Statistics and R: Data Analysis Fundamentals
Learn statistical concepts and R programming for life sciences data analysis. Master basic statistical inference, p-values, and data visualization techniques.
Learn statistical concepts and R programming for life sciences data analysis. Master basic statistical inference, p-values, and data visualization techniques.
This comprehensive course combines R programming with statistical analysis, focusing on applications in life sciences. Students learn fundamental statistical concepts including inference, p-values, and confidence intervals while developing practical R programming skills. The course emphasizes hands-on data analysis, visualization techniques, and robust statistical methods. Through problem sets and R programming exercises, students gain experience in conducting reproducible research and analyzing real-world data. The course covers descriptive statistics, exploratory data analysis, and non-parametric approaches, providing a solid foundation in both statistical theory and practical implementation.
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What you'll learn
Understand random variables and statistical distributions
Master statistical inference including p-values and confidence intervals
Develop skills in exploratory data analysis using R
Learn non-parametric statistical techniques
Apply R programming for biological 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
The course integrates statistical theory with practical R programming skills, focusing on life sciences applications. Students learn fundamental statistical concepts including random variables, distributions, and inference while developing hands-on experience with R programming. The curriculum covers exploratory data analysis, non-parametric statistics, and visualization techniques. Through practical exercises and problem sets, students learn to conduct reproducible research and analyze real-world biological data. The course emphasizes robust statistical methods and provides a foundation for advanced data analysis in life sciences.
Fee Structure
Instructors
32 Courses
Harvard Biostatistics Professor and Genomics Data Analysis Pioneer
Rafael Irizarry is a distinguished Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. His expertise spans genomics, data analysis, and the R programming language. Irizarry's career has been marked by significant contributions to the field of genomics data analysis over the past two decades
14 Courses
Leading Expert in Computational Biology and Statistical Genomics
Dr. Michael Love serves as an Associate Professor in the Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health, where he also directs the Bioinformatics and Computational Biology PhD program. After completing his BS in Mathematics and MS in Statistics from Stanford University, he earned his PhD in Computational Biology from Freie Universität Berlin in 2013. His research focuses on developing statistical and computational methods for analyzing high-throughput sequencing data, particularly through his widely-used DESeq2 package for RNA sequencing analysis. As director of the Love Lab, he collaborates extensively with the Genetics Department and Lineberger Comprehensive Cancer Center, studying relationships between genetic variants and disease-related molecular changes. His expertise spans gene regulation, neuropsychiatric disorders, cancer genomics, and statistical software development. His contributions to the field have earned him multiple honors, including the UNC Center for Environmental Health and Susceptibility Recruitment Award and the Junior Faculty Development Award. Currently, he leads a $9.25 million NIH-funded study as part of the Impact of Genomic Variation on Function consortium, working to understand how genomic variation influences human health and disease.
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