R Language

1 Session - 3 Hrs
Total 20 Sessions - 60 Hrs

R Language

R may be a language and surroundings for applied math computing and graphics. it’s a wildebeest project that is comparable to the S language and surroundings that was developed at Bell Laboratories (formerly AT&T, currently glowing Technologies) by John Chambers and colleagues. R is thought of as a unique implementation of S. There square measure some necessary variations, however abundant code written for S runs timeless underneath R.

R provides a large form of applied math (linear and nonlinear modelling, classical applied math tests, time-series analysis, classification, clustering, …) and graphical techniques, and is very protractible. The S language is usually the vehicle of selection for analysis in applied math methodology, associated R provides an Open supply route to participation in this activity.

One of R’s strengths is that the ease with that well-designed publication-quality plots is created, as well as mathematical symbols and formulae wherever required. guardianship has been confiscate the defaults for the minor style selections in graphics, however the user retains full management.

R is on the market as Free software package underneath the terms of the Free software package Foundation’s wildebeest General Public License in ASCII text file type. It compiles and runs on a large form of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

Course Contents

R Programming, R Studio IDE setup and overview, capabilities of R.

R Basics

  • Variables, Loops & Functions, Data Structures.
  • Data Import & Export (CSV, excel), Database connectivity(SQL: MySQL, NoSQL: MongoDB).
  • JSON, HTML, XML file handling.

Statistical Processing.

Extract, Transformations, Loading(ETL), Data Preparation & Cleaning, Data Distribution & Analysis, Data Preparation for Machine Learning.

Web Scrapping using R.

Web Log processing using R.

Machine Learning:

  • Regression –>  Linear, Multi Linear, Polynomial, Decision Trees, Random Forrest, SVR.
  • Classification –> Logistic Regression, SVM, Kernel SVM, Decision Trees, Random Forrest, Naïve Bayes, K Nearest Neighbor.
  • Clustering –> KMeans Clustering, Dimensionality Reduction: PCA, LDA, Association Rule Mining: Apriori, ECLAT.

Neural Networks: Using H2O Deep Learning.

Data Visualization

  • Shiny.
  • ggplot.
  • Bar Plot, Histograms, Pie Charts, Contour Plot, Scatter plot, Line charts.

Sentiment Analysis

Exploratory Data Analysis

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