Summer Sale! Enroll today and start becoming a bayezian!

What You'll Explore

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Discover the world of uncertainty and variation in sports analytics

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Learn about probability, random variables, and distributions in a sports context

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Master the art of simulating distributions and building models using Stan

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Apply Bayesian methods to real-world sports scenarios

Embrace Bayesian Analysis with Sports Insights.

Enroll today to become a BayeZian and transform your data analysis skills!

“Scott's approach to teaching Bayesian statistics is a masterclass in clarity and depth. With his unique ability to blend statistical modeling with real-world applications like sports analytics, he unravels the complexities of RStan coding and Bayesian modeling. From understanding the intricacies of physics phenomena to delivering captivating narratives through detailed visualizations, Scott is the ideal guide to jumpstart your journey into Bayesian modeling. ”

Michael S. Czahor, PhD

President @ Athlyticz

Embrace Bayesian Analysis with Sports Insights.

Enroll today to become a BayeZian and transform your data analysis skills!

becoming a bayezian course

Course Information

  • AthlyticZ Pro Series, an Immersive, masterclass style coding framework

  • 80 Videos (20 Modules) of engaging content

  • Asynchronous, self-paced learning

  • Pre-recorded video lessons for flexible scheduling

  • Interactive Features: Quizzes throughout the course to reinforce learning

  • Supplementary Resources: Access to downloadable slides, readings, and additional materials

  • Hands-On Projects: Dive into a detailed case study on predicting expected goals with a detailed soccer dataset

  • Virtual Machines: Immerse yourself in our customized IDE solutions for real world programming experience.

$1499 usd

Summer Sale

$1199 usd

Your Bayesian Analysis Coach for Becoming a BayeZian

My passion lies in leveraging data for good causes and exploring the intricate dynamics of professional sports through statistical modeling.

Scott spencer

Dive into the world of Bayesian analysis and cutting-edge probabilistic programming alongside a Columbia Professor and Stan language collaborator with expertise in crafting intricate generative models. Scott's expertise spans from decoding human behavior to forecasting sea-level rise impacts on coastal property values, all while dissecting the statistical DNA of professional sports.

Scott's influence extends beyond academia, shaping decisions for tech giants like Amazon, healthcare leaders such as Johnson & Johnson, and entertainment moguls like Vevo. His knack for transparent storytelling through R packages ensures that even the most complex insights are accessible and actionable, making him a sought-after guide for data enthusiasts across industries.

Join the BayeZian revolution and unlock the true potential of Bayesian methods with Scott, where every analysis tells a compelling story and uncertainty is the key to innovation!"

What You'll Learn

Introduction Bayesian Analysis for Sports

  • Unleash Advanced Bayesian Analysis Skills

  • Embark on a transformative journey where you'll delve into essential techniques and concepts crucial for mastering Bayesian analysis in sports.

Exploring Uncertainty and Variation

  • Refining Understanding of Uncertainty and Variation

  • Delve into the depths of uncertainty and variation analysis, applying Bayesian principles to sports analytics. Learn efficient methods to model and interpret uncertainty, enhancing your analytical insights.

Introducing Probability Random & Random Variables

  • Elevating Probability and Random Variable Analysis

  • Immerse yourself in the world of probability and random variables within a Bayesian framework. Explore discrete and continuous distributions, mastering techniques to model data variability effectively.

Priors, Likelihoods, and Posteriors

  • Empowering Priors, Likelihoods, and Posteriors

  • Take command of Bayesian inference principles, understanding the role of priors, likelihoods, and posteriors in probabilistic modeling. Craft custom functions and distributions for advanced data analysis.

Simulating Distributions in R

  • Uncover Advanced Simulation Techniques

  • Learn to set up simulations, transform random numbers into distributions, and simulate complex processes using Bayesian methods in R. Gain practical skills to model real-world scenarios effectively.

Posterior Simulation and Approximations

  • Master Posterior Simulation and Approximation

  • Explore grid approximation and approximate posteriors using Metropolis Hastings (MH) and Hamiltonian Monte Carlo (HMC) methods. Gain insights into efficiently approximating complex posterior distributions.

A Language for Describing Models

  • Develop a Language for Describing Bayesian Models

  • Learn the art of describing complex models using a Bayesian framework. Understand model specification, priors, likelihoods, and posterior distributions to build robust and interpretable models.

Bayesian Regression and Generalized Linear Models

  • Dive into Bayesian Regression and GLMs

  • Explore Bayesian regression techniques, including simple normal regression and extending models with categorical predictors. Understand the concept of link functions in generalized linear models (GLMs) for advanced analysis.

Case Study: Bayesian Analysis in Sports

  • Apply Bayesian Analysis in Sports Analytics

  • Engage in a practical case study focusing on Bayesian analysis in sports. Explore modeling goals, spatial-temporal impact analysis, and hierarchical modeling to derive actionable insights.

Start your journey towards mastering Bayesian analysis with Becoming a BayeZian

Join us today to access comprehensive course materials and elevate your data analysis skillset through Bayesian modeling.

Join the becoming a bayezian course

Are you ready to embark on a transformative journey into the realm of Bayesian analysis? Enroll in the Becoming a BayeZian course today and unlock the power of probabilistic programming in data analytics!

Course Overview

Immerse yourself in our meticulously designed course curriculum, tailored to equip you with the essential techniques and concepts needed to master Bayesian analysis and enhance your statistical modeling and inference skills.

PART 1 Unleashing Bayesian Fundamentals

Highlights included:

Introduction to Bayesian Analysis

Explore the foundational principles of Bayesian analysis and its significance in probabilistic programming.

