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Discover the world of uncertainty and variation in sports analytics
Learn about probability, random variables, and distributions in a sports context
Master the art of simulating distributions and building models using Stan
Apply Bayesian methods to real-world sports scenarios
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
Enroll today to become a BayeZian and transform your data analysis skills!
Course Information
$1499 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!"
Introduction Bayesian Analysis for Sports
Exploring Uncertainty and Variation
Introducing Probability Random & Random Variables
Priors, Likelihoods, and Posteriors
Simulating Distributions in R
Posterior Simulation and Approximations
A Language for Describing Models
Bayesian Regression and Generalized Linear Models
Case Study: Bayesian Analysis in Sports
Join us today to access comprehensive course materials and elevate your data analysis skillset through Bayesian modeling.
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!
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.
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.
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.
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.
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.
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.
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."
AUTHOR: DO YOU WANT TO WORK IN BASEBALL?
SR. VP: COLORADO ROCKIES 2001-2014
ASST. GM: LOS ANGELES DODGERS 1998-2000
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.
No, you won't need any commercial software for this course. All Stan models will run through our prepackaged VMs built for your convenience.
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.
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.
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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