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.
Showing posts with label Johns Hopkins University. Show all posts
Showing posts with label Johns Hopkins University. Show all posts
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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