Data Analysis Using R programming

Speaker:  Ashish Seth – Tashkent, Uzbekistan
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Data have become a real resource of interest across most industries and is rightly considered the gateway to competitive advantage and disruptive strategy. Along with the rise of data, there come two distinct efforts concerned with harnessing its potential. One is called Data Science and the other Big Data. These terms are often used interchangeably despite having fundamentally different roles to play in bringing the potential of data to the doorstep of an organization. Data Science looks to create models that capture the underlying patterns of complex systems, and codify those models into working applications. Big Data looks to collect and manage large amounts of varied data to serve large-scale web applications and vast sensor networks. Although both offer the potential to produce value from data, the fundamental difference between Data Science and Big Data is discussed in the lecture. 

The main emphasis of the lecture is to demonstrate the constructs of R programming and how it can be used for data analysis. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is an official part of the Free Software Foundation’s GNU project, and the R Foundation has similar goals to other open source software foundations like the Apache Foundation or the GNOME Foundation.

To summarize, the key point of lecture are as follows
Fundamentals of data science and big data.
Basics of R programming
Installing and configuring R environment. 
Various constructs of R programming 
Performing data analysis using R programming on a case study

About this Lecture

Number of Slides:  40
Duration:  90 minutes
Languages Available:  English
Last Updated: 

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