Statistical Learning with R: Data Science Essentials
Master statistical modeling and machine learning techniques using R programming language, from regression to deep learning.
Master statistical modeling and machine learning techniques using R programming language, from regression to deep learning.
This comprehensive course introduces statistical learning methods with hands-on implementation in R. Students explore both supervised and unsupervised learning techniques, including regression, classification, and modern machine learning approaches. The curriculum balances theoretical understanding with practical application, emphasizing accessible explanations over complex mathematics. Updated for 2021, the course covers essential topics from linear regression to deep learning, all implemented using R programming. Following the renowned textbook "An Introduction to Statistical Learning," students gain practical experience through R tutorials and hands-on exercises.
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What you'll learn
Master fundamental statistical learning concepts and their R implementations
Develop proficiency in regression and classification techniques
Understand model selection and validation methods
Implement machine learning algorithms using R
Explore deep learning and neural networks
Apply unsupervised learning methods for data analysis
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This practical course covers essential statistical learning methods using R programming language. The curriculum balances theoretical understanding with hands-on implementation, covering supervised and unsupervised learning techniques. Topics include linear and polynomial regression, classification methods, model selection, regularization, tree-based methods, support vector machines, neural networks, and clustering. The course emphasizes practical applications while providing sufficient theoretical foundation for understanding modern data science approaches. All concepts are implemented in R with detailed tutorials.
Fee Structure
Instructors
2 Courses
A Pioneer in Statistical Learning and Data Science
Trevor Hastie, born on June 27, 1953, in South Africa, has revolutionized the field of applied statistics as the John A. Overdeck Professor at Stanford University. His remarkable journey began with degrees from Rhodes University and the University of Cape Town, followed by a Ph.D. from Stanford in 1984 under Werner Stuetzle's supervision. After nine influential years at AT&T Bell Laboratories, where he helped develop the R statistical computing environment, he joined Stanford's faculty in 1994. His groundbreaking research spans statistical modeling, machine learning, and bioinformatics, with significant contributions including the development of generalized additive models and principal curves. Hastie has authored six influential books, including the widely-used "Elements of Statistical Learning" and "Statistical Learning with Sparsity," while publishing over 200 research articles that have garnered more than 378,000 citations. His exceptional contributions have earned him numerous accolades, including fellowship in major statistical societies, election to the U.S. National Academy of Sciences in 2018, and membership in the Royal Netherlands Academy of Arts and Sciences in 2019. Currently focusing on applied problems in biology, genomics, and medicine, he continues to advance the field through innovative statistical methodologies and software implementations in the R system, while serving as Professor of Statistics and Biomedical Data Science at Stanford.
2 Courses
A Statistical Pioneer Revolutionizing Data Science and Medicine
Robert Tibshirani, born in Niagara Falls, Ontario, has fundamentally transformed statistical analysis through his groundbreaking methodologies, particularly the Lasso method which revolutionized high-dimensional modeling. After completing his education at the University of Waterloo, University of Toronto, and Stanford University, where he earned his Ph.D. under Bradley Efron, he began his career at the University of Toronto before joining Stanford in 1998 as Professor in the Departments of Health Research and Policy and Statistics. His influential works include five major books, such as "The Elements of Statistical Learning" and "An Introduction to the Bootstrap," which have become cornerstone texts in statistical learning. His research spans applied statistics, biostatistics, and data mining, with particular focus on genomics and proteomics. His impact is reflected in nearly 500,000 citations, and his achievements have earned him numerous prestigious honors including the COPSS Presidents' Award, ISI Founders of Statistics Prize, and election to the U.S. National Academy of Sciences, the Royal Society of Canada, and the Royal Society of Britain. Currently collaborating with Balasubramanian Narasimhan on software packages for genomics and proteomics, Tibshirani continues to bridge the gap between statistical theory and practical applications while mentoring future generations of statisticians.
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4.5 course rating
42 ratings
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