Data Science using R


Data Science using R is simply a programming environment and language made particularly for graphical programs and statistical computations. It is licensed underneath the GNU license, just like the S language, it is developed by means of Bell Laboratories. It could be very just like the S language, even though applied in a special manner. Because of this, some programs written in the S language also may be run in R’s environment, without a lot alteration.

The R language can be very useful in the discipline of statistical computation and statistics science. It offers various techniques like clustering, time-series evaluation and classification technique, nonlinear/linear modelling and classical statistical tests. Also, this language is very adaptable and extensible. Along with these, it facilitates many graphical strategies too.

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Data science using R capabilities for information technological information programs:

R is a very unique language and has some actually interesting abilities which aren’t determined in extraordinary languages. These functions are very important for records technology packages. Some of these capabilities are described below:

Multiple calculations can be carried out with vectors

R is a vector language. Unlike other languages, R can do many stuff right now. You can add functions to a unmarried vector without placing it in a loop. This feature of R makes it more powerful and quicker than the opposite languages.

You can run your code without any compiler with data science using R

As R is an interpreted language, you could run your code without any compilers. In other programming languages like Java or C, a compiler is wanted to make out the commands out of your code before running it. However, R straight away interprets the code right into a full-fledged program. This makes improvement of the code easier.

Statistical language in data science using R

R modified into designed for statistical research, and proved amazing in its area of work. However, as the power of R is being realised, it is locating use in a whole lot of other places, starting from monetary research to genetics and biology and medicine. This is because R is a Turing-whole language, this means that that any task can be programmed in R.

Data technology help

R offers guide features for information technology programs. Some of them are charts, graphs, records interface, statistical capabilities, etc. All these capabilities are especially used for facts science packages and statistical analysis.

The highlights which display why Data science using R is critical for records technological know-how:

Data assessment software

R is s information evaluation software. It is used with the useful resource of facts scientists for statistical analysis, predictive modeling and visualization.

Statistical evaluation surroundings

R programming for data science using R provides an entire environment for statistical assessment. It is simple to position into impact statistical techniques in R. Most of the new studies in statistical assessment and modeling is executed the usage of R. So, the new strategies are first available fine in R.

Open supply

R is open deliver technology, so it is very smooth to combine with different applications.

Community useful resource

R has the community help of primary statisticians, information scientists from distinctive factors of the arena and is developing rapidly. So, most of the improvement of R language is completed with the resource of keeping statistics science and records in mind. Learn r for data science, out to be the default desire for statistics science packages and statistics technology professionals.

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Module 1

Introduction to Data Science

Module 2

Introduction to R

Module 3

Working with IDE – RStudio

Module 4

Working with data types/ modes in R

Module 5

Data structures in R

Module 6

File processing in R

Module 7

Working with vectors

Module 8

Data frames

Module 9

arrays and lists

Module 10

Introduction to R libraries and packages

Module 11

Data Manipulation in R using dplyr

Module 12

Visualization in R

Module 13

Linear Regression

Module 14

Logistic Regression

Module 15

Ensemble Methods

Module 16

Principal Component Analysis

Module 17

Support Vector Machine

Module 18

Bayesian Analysis

Module 19

Neural Networks

Module 20

K-means clustering

Module 21

NLP using R

Module 22

Working with Tibbles

Module 23

Regex in R

Module 24

Data science project stages and lifecycle using R
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Enrolled: 301 students
Duration: 40 Hours
Lectures: 24
Level: Beginner


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Data Science using R
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