Introduction to PyMC3 for Bayesian Modeling and Inference from Coursera

The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.

The instructor for this course will be Dr. Srijith Rajamohan.

What's inside

Syllabus

Introduction to PyMC3 - Part 1

This module serves as an introduction to the PyMC3 framework for probabilistic programming. It introduces some of the concepts related to modeling and the PyMC3 syntax. The visualization library ArViz, that is integrated into PyMC3, will also be introduced. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Introduction to PyMC3 - Part 2

This module will teach the basics of using PyMC3 to solve regression and classification problems using PyMC3. It will also show how to deal with outliers in your data and create hierarchical models. Finally, a case study is presented to help apply everything that was learned in Module 1 and 2. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Metrics in PyMC3

This module introduces various measures and metrics to assess the quality of the solutions inferred using PyMC3. Hands-on examples are used to illustrate how various methods and visualizations can be used in PyMC3. Finally, a brief overview of how to debug PyMC3 algorithms is provided. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Modeling of COVID-19 cases using PyMC3

This is an ungraded final project. We will utilize everything that has been learned in this course to model the disease dynamics of COVID-19 using a SIR model. Utilizing real-life data, the goal would be to infer the parameters of the SIR model for COVID-19.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Uses the Python package PyMC3 that is growing in popularity

Suitable for beginners in Bayesian Modeling

Provides hands-on exercises

Introduces the ArViz library for data visualization

Includes a final project to apply the concepts learned

Instructor is experienced in probabilistic programming

Reviews summary

Pymc3 for bayesian inference

According to students, PyMC3 is a beginner-friendly course that provides a good introduction to Bayesian modeling and inference. Learners say the materials are well organized and that the course notes are comprehensive. However, some students report that the lectures are not engaging and that the code evaluations are lacking.

Organized and comprehensive

"The course materials are actually alright"

"amazing, nice material, well explained"

Can be improved

"the teacher is reading notes"

"The lecture doesn't serve its purpose"

Could be improved

"the code is not well suited for beginners"

"Coding evaluations don't exist as promised"

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 PyMC3 for Bayesian Modeling and Inference with these activities:

Read 'Bayesian Data Analysis' by Andrew Gelman et al.

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Gain in-depth knowledge and theory behind Bayesian modeling by reading a classic text in the field, enhancing understanding and providing a solid foundation for the course.

View Bayesian Data Analysis (Chapman & Hall/CRC... on Amazon

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Obtain a copy of the book
Read and study selected chapters
Take notes and summarize key concepts

Review PyMC3 concepts

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Review core PyMC3 concepts to build a foundational understanding and reinforce knowledge, making it easier to apply in the context of this course.

Browse courses on PyMC3

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Revisit PyMC3's documentation
Rerun examples from the course website
Practice building simple Bayesian models using PyMC3

Engage in peer-led discussion and Q&A

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Foster collaborative learning by engaging with peers, clarifying concepts, and gaining diverse perspectives on Bayesian modeling.

Browse courses on Bayesian Modeling

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Join or create a study group or discussion forum
Prepare questions and share insights with peers
Discuss concepts and work through problems together

Five other activities

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Solve practice problems on Bayesian modeling

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Enhance comprehension by solving various practice problems, reinforcing concepts and strategies for Bayesian modeling.

Browse courses on Bayesian Modeling

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Work through examples from the course's online resources
Attempt problems from external sources
Discuss solutions with peers or the instructor

Follow online tutorials on PyMC3 and Bayesian modeling

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Expand knowledge by exploring additional resources and tutorials, gaining diverse perspectives and practical insights on PyMC3 and Bayesian modeling.

Browse courses on Bayesian Modeling

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Search for well-regarded online tutorials
Follow the tutorials step-by-step
Implement what you've learned in your own projects

Develop a Bayesian model of a real-world situation

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Apply skills learned in the course to create a practical model, enhancing problem-solving abilities and solidifying understanding of Bayesian modeling.

Browse courses on Bayesian Modeling

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Identify a real-world problem
Collect relevant data
Build a Bayesian model using PyMC3
Interpret the results
Write a short report summarizing the findings

Summarize and compile key concepts from the course

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Reinforce understanding by summarizing and compiling the most important concepts from the course, creating a valuable resource for review and retention.

