Statistical Inference and Hypothesis Testing in Data Science
Master statistical inference and hypothesis testing for data science with hands-on R programming and real-world applications.
Master statistical inference and hypothesis testing for data science with hands-on R programming and real-world applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Foundations: Statistical Inference Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Design and execute appropriate hypothesis tests for different scenarios
Interpret p-values and significance levels correctly
Understand and apply t-tests and chi-squared tests
Evaluate test power and minimize error rates
Implement statistical tests using R programming
Make data-driven decisions using hypothesis testing
Skills you'll gain
This course includes:
12.2 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course focuses on the theory and implementation of hypothesis testing in data science applications. Students learn to make informed decisions from data through statistical testing methods. The curriculum covers fundamental concepts including test statistics, significance levels, p-values, power functions, and various types of tests like t-tests and chi-squared tests. Special attention is given to proper interpretation and ethical implications of testing concepts. The course includes hands-on programming assignments in R and practical applications to real-world data analysis scenarios.
Start Here! Module 1
Module 1 · 1 Hours to complete
Fundamental Concepts of Hypothesis Testing
Module 2 · 8 Hours to complete
Composite Tests, Power Functions, and P-Values
Module 3 · 7 Hours to complete
t-Tests and Two-Sample Tests
Module 4 · 7 Hours to complete
Beyond Normality
Module 5 · 3 Hours to complete
Likelihood Ratio Tests and Chi-Squared Tests
Module 6 · 6 Hours to complete
Fee Structure
Instructor
Associate Professor
Jem Corcoran is an Associate Professor in the Department of Applied Mathematics at the University of Colorado Boulder, where he specializes in probability theory and statistical inference. He holds a Ph.D. in Applied Mathematics from Colorado State University and has been instrumental in developing courses that bridge theoretical concepts with practical applications in data science. His teaching includes "Probability Theory: Foundation for Data Science," "Statistical Inference and Hypothesis Testing in Data Science Applications," and "Statistical Inference for Estimation in Data Science."Dr. Corcoran's research focuses on advanced statistical methods and their applications, particularly in the context of data analysis and modeling. He has contributed significantly to the field through numerous publications and is recognized for his expertise in applied probability and Monte Carlo methods. As a dedicated educator, he aims to enhance student understanding of complex statistical concepts, preparing them for successful careers in data science and related fields. Through his work, Jem Corcoran continues to influence the next generation of mathematicians and data scientists at CU Boulder.
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4.8 course rating
44 ratings
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