What you'll learn

Comprehensive introduction to R programming & R Studio
Introduction to R coding
Introduction to data science
introduction to data analytics
how to install R studio
how to analyse data using R and R studio

Requirements

Some secondary level mathematics might be helpful, but not compulsory
you should have a basic understanding of Computer Programming terminologies.
A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

Description

(Please note: this course is a basic introduction to R and RStudio, meant for beginner level. More advanced courses coming soon.)

R is currently one of the most requested programming languages in the Data Science job market that makes it the hottest trend nowadays.

R is a programming language and free software environment for statistical computing, data manipulation & analysis, graphics representation and reporting supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

No one is born a data scientist. Every person who works with R today was once a complete beginner. No matter how much you know about the R ecosystem already, you'll always have more to learn.

Applications of R:

We use R for Data Science. It gives us a broad variety of libraries related to statistics. It also provides the environment for statistical computing and design.

R is used by many quantitative analysts as its programming tool. Thus, it helps in data importing and cleaning.

R is the most prevalent language. So many data analysts and research programmers use it. Hence, it is used as a fundamental tool for finance.

Tech giants like Google, Facebook, bing, Accenture, Wipro and many more using R nowadays.

Why R Programming Language?

R programming is used as a leading tool for machine learning, statistics, and data analysis. Objects, functions, and packages can easily be created by R.

It's a platform-independent language. This means it can be applied to all operating system.

It's an open-source free language. That means anyone can install it in any organization without purchasing a license.

R programming language is not only a statistic package but also allows us to integrate with other languages (C, C++). Thus, you can easily interact with many data sources and statistical packages.

The R programming language has a vast community of users and it's growing day by day.

Statistical Features of R:

Basic Statistics: The most common basic statistics terms are the mean, mode, and median. These are all known as "Measures of Central Tendency." So using the R language we can measure central tendency very easily.

Static graphics: R is rich with facilities for creating and developing interesting static graphics. R contains functionality for many Description types including graphic maps, mosaic Descriptions, biDescriptions, and the list goes on.

Probability distributions: Probability distributions play a vital role in statistics and by using R we can easily handle various types of probability distribution such as Binomial Distribution, Normal Distribution, Chi-squared Distribution and many more.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R's strengths is the ease with which well-designed publication-quality Descriptions can be produced, including mathematical symbols and formulae where needed.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

R & RStudio includes

an effective data handling and storage facility,

a suite of operators for calculations on arrays, in particular matrices,

a large, coherent, integrated collection of intermediate tools for data analysis,

graphical facilities for data analysis and display either on-screen or on hardcopy, and

a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

Who this course is for:

data science students
data analytics students
statistics students
statistical analysis students
data engineering students
people interested in data science
people interested in data analytics with R
people with python coding skills, interested to learn more about R programming
Data Science beginners
This course is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming.
If you are trying to understand the R programming language as a beginner, this course will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

Citación :

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