Browse courses on Bayesian Modeling

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Review course materials and notes
Identify key concepts and summarize them concisely
Create a document or presentation compiling the summaries

Participate in a hackathon or modeling competition

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Challenge yourself, apply skills in a competitive setting, and potentially gain recognition for exceptional performance in Bayesian modeling.

Browse courses on Bayesian Modeling

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Identify relevant hackathons or competitions
Form a team or participate individually
Develop an innovative solution using PyMC3
Submit the solution and present if necessary

Career center

Learners who complete Introduction to PyMC3 for Bayesian Modeling and Inference will develop knowledge and skills that may be useful to these careers:

Statistician

A Statistician collects, analyzes, and interprets data. To be an effective Statistician, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Statistician may find this course useful because it teaches them how to model and make scalable inferences about complex data.

See salaries and explore the career path for Statistician

Biostatistician

A Biostatistician uses statistical methods to analyze biological data. To be an effective Biostatistician, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Biostatistician may find this course useful because it provides them with the tools to model and make scalable inferences from complex biological data.

See salaries and explore the career path for Biostatistician

Data Analyst

A Data Analyst uses data to solve business problems. To be an effective Data Analyst, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Data Analyst may find this course useful because it provides them with the tools to model and make scalable inferences from complex data.

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Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This requires a strong understanding of algorithms, data structures, and software engineering. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Machine Learning Engineer may find this course useful because it provides a foundation for modeling and making scalable inferences for data.

See salaries and explore the career path for Machine Learning Engineer

Operations Research Analyst

An Operations Research Analyst uses analytical methods to solve business problems. To be an effective Operations Research Analyst, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become an Operations Research Analyst may find this course useful because it provides them with the tools to model and make scalable inferences for optimizing complex systems.

See salaries and explore the career path for Operations Research Analyst

Epidemiologist

An Epidemiologist studies the causes and spread of disease. To be an effective Epidemiologist, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become an Epidemiologist may find this course useful because it provides them with the tools to model and make scalable inferences about disease dynamics.

See salaries and explore the career path for Epidemiologist

Data Scientist

A Data Scientist uses scientific methods to analyze data to extract knowledge. To be an effective Data Scientist, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Data Scientist may find this course useful because it teaches them how to perform scalable inference for a variety of problems.

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Risk Manager

A Risk Manager identifies, assesses, and manages risk. To be an effective Risk Manager, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Risk Manager may find this course useful because it provides them with the tools to model and make scalable inferences for risk assessment and management.

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Economist

An Economist analyzes economic data and trends to make predictions about the economy. To be an effective Economist, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become an Economist may find this course useful because it provides them with the tools to model and make scalable inferences from economic data.

See salaries and explore the career path for Economist

Insurance Analyst

An Insurance Analyst analyzes insurance data to assess risk and design insurance policies. To be an effective Insurance Analyst, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become an Insurance Analyst may find this course useful because it provides them with the tools to model and make scalable inferences for insurance risk.

See salaries and explore the career path for Insurance Analyst

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to evaluate financial risk and make investment decisions. To be an effective Quantitative Analyst, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become a Quantitative Analyst may find this course useful because it provides them with the tools to model and make scalable inferences about financial data.

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Actuary

An Actuary uses mathematics and statistics to assess risk and design insurance policies. To be an effective Actuary, one needs a strong foundation in statistics, probability, and modeling. This course introduces PyMC3, a probabilistic programming framework. One who wishes to become an Actuary may find this course useful because it teaches them how to model and make scalable inferences about insurance risk.

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Financial Analyst

A Financial Analyst provides advice on investments and financial planning. To be an effective Financial Analyst, one needs a strong understanding of financial markets and instruments. This course does not directly teach about financial markets or instruments. However, it does teach the fundamentals of modeling and making scalable inferences. This may be useful for understanding the dynamics of financial markets and making informed investment decisions.

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Data Architect

A Data Architect designs and manages data systems. This course does not directly teach data architecture. However, it does provide a strong foundation in probabilistic programming, which can be useful for tasks such as designing data pipelines and managing complex data systems.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course does not directly teach software engineering. However, it does introduce the basics of probabilistic programming, which can be useful for tasks such as modeling and making inferences from complex data.

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