Data Science Using R

With this comprehensive R training learn hand-on skills on Data Science with R - the golden boy of Data Science! Over past several years R has garnered immense popularity among Data Science practitioners and it is no surprise that R language is often as referred as lingua franca of Data Science!

This Data Science R course effectively covers basic data analytics, statistical predictive modelling and machine learning through various practical examples and projects.

Best R training in Bangalore and Delhi NCR, for candidates who do not have programming background but want to acquire job oriented practical skills on a prominent open source Data Science platform. This Data Science R course is also available through live online and self-paced video based mode as well. 

You may also check for amazing value combo course Data Science Specialization to learn Data Science & Machine Learning using Python & R.

Course Curriculum:

• Building Blocks
• Introduction to Bridge Course & Analytics Software’s Basic Excel
• Basic Programming Elements
• Introduction to Basic Statistics
• RDBMS & SQL (Basics)
• Introduction to Analytics & Data Science
• Introduction to Mathematical Foundations
• Data Importing/Exporting
• Introduction R/R-Studio - GUI     
• Concept of Packages - Useful Packages (Base & Other packages)
• Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists) 
• Importing Data from various sources
• Exporting Data to various formats
• Viewing Data (Viewing partial data and full data)
• Variable & Value Labels –  Date Values
• Dimensionality Reduction & Collaborative Filtering (e-learning)
• Dimensionality Reduction: Feature Extraction & Selection
• Collaborative Filtering & Its Challenges
• Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
• Fine tuning the models using Hyper parameters, grid search, piping etc.
• Project - Consolidate Learnings
• Time Series Forecasting: Solving forecasting problems
• Machine Learning -Predictive Modeling – Basics
• Segmentation: Solving segmentation problems
• Unsupervised Learning: Segmentation
• Supervised Learning: Decision Trees
• Supervised Learning: Artificial Neural Networks (ANN)
• Supervised Learning: Ensemble Learning
• Supervised Learning: Support Vector Machines
• Supervised Learning: KNN
• Supervised Learning: Naïve Bayes
• Text Mining & Analytics