Advanced R Programming
This course is part of Mastering Software Development in 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 Mastering Software Development in R 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.
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Create robust and reusable data science tools
Implement functional and object-oriented programming
Develop custom data types and functions
Optimize code performance through profiling
Master advanced error handling techniques
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
3 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course covers advanced topics in R programming essential for developing robust data science tools. The curriculum includes functional programming, error handling, object-oriented programming, profiling, benchmarking, and debugging. Students learn to create reusable functions, define custom data types, and develop organization-specific data science tools. Special emphasis is placed on writing efficient, maintainable code through proper function design and implementation.
Welcome to Advanced R Programming
Module 1 · 0 Hours to complete
Functions
Module 2 · 2 Hours to complete
Functions: Lesson Choices
Module 3 · 2 Hours to complete
Functional Programming
Module 4 · 2 Hours to complete
Functional Programming: Lesson Choices
Module 5 · 3 Hours to complete
Debugging and Profiling
Module 6 · 2 Hours to complete
Object-Oriented Programming
Module 7 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Mastering Software Development in R
Instructors
Pioneering Research in Environmental Health
Dr. Anderson is an Assistant Professor at Colorado State University in the Department of Environmental & Radiological Health Sciences and a Faculty Associate in the Department of Statistics. She is actively involved in the university’s Partnership of Air Quality, Climate, and Health and serves on the editorial boards of Epidemiology and Environmental Health Perspectives. Previously, she completed a postdoctoral appointment in Biostatistics at Johns Hopkins Bloomberg School of Public Health and earned her PhD in Engineering from Yale University. Her research focuses on health risks related to climate-related exposures, such as heat waves and air pollution, and she has conducted several national-level studies. Dr. Anderson has also developed open-source R software packages to enhance environmental epidemiologic research.
Professor of Biostatistics at Johns Hopkins University
Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and serves as a Co-Editor of the Simply Statistics blog. He earned his PhD in Statistics from the University of California, Los Angeles, and is recognized for his research in air pollution, health risk assessment, and statistical methods for environmental data. In 2016, he received the Mortimer Spiegelman Award from the American Public Health Association, honoring his significant contributions to health statistics. Dr. Peng developed the Statistical Programming course at Johns Hopkins to equip students with essential computational tools for data analysis. Additionally, he is a national leader in promoting reproducible research practices and serves as the Reproducible Research editor for the journal Biostatistics. His interdisciplinary research has been published in prestigious journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. He has authored over a dozen software packages that implement statistical methods for environmental studies and reproducible research, and he regularly conducts workshops and tutorials on statistical computing and data analysis.
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4.2 course rating
573 ratings
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