the concepts and techniques.

Browse courses on Bayesian Modeling

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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

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Provides hands-on experience in applying Bayesian concepts by creating a custom model.

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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

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Apply your skills to develop a comprehensive Bayesian analysis project, demonstrating your understanding of the concepts.

Browse courses on Bayesian Statistics

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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

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Deepens knowledge of Markov Chain Monte Carlo methods used in Bayesian computations.

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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

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.

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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.

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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.

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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

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

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

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.

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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.

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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.

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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