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Roger D. Peng, PhD and Brooke Anderson

R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.

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R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.

This Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.

This Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts.

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What's inside

Five courses

The R Programming Environment

This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks.

Advanced R Programming

This course covers advanced topics in R programming, including functional programming, error handling, object-oriented programming, profiling, debugging, and function design. Upon completion, students will be able to identify and abstract common data analysis tasks and encapsulate them in user-facing functions. They will also be able to define new data types in R and develop a universe of functionality specific to those data types.

Building R Packages

Writing good code for data science is only part of the job. In order to maximize the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others.

Building Data Visualization Tools

(0 hours)
The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks.

Mastering Software Development in R Capstone

Learning objectives

  • Build r packages
  • Custom data visualization and graphics
  • Data manipulation and wrangling
  • Produce and scale data science products

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