Beginners Guide to R – R Introduction

R Introduction

R is a powerful programming language for statistical computing and data analysis.

It was developed in the early 1990s by Ross Ihaka and Robert Gentleman, and released in 2000.

R is still maintained by the R Core Team.

Who uses R?

Here’s a list of some top-tier companies that you know that use R.

Facebook: For behavior analysis related to status updates and profile pictures.

Google: For advertising effectiveness and economic forecasting.

Twitter: For data visualization and semantic clustering.

Uber: For statistical analysis.

Airbnb: Scale data science.

ANZ: For credit risk modeling.

Why Choose R?

Here are some of the features that make R an appealing choice.

R is Popular: R ranks 22nd in the TIOBE index (a measure of popularity of programming languages).

R is Simple: R has a simple syntax similar to the English language.

R is Free: The R is developed under an open-source license, making it free to install, use, and distribute, even for commercial purposes.

R is Platform Independent: R works on different platforms (Windows, Mac and Linux).

R is Portable: Code written for one platform will work on any other platform.

R is Interpreted: Meaning that code can be executed as soon as it is written. This means, quicker development cycles.



Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

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