We recommend the Programming tracks
R Programming
Develop your R programming skills and learn how to code like a programmer in this beginner’s track. Explore the R language and practice your R coding skills. First, you’ll learn how to work with common data structures in R, like vectors, matrices, and data frames, before expanding your skills by mastering conditional statements, loops, and vectorized functions. You’ll then discover how to optimize your R code using code profiling and benchmarking. Finally, you'll get to grips with writing functions and object-oriented programming (OOP). By the end of this track, you’ll have more confidence in programming in R and be ready to tackle more advanced R programming tasks, including advanced data visualization and machine learning.
Big Data
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.
R Developer
Gain the career-building R programming skills you need to successfully develop software, wrangle data, and perform advanced data analysis in R. No prior coding experience is required, you can start your journey to becoming an R developer today! 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 in R and object-oriented programming—an essential skill for R developers 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. By the time you finish this track, you’ll have a firm grasp of what’s needed to become an R developer and have the skills to get started as one.
Intermediate Tidyverse Toolbox
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.
Analyzing Genomic Data
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, as well as Chip-sequencing data.
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