The course "Advanced Probability and Statistical Methods" provides a deep dive into advanced probability and statistical methods, essential for mastering data analysis in computer science. Covering joint distributions, expectation, statistical testing, and Markov chains, you'll explore key concepts and techniques that underpin modern data-driven decision-making. By engaging with real-world problems, you鈥檒l learn to apply these methods effectively, gaining insights into the relationships between random variables and their applications in diverse fields. Completing this course equips you with the skills to analyze complex data sets and make informed predictions, enhancing your proficiency in statistical reasoning and inference. Unique to this course is its blend of theoretical foundations and practical applications, ensuring that you can not only understand the principles but also implement them using tools like R. Whether you're pursuing a career in data science, machine learning, or any data-centric discipline, this course will empower you to tackle challenging statistical problems and drive meaningful insights from data.
Rare Events & Markov
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Skills You'll Learn
Markov Chain Modeling, Expectation Calculations, Statistical Inference, Application of Limit Theorems, Joint Probability Analysis
From the lesson
Inequalities and Central Limit Theorem
This module will apply several limit theorems to solve problems to include the central limit theorem, the Markov inequality, and the Chebyshev inequality. We will also prove Murphy鈥檚 Law.
Taught By
Ian McCulloh
Tony Johnson