Statistical Inference for Estimation in Data Science
Master statistical estimation techniques including maximum likelihood and confidence intervals using R.
Master statistical estimation techniques including maximum likelihood and confidence intervals using R.
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.
4.1
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Identify and evaluate characteristics of good estimators
Construct maximum likelihood estimators
Develop method of moments estimators
Create confidence intervals for various parameters
Understand sampling distributions
Implement statistical inference techniques in R
Skills you'll gain
This course includes:
8.1 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course covers advanced statistical inference and estimation techniques essential for data science applications. Students learn theoretical foundations and practical implementations of parameter estimation, including method of moments and maximum likelihood estimation. The curriculum includes construction and interpretation of confidence intervals, sampling distributions, and asymptotic properties of estimators. Special emphasis is placed on both mathematical rigor and practical application using R programming.
Start Here!
Module 1 · 1 Hours to complete
Point Estimation
Module 2 · 7 Hours to complete
Maximum Likelihood Estimation
Module 3 · 4 Hours to complete
Large Sample Properties of Maximum Likelihood Estimators
Module 4 · 5 Hours to complete
Confidence Intervals Involving the Normal Distribution
Module 5 · 5 Hours to complete
Beyond Normality: Confidence Intervals Unleashed!
Module 6 · 4 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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4.1 course rating
71 ratings
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