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Probability and Statistics - Spring 2023

UofU Data Science via YouTube

Overview

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Learn fundamental concepts in probability theory and statistical analysis through this comprehensive course from the University of Utah Data Science program. Master essential probability concepts including conditional probability, independence, total probability, and Bayes' rule before progressing to discrete random variables, expectation, and variance. Explore key statistical distributions and develop skills in statistical inference, hypothesis testing, t-distributions, and p-value analysis. Build a solid foundation in both theoretical probability and practical statistical methods through structured lectures covering everything from basic probability principles to advanced hypothesis testing techniques with t-tests, preparing you for data science applications and statistical reasoning.

Syllabus

ProbStats-S23 L0
ProbStats-S23 L1
ProbStats-S23 L2
ProbStats-S23 L3 | Conditional Probability
ProbStats-S23 L04 - Independence
ProbStats-S23 L04 - Total Probability
ProbStats-S23 L05 BayesRule
ProbStats-S23 L06 - Discrete RVs (a)
ProbStats-S23 L06b
ProbStats-S23 L06-R
ProbStats-S23 L07a
ProbStats-S23 L07b
ProbStats-S23 L08 Expectation
ProbStats-S23 L08-Var
ProbStats-S23 L09a
ProbStats-S23 L09.5
ProbStats-S23 L10
ProbStats-S23 L11
ProbStats-S23 L12 : Intro to Statistics
ProbStats-S23 L13
ProbStats-S23 L14
ProbStats-S23 L14b
ProbStats-S23 L14c : t-distribution
ProbStats-S23 L15
ProbStats-S23 L15b : Hypothesis Testing
ProbStats-S23 : Hypothesis Testing with t-Test and p-values
ProbStats-S23 L16
ProbStats-S23 L17
ProbStats-S23 L18 - Review

Taught by

UofU Data Science

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