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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.

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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.

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
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

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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
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.
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.
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.
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.
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.
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.
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.

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