In this course, I will learn how to program in R and how to use R for effective data analysis. I 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.
In February of 2018, I was denied admission to a Ph.D. program in Linguistics. As I was navigating the application process, I simultaneously revisited a dormant interest in computer science. In March I enrolled in a certified Computer Science class via HarvardX, which I completed four months later. I am at the beginning of a new phase in my life-long learning process and look forward to that which the future will inevitably deliver.
Sunday, July 29, 2018
R Programming by Johns Hopkins University
I am about to begin R Programming from Johns-Hopkins by way of Coursera! This is course 2 of 10 that I am working through on my way to becoming a Data Scientist.
In this course, I will learn how to program in R and how to use R for effective data analysis. I 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.
In this course, I will learn how to program in R and how to use R for effective data analysis. I 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.
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