Data Science with R Programming

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Course Summary

Using R for Data Science

Those interested in data science may be interested in learning the R programming language. R for data science can be used for statistical analysis and other functions. There are a number of ways to embark on your path to learn R. Keep reading to learn more about R in data science, R vs. Python, real-world applications of R, the best add-on packages for R and more.
The R Foundation, a nonprofit focused on supporting the continued development of R through the R Project, describes R as “a language and environment for statistical computing and graphics.” But, if you’re familiar with R for data science, you probably know it’s a lot more than that. R was created in the 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. The R language was modeled based on the S language developed at Bell Laboratories by John Chambers and other employees. Today, R is an open-source language; it’s accessible as a free software compatible with many systems and platforms. R is an open-source software: R is free and adaptable because it’s an open-source software. R’s open interfaces allow it to integrate with other applications and systems. Open-source software have a high standard of quality, since multiple people use and iterate on them.R is a programming language: As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data.R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.R is an environment for statistical analysis. R has various statistical and graphical capabilities. The R Foundation notes that it can be used for classification, clustering, statistical tests and linear and nonlinear modeling.R is a community. R Project contributors include individuals who have suggested improvements, noted bugs and created add-on packages. While there are more than 20 official contributors, the R community extends to those using the open-source software on their own.

R vs. Python

Python and R are both open-source software languages that have been around for a while. When comparing R vs. Python, some feel that Python is a more general programming language. Python is often taught in introductory programming courses and is the primary language for multiple machine learning workflows, R Studio reports. R is typically used in statistical computing. R Studio notes that R is often taught in statistics and data science courses. It adds that many machine learning interfaces are written in Python, while many statistical methods are written in R.

In terms of R vs. Python environments, the R environment is ideal for data manipulation and graphing. Some Python applications include web development, numeric computing and software development. Additionally, while R has numerous packages, Python has many libraries devoted to data science.

Whether or not R vs. Python is better may come down to what you’re using each for. Being knowledgeable in both languages can be beneficial in data science. In fact, R Studio notes that many data science teams are “bilingual,” using both R and Python.

How Is R Used in Data Science?

R for data science focuses on the language’s statistical and graphical uses. When you learn R for data science, you’ll learn how to use the language to perform statistical analyses and develop data visualizations. R’s statistical functions also make it easy to clean, import and analyze data.

It may be equipped with an Integrated Development Environment (IDE). According to computer software company GitHub, the purpose of an IDE is to make writing and working with software packages easier. R Studio is an IDE for R that improves the accessibility of graphics and includes a syntax-highlighting editor that helps with code execution. This may be helpful as you begin to learn R for data science.

Data Science Projects That Use R

R for data science is used in industries such as banking, telecommunications and media. Below we explore examples of data visualization in R through real-life projects.

T-Mobile: The international communications company uses R to classify customer service texts so it can properly direct customers to an agent, Revolutions reports. T-Mobile even shared an open-source version of their messaging classification application programming interface on GitHub.

Twitter: R can be used to perform text analysis of tweets. Text analytics and scraping of Twitter data is possible through the twitteR package.

Google Analytics: R can be combined with Google Analytics data to complete statistical analysis and create clear data visualizations, according to Google Developers. Installing the RGoogleAnalytics package will enable these insights.

The Financial Times: The Financial Times embraced R to create a data visualizations in its article, “Is Russia-Saudi Arabia the worst World Cup game ever?,” Revolutions reports. The visualization mapped every World Cup match since 1998 and was created using R and the ggplot2.

BBC: Similarly, Revolutions explains how BBC uses data visualization in R to create graphics for its publications. BBC developed an R package and R cookbook to standardize their data visualization graphic creation process. Its cookbook is based on the bbplot package. BBC offers a six-week training for its data journalists to learn this process.

Syllabus

Topics you have to study

  • Understand critical programming language concepts
  • Configure statistical programming software
  • Make use of R loop functions and debugging tools
  • Collect detailed information using R profiler

Projects

Projects is conducted by highly knowledgeable trainers which helps students to enhance their skill set by putting great emphasizes on project-based learning. After the successful completion of the course, students can get their certificates by logging to their dashboard, which makes the certificates completely secure and authorized.

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