Coursera - Computing for Data Analysis (2013)
English | Audio: aac, 44100 Hz, mono (und)
MP4 | Video: h264, yuv420p, 960x540, 30.00 fps(r) (und) | 475 MB
Genre: Video Training
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.
Introduction and overview
Data types, subsetting
Plotting, visualizing data
Priniciples of data graphics
Objected oriented programming
About the Instructor(s)
Roger D. Peng is an associate professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. He received his Ph.D. in Statistics from the University of California, Los Angeles and is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for spatial and temporal data. He created the course Statistical Programming at Johns Hopkins where it has been taught for the past 8 years. Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. His research is highly interdisciplinary and his work has been published in major substantive and statistical journals, including the Journal of the American Medical Association, Journal of the American Statistical Association, Journal of the Royal Statistical Society, and American Journal of Epidemiology. Dr. Peng is the author of more than a dozen software packages implementing statistical methods for environmental studies, methods for reproducible research, and data distribution tools. He has also given workshops, tutorials, and short courses in statistical computing and data analysis.
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