Hetvi Patel completed an online Coursera course in R Programming from Johns Hopkins University. The course covered using and programming in R for effective data analysis, including programming in R, reading data, accessing packages, writing functions, debugging, profiling code, and organizing and commenting code. The certificate was signed by Roger D. Peng, Jeffrey Leek, and Brian Caffo of Johns Hopkins Bloomberg School of Public Health and notes that the online course does not reflect the entire on-campus curriculum.
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
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Statement of Accomplishment: Data Science Specialization II - R ProgrammingFolco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 2nd Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
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 and describe 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, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize
a data analysis so that it is reproducible and accessible to others.
1. coursera.org
Statement of Accomplishment
FEBRUARY 12, 2015
HETVI PATEL
HAS SUCCESSFULLY COMPLETED THE JOHNS HOPKINS UNIVERSITY'S OFFERING OF
R Programming
This course covers how to use & program in R for effective data
analysis. It covers practical issues in statistical computing:
programming in R, reading data into R, accessing R packages,
writing R functions, debugging, profiling R code, & organizing and
commenting R code.
ROGER D. PENG, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
JEFFREY LEEK, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
BRIAN CAFFO, PHD, MS
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
PLEASE NOTE: THE ONLINE OFFERING OF THIS CLASS DOES NOT REFLECT THE ENTIRE CURRICULUM OFFERED TO STUDENTS ENROLLED AT
THE JOHNS HOPKINS UNIVERSITY. THIS STATEMENT DOES NOT AFFIRM THAT THIS STUDENT WAS ENROLLED AS A STUDENT AT THE JOHNS
HOPKINS UNIVERSITY IN ANY WAY. IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY GRADE; IT DOES NOT CONFER JOHNS HOPKINS
UNIVERSITY CREDIT; IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY DEGREE; AND IT DOES NOT VERIFY THE IDENTITY OF THE
STUDENT.