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’ll learn to apply these methods effectively, gaining insights into the relationships between random variables and their applications in diverse fields.
Advanced Probability and Statistical Methods
Advanced Probability and Statistical Methods
This course is part of Statistical Methods for Computer Science Specialization
Instructors: Ian McCulloh
Sponsored by ITC-Infotech
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
47 hours to complete
3 weeks at 15 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Learn to analyze relationships between random variables through joint probability distributions and independence concepts.
Understand how to calculate and interpret expected values, variances, and correlations for random variables.
Acquire essential skills in conducting statistical tests, including T-tests and confidence intervals, for data analysis.
Explore the principles of Markov chains and their applications in modeling systems with memoryless properties and calculating entropy.
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Assessments
22 assignments
Taught in English
Recently updated!
October 2024
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This course is part of the Statistical Methods for Computer Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
This course provides a comprehensive overview of probability theory and statistical inference, covering joint probability distributions, independence, and conditional distributions. Students will explore expected values, variances, and key statistical theorems, including the central limit theorem. Hypothesis testing, regression analysis, and stochastic processes such as Poisson processes and Markov chains will also be examined. Through practical applications and problem-solving, participants will gain essential skills in data analysis and interpretation.
What's included
2 readings1 plugin
This module presents the joint distributions of multiple random variables, both discrete and continuous and introduces the concept of independence.
What's included
9 videos4 readings5 assignments1 ungraded lab
This module focuses on the expectation of a random variable and joint random variable. Students will solve problems using the linearity of expectation and identify when its application is inappropriate. We will also explore variance, covariance, and correlation.
What's included
7 videos3 readings4 assignments1 ungraded lab
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’s Law.
What's included
9 videos4 readings5 assignments1 ungraded lab
This module develops student proficiency in probabilistic models to include Markov chains. Students will be introduced to problems involving surprise, uncertainty, and entropy.
What's included
4 videos2 readings3 assignments1 ungraded lab
This module develops student proficiency in probabilistic models to include Markov chains. Students will be introduced to problems involving surprise, uncertainty, and entropy.
What's included
8 videos4 readings5 assignments1 ungraded lab
Instructors
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