Bayesian Statistics for Data Science

🎉 Master Bayesian Statistics for Data Science! 📚

Course Title: Bayesian Statistics for Data Science

Headline: A former Google data scientist, Brian Greczo, guides you through the essentials of Bayesian statistics. Learn with real-world examples in R and Stan (Python code available too!).

Dive into the World of Bayesian Statistics! 🌱

Course Overview: This course is designed to cover the fundamental concepts of statistics that are typically taught in an introductory college course, with a special emphasis on the core elements of any Bayesian model—prior distributions, likelihoods, and finding posterior distributions, credible intervals, and predictive distributions. By the end of this course, you'll not only be more fluent in probability but also have a fresh perspective on data analysis!

Why This Course? No prior experience in Bayesian statistics is necessary. As long as you have a solid understanding of basic algebra and arithmetic, you're good to go. If you wish to engage with the coding examples, proficiency in R and RStudio, or Python, will be beneficial.

What You'll Get:

• 5.5 Hours of Video Lectures: Engage with a comprehensive curriculum that covers all the essential topics.
• Interactive Demonstrations: Learn by doing with live examples using R and Stan (Python examples included).
• Quizzes and Review Assignments: Test your knowledge with quizzes and reinforce your learning with review assignments that come with detailed solutions.

Key Learning Objectives:

• Master the basics of probability, setting a strong foundation for understanding Bayesian concepts.
• Grasp Bayes' rule, complete with relatable examples like medical testing and coin flipping.
• Understand the components of a Bayesian model, including:
  • The role of prior distributions in your analysis
  • How to interpret posterior, likelihood, and predictive distributions
  • The utility of conjugate priors
  • Calculating credible intervals and Bayes estimators
• Model various types of data:
  • Binary data with the Bernoulli and Binomial Distribution, paired with Beta distribution priors
  • Count data with the Poisson Distribution, alongside Gamma distribution priors
  • Continuous data with the Normal Distribution, complemented by Normal distribution priors
• Get an introduction to linear regression within a Bayesian framework.

Who is this course for? This course is a perfect fit for:

• Absolute Beginners: Start your journey in Bayesian statistics with clear explanations and practical examples.
• Data Science Professionals: Refresh or expand your statistical knowledge base, enhancing your data analysis skills.
• Academics: Cross disciplines and enrich your research with a deeper understanding of Bayesian methods.

Whether you're at the beginning of your career in data science, aiming to enhance your existing skill set, or simply have a fascination with Bayesian statistics, this course is tailored for all levels. Let Brian Greczo guide you through the intricacies of Bayesian statistics in an accessible and comprehensive manner!

Enroll Now and Transform Your Approach to Data Analysis! 🎓✨