R has great ways to handle working with big data including programming in parallel and interfacing with Spark. In this track, you'll learn how to write scalable and efficient R code and ways to visualize it too.
Gain the career-building programming skills you need to successfully develop software, wrangle data, and perform advanced data analysis in R. No prior coding experience required.
In this track, you'll learn how to manipulate data, write efficient R code, and work with challenging data, including date and time data, text data, and web data using APIs. As you become more comfortable with these skills, you'll move on to learn about writing functions and object-oriented programming—an essential skill when working with large and complex programs. Through interactive exercises, you'll also gain experience working with powerful R libraries, including devtools, testthat, and rvest, that will help you perform key programmer tasks, such as web development, data analysis, and task automation. Start this track and embark on your journey to becoming a R programmer.
Take your tidyverse skills to the next level. This track covers getting your data in the right condition to start your analyses, writing better code with functional programming, and generating, exploring, and evaluating machine learning models. And you'll do all of this in the wonderful and clean world of the tidyverse.
Are you interested in analyzing next-generation sequencing data but lacking in strong computational skills? In this skills track, geared towards non-computational biologists, you will learn to use Bioconductor, the specialized repository for bioinformatics software, along with essential Bioconductor packages. Then, you'll learn about current best-practice workflows for RNA sequencing differential expression analysis, analyzing single-cell RNA sequencing data, as well as Chip-sequencing data.