In the present world, when we are getting drowned in the heap of data, it is very important to use tools & techniques to get meaningful knowledge & information from data. This highlights the need of data science in the present world. Companies across the globe are using various tools of data science to get key insights from their business data and in-turn improving their bottom line. That’s why the role of a data scientist is getting important with day by day. According to industry experts data scientist is considered at the sexiest job of 21st century.
R is a powerful and widely used open source software and programming environment for data analysis. R is one of the top 5 open source programming language in data science. R has been ranked 9 amongst all programming languages. R popularity is increasing rapidly day by day. R has become de-facto standard language among data analysts across the globe. R is used by some of industry giants such as facebook, google, twitter, New York times for various data analysis activities such as data pre-processing, prediction, forecasting, data visualization etc. According to Dice Salary Survey , 2014 , R is among the highest paid IT skill in US.
I have recently authored a course on “R Programming Fundmentals” available on Pluralsight. This course will provide everything you need to know to get started with the R framework, and contains a number of demos to provide hands-on practice in order to become an efficient and productive R programmer. By the end of this course, you will also learn to play with data and to extract key information using various R functions and constructs.
Course contents ( Total content length : 06h 58m) :
This first module will provide you all information you need to know to get going with R framework. We will discuss the reasons of using R framework for data analysis projects.We will also go through the process of installing R and RStudio IDE on the local machine. By the end of this module, you will be familiar with R framework and things you can accomplish using R programming.
This module will discuss ways to get help, if you come across any confusion or problem, while learning R programming. By the end of this module , you will know where and how to get help if you run across cross any issue in your R endeavors.
This module will cover some of the fundamental blocks of R programming such as variables, environments and operators. This module will also talk about vectorized operations and its benefits.
This module is the first part of data structure modules. In this module, We will discuss the need of different data structures. The part will cover various one-dimensional R data structures. By the end of this module, you will learn to create various one-dimensional data structures and to perform various operations on them.
This module is the second part of data structure modules. In this module, We will discuss several higher dimensional data structures such as data frames, matrices and arrays. By the end of this module, you will learn to create these data structures and to perform various operations on them.
This module is focused on various components of functions and their usage. By the end of this module, you will learn to create your own functions and how to use them in real world scenarios.
This module is focused on various flow control mechanism available in R. By the end of this module, you will learn to use various conditional statements and looping statements to control the flow of execution in R.
This module is focused on R packages. We will discuss some fundamental concepts related to R packages. By the end of this module, you will learn to load, install, remove and update R packages.
This module is focused on various techniques to import data in R environment from a variety of sources. By the end of this module, you will learn to import CSV files, tabular files, XML files, excel files, built-in datasets and various databases.
This module is focused on various techniques to explore a given dataset. By the end of this module, you will learn about various statistical indicators to analyze continuous and categorical data. You will also learn to use various R functions to explore the dataset and to extract various key insights.
I hope that this course will help you to generate interest in R programming as well as in the field of data science. This course will be followed by related courses on R, where I will cover different applications of R framework in the field of machine learning, data visualization, data analysis etc.
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