Statistical Inference
Master fundamental concepts of statistical inference, from probability and distributions to hypothesis testing and power analysis. Essential for data science.
Master fundamental concepts of statistical inference, from probability and distributions to hypothesis testing and power analysis. Essential for data science.
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: Statistics and Machine Learning 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.
4.2
(4,430 ratings)
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Instructors:
English
বাংলা, اردو, Tiếng Việt, 2 more
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What you'll learn
Draw valid conclusions about populations from sample data
Apply probability concepts and calculate expected values
Construct and interpret confidence intervals and hypothesis tests
Evaluate statistical significance using p-values
Perform power analysis and use resampling methods
Skills you'll gain
This course includes:
5.1 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course delves into the fundamentals of statistical inference, teaching students how to draw meaningful conclusions from data. The curriculum covers essential topics including probability, expected values, distributions, confidence intervals, hypothesis testing, p-values, power analysis, and resampling methods. Through practical applications and hands-on exercises, students learn to make informed decisions in data analysis and understand the theoretical foundations of statistical inference.
Probability & Expected Values
Module 1 · 18 Hours to complete
Variability, Distribution, & Asymptotics
Module 2 · 11 Hours to complete
Intervals, Testing, & Pvalues
Module 3 · 11 Hours to complete
Power, Bootstrapping, & Permutation Tests
Module 4 · 12 Hours to complete
Fee Structure
Instructors
Expert in Biostatistics and Neuroinformatics
Brian Caffo, PhD, is a professor in the Department of Biostatistics at the Johns Hopkins University Bloomberg School of Public Health. He earned his PhD in Statistics from the University of Florida in 2001. Specializing in computational statistics and neuroinformatics, he co-created the SMART working group
Chief Data Officer and J Orin Edson Foundation Chair at Fred Hutchinson Cancer Center
Dr. Jeff Leek serves as the Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. He earned his PhD in Biostatistics from the University of Washington and is known for his significant contributions to genomic data analysis and statistical methods for personalized medicine. His research has advanced our understanding of molecular mechanisms related to brain development, stem cell self-renewal, and immune responses to trauma, with findings published in top scientific journals such as Nature and Proceedings of the National Academy of Sciences. Dr. Leek developed a highly acclaimed Data Analysis course for Biostatistics students at Johns Hopkins, which has consistently received teaching excellence awards. He is also recognized for his efforts in creating educational initiatives that leverage data science for public health and economic development, including massive open online courses that have engaged millions worldwide.
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4.2 course rating
4,430 ratings
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