Data Analytics

R Programming

Data Analytics is an important field for businesses around the globe. Companies are collecting more data than ever before. Through a variety of different techniques, companies are able to make qualitative and quantitative observations about behavioral and data patterns. From these observations they are able to make better and more competitive business decisions.

Tech Kits

Tech Kits are part of the walk-in service provided by OPIM Innovate. There are three levels of difficulty meant for different users and their experience with the different technologies. Many of the Tech Kits build off each other as you progress.

Beginner

Introducing R Programming

Length: 30 - 60 Minutes

Description: R is a language and environment for statistical computing and graphics. It is both widely used and open-source, providing great flexibility for data analysis. This tech kit provides an overview of the R interface along with a number of examples involving arithmetic operations, variables, comparison operators, vectors, matrices, data frames and factors.

Intermediate

Exploring the Capabilities of R

Length: 30 - 60 Minutes

Description: As a result of R’s status as an open-source language and software environment, a plethora of R learning resources are available for users. This makes R an attractive language to learn and leverage. This tech kit explores using packages, relational operators, conditional operators, loops, and functions within R.

Advanced

Recognizing the Versatility of R

Length: 30 - 60 Minutes

Description: Many useful R functions come in packages: free libraries of code written by R’s user community. In addition to built-in packages, over 5,000 packages are available for download. This tech kit explores importing, tidying, summarizing and visualizing data using built-in capabilities and additional packages.

 

Projects

Resources

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R i386 3.4.2

Freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc.

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RStudio 1.1.383

RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management