Bayesian Methods in Data Science Training Course.
Introduction
Bayesian methods provide a powerful framework for probabilistic reasoning, enabling data scientists to make informed decisions under uncertainty. From A/B testing to machine learning, Bayesian approaches are increasingly used in modern data science. This 5-day intensive training course is designed to provide participants with a deep understanding of Bayesian methods, covering foundational concepts, advanced techniques, and practical applications. Participants will gain hands-on experience in implementing Bayesian models, preparing them to tackle real-world challenges and future advancements in the field.
Objectives
By the end of this course, participants will:
Understand the fundamentals of Bayesian inference, including Bayes’ theorem, prior and posterior distributions, and likelihood functions.
Gain proficiency in using probabilistic programming languages such as PyMC3 and Stan.
Learn how to build, evaluate, and interpret Bayesian models for regression, classification, and time-series analysis.
Explore advanced topics such as hierarchical models, Bayesian networks, and Markov Chain Monte Carlo (MCMC) methods.
Apply Bayesian methods to real-world problems in healthcare, finance, marketing, and more.
Understand ethical considerations and future trends in Bayesian data science, including explainable AI and Bayesian deep learning.
Who Should Attend?
This course is ideal for:
Data scientists and analysts looking to enhance their probabilistic modeling skills.
Statisticians and researchers exploring Bayesian inference and its applications.
Machine learning engineers interested in incorporating Bayesian methods into their workflows.
Professionals in healthcare, finance, marketing, and other industries where uncertainty modeling is critical.
AI enthusiasts and practitioners preparing for future challenges in Bayesian data science.
Course Outline
Day 1: Foundations of Bayesian Inference
Morning Session:
Introduction to Bayesian Methods: Bayes’ Theorem, Prior, Likelihood, and Posterior
Comparison with Frequentist Approaches: Strengths and Limitations
Hands-on Lab: Basic Bayesian Inference with PyMC3
Afternoon Session:
Probability Distributions: Gaussian, Binomial, Poisson, and Beta
Conjugate Priors and Analytical Solutions
Case Study: Bayesian A/B Testing for Marketing Campaigns
Day 2: Bayesian Modeling for Regression and Classification
Morning Session:
Bayesian Linear Regression: Model Specification and Inference
Hands-on Lab: Building a Bayesian Linear Regression Model
Model Evaluation: Posterior Predictive Checks and Cross-Validation
Afternoon Session:
Bayesian Logistic Regression for Classification
Hands-on Lab: Building a Bayesian Logistic Regression Model
Case Study: Credit Scoring Using Bayesian Methods
Day 3: Advanced Bayesian Techniques
Morning Session:
Hierarchical Models: Partial Pooling and Shrinkage
Hands-on Lab: Building a Hierarchical Bayesian Model
Introduction to Bayesian Networks: Structure and Inference
Afternoon Session:
Markov Chain Monte Carlo (MCMC) Methods: Metropolis-Hastings and Gibbs Sampling
Hands-on Lab: Implementing MCMC with PyMC3
Case Study: Bayesian Network for Disease Diagnosis
Day 4: Bayesian Time-Series Analysis and Machine Learning
Morning Session:
Bayesian Time-Series Models: ARIMA, State-Space Models, and Gaussian Processes
Hands-on Lab: Building a Bayesian Time-Series Model
Case Study: Financial Forecasting Using Bayesian Methods
Afternoon Session:
Bayesian Machine Learning: Naive Bayes, Bayesian Neural Networks, and Gaussian Processes
Hands-on Lab: Implementing Bayesian Neural Networks with TensorFlow Probability
Ethical Considerations: Bias, Fairness, and Uncertainty in Bayesian Models
Day 5: Real-World Applications and Capstone Project
Morning Session:
Deploying Bayesian Models: Tools and Best Practices
Model Interpretability and Explainable AI (XAI) in Bayesian Data Science
Future Trends: Bayesian Deep Learning and Probabilistic Programming
Afternoon Session:
Capstone Project: End-to-End Bayesian Solution for a Real-World Problem
Project Presentations and Feedback
Course Wrap-up: Key Takeaways, Resources for Further Learning, and Certification
Key Features of the Course
Hands-on labs using modern tools like PyMC3, Stan, and TensorFlow Probability.
Real-world case studies and industry-relevant applications.
Focus on ethical AI, model interpretability, and future-proofing skills.
Access to course materials, code repositories, and a community forum for ongoing learning.
Preparing for Future Challenges
This course is designed to not only address current industry needs but also prepare participants for emerging trends and challenges in Bayesian data science. By focusing on ethical AI, explainability, and advanced techniques, attendees will be equipped to lead innovation and adapt to the rapidly evolving landscape of probabilistic modeling.
Related Events