Understanding Uncertainty and Variation

Dive deep into the concepts of uncertainty and variation in Bayesian inference with real-world examples.

Introducing Probability and Random Variables

Learn about probability distributions, random variables, and their role in Bayesian modeling.

Getting to Bayes Rule

Understand Bayes' theorem and its application in updating beliefs based on new evidence.

PART 2 Model Structures and Simulation in Stan

Highlights included:

Priors, Likelihoods, and Posteriors

Explore the foundational principles of Bayesian model structures, normalizing constants, and conjugate priors.

Simulating Distributions in R

Begin simulating distributions in R, transform random numbers to distributions, and review discrete distributions.

Random Variable Code Objects

Learn to represent distributions with a random variable code object.

Simulation and Models in Stan

Review the Stan documentation, start up with a toy Stan example, and try out another beta binomial example in Stan.

PART 3Approximation Methods, Regression, and cmdstanr

Highlights included:

Posterior Simulation

Practice with a grid approximation example.

Approximating Posteriors

Start approximating posteriors with Metropolis-Hastings and get introduced to Hamiltonian Monte Carlo.

Simple Normal Regression + Extending Normal Regression

Code and fit a simple normal regression model, followed by checking HMC diagnostics and reviewing model parameters.

cmdstanr Model Object, Helper Functions, and Model Evaluation

Learn about three (3) approaches to posterior predictive checks.

PART 4 Extending Normal Regression, GLMs, and Full Soccer Case study

Highlights included:

Extending Normal Regression

Learn about using categorical and multiple predictors.

Generalized Linear Models (GLMs), a Conceptual Introduction

Begin using GLMs with logit and log-link functions.

Diving deeper into GLMs

Learn how to handle count and categorical outcomes.

Hierarchical Modeling and Workflow

Get introduced to hierarchical models, learn how parameters can share information, diagnose and reparameterize your model, and recap the full model workflow before diving into a course case study.

Expected Goals Case Study Part I

Get introduced to the case study pitch data, model goals as a Bernoulli, and add interesting features to the model.

Expected Goals Case Study Part II

Enhance the model by modeling correlation between predictors, add hierarchical information to the model, reparameterize, and use the model estimates for decision making.

How We Conduct Our Course

At AthlyticZ, we've designed our course structure to empower your learning journey at your own pace, ensuring flexibility and accessibility.

Prerecorded Video Lessons

Delve into our comprehensive course content with prerecorded video lessons, allowing you to learn at your convenience and revisit concepts as needed.

Assignments and Exercises

Engage with assignments and exercises that reinforce your understanding of key concepts and help you build practical skills.

Continuous Assessment

Benefit from quizzes and assessments integrated throughout the course, providing you with ongoing feedback on your progress and understanding.

Hands-On Projects

Apply your knowledge to real-world projects, including both minor and major assignments, where you'll develop comprehensive models using Stan.

Supplementary Resources

Access a wealth of supplementary resources such as readings, downloadable slides, and personalized tutorials tailored to your interests, enriching your learning experience.

Interactive Features

Utilize interactive features within each lesson, including note-taking capabilities and custom-built IDEs with preloaded code, ensuring an immersive and engaging learning environment.

Dive into advanced Bayesian analysis skills with Becoming a BayeZian

Start your journey today to access our comprehensive course materials and elevate your data analysis prowess through practical examples.

"Transformative Learning Experience"

"AthlyticZ has completely transformed the learning approach to data science through the use of sports-based problems. The course structure is intuitive, the content is comprehensive, the instructors are the best of the best, and the practical projects have immediate impact to students."

BILL GEIVETT M.ED.

PRESIDENT @ IMA TEAM

AUTHOR: DO YOU WANT TO WORK IN BASEBALL?

SR. VP: COLORADO ROCKIES 2001-2014

ASST. GM: LOS ANGELES DODGERS 1998-2000

Frequently Asked Questions

Can I expense this with my employer?

Unlock the transformative potential of Bayesian analysis with our course. Here's how your employer can harness the power of Becoming a BayeZian:

  • Streamline Data Analysis: Arm your team with advanced Bayesian skills to streamline data analysis processes, leading to quicker insights and informed decision-making.

  • Optimize Workflows: Develop tailored post-processing workflows aligned with your company's requirements, enhancing efficiency and minimizing manual data manipulation tasks.

  • Cost-Effective Solutions: Reduce expenses on commercial software by utilizing open-source Bayesian analysis tools and R packages for comprehensive data analysis and visualization.

  • Empower your team to excel in data analysis and propel business growth with our comprehensive Becoming a BayeZian course.

Do I need access to any commercial software?

No, you won't need any commercial software for this course. All Stan models will run through our prepackaged VMs built for your convenience.

Does the course assume extensive knowledge of R ?

We recommend having intermediate Tidyverse R skills for this course. Our BreeZing through the Tidyverse Course is a great choice for prerequisite knowledge in R.

Is the course graded? What defines successful completion?

Evaluation is based on the completion of course work rather than "correct answers." Successful completion entails:

• 80% or higher on all quizzes

• 100% of modules completed

Upon meeting these criteria, you'll receive a certificate of completion.

What if I need to drop out? Is there a refund policy?

At AthlyticZ, we're committed to your success and satisfaction throughout your learning journey.

Our refund policy is as follows:

  • You can drop the course within 3 days of commencement for a full refund. After this date, refunds are not provided.

  • No refunds will be granted if you have completed 25% or more of the course material, regardless of the time elapsed since the course began.

    Thank you for choosing AthlyticZ for your educational needs.

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