R Programming for Data Science Course in Hyderabad, India

Digital Nest is the pioneer and leading institute in Hyderabad, India for rendering the R Data Science Courses in HyderabadThis provides an opportunity for the trainees to become experts in R Programming and work on R Programming for Data Science, R for data analyticsR for machine learningR for data analysis.

We have the professional back-end team for the real-time assistance to support and respond to the trainees’ queries at any point of time. We are positioned in MadhapurHitech City and operate in three learning centres viz. Ameerpet, Panjagutta and Madhapur in Hyderabad, India.

 What is R programming for Data Science Course all about?

R is a prominent language of choice to analyze and segregate data. For Data Visualization, Machine Learning and Data Analysis, R Programming Language is used extensively. This Data Science Course with R really helps in organizing data quickly.

This R programming course content curriculum is composed of Real-Time Data Science Industry Expertise to ensure trainees both theoretical and practical knowledge. We provide a comprehensive and credible way of R Data Science Course in Hyderabad with Hands-on Training Experience to students for grasping In-Depth subject knowledge by working on Real time Data Science Projects with R.

R Programming for Data Science is a certification course that helps the trainees with the clear understanding of its exclusive use of python in Data Science. This Data science course with python consists of basic concepts of R Programming Language for Beginners, deep understanding of R Programming for Data ScienceR for Machine Learning and R for Artificial Intelligence.

We render training in two forms:
  1. R Programming Classroom Training in Madhapur, Ameerpet, Panjagutta, Hyderabad India.
  2. R Data Science Online Course

R Programming Course Content is composed and updated as per latest Data Science industry standards and the content of the course is stimulated by the changes in the subject.

Digital Nest, like any other institute that provide R training in Hyderabad, India offers the spectacular course along with the Real-Time case studies that are emphasized by the expert trainers. Module based assignments are conducted to ensure consistent understanding amongst the trainees. R Data Science Certification is imparted by the execution of R Data Science Projects submitted by every student. Students are encouraged to practice on their technical plans and qualified guidance is assured by our certified Certified Data Science Professional.

Who are eligible candidates for this Data Science Course with R?

Any student who wants to establish their career in Data Science can take this R Programming Course in Hyderabad.

The ideal candidates are
  • Software engineers and data analysts
  • Business intelligence professionals
  • SAS developers wanting to learn open source technology
  • Those who are seeking for a career in data science

 

What are the pre-requisites to take this R Data Science Course?

There would not be particular prerequisites to learn R Programming. It would be beneficial if one has the basic knowledge in any of the programming languages.

 

Python data science training in Hyderabad Course Highlights
  • The program comes with a cutting-edge industry-aligned curriculum.
  • Course can be rendered in two modes: Classroom and Online training, using the practical hands-on learning methodology.
  • 100% Placement, Internship and Live Project assistance by industry’s professionals.
  • Students will be provided Videos, Backup Classes, Revision Classes, Assignments and projects.
  • Candidate will be provided with the Digital Nest Certificate
  • Deep understanding of Big Data Analytics along with business perspectives and cutting-edge Practices

 

Data Analytics with R Course Structure

Module 1: Introduction to R

  • Why R and importance of R in Analytics?
  • Installation of R and R-studio
  • Data types
  • Variables
  • Operators
  • Decision making
  • Loops
  • Lists
  • Vectors
  • Strings
  • Matrices
  • Arrays
  • Factors
  • Functions (Built-in and User defined functions)
  • Importing Data from texts and spreadsheets
  • Data frames
  • Packages, libraries and their installation
  • Data manipulation and re-shaping
  • Data Visualization using R
  • Exam

Module 2: Machine Learning

  • Supervised Learning:
  • What is supervised learning
  • Algorithms in Supervised learning
  • Steps in Supervised learning

 Regression Analysis :

  • Regression vs classification
  • Computation of co-relation coefficient and Analysis
  • Performance and accuracy measurement of a Model
  • Model Training, Validation and Testing
  • Ordinary Least squares
  • Variable selection
  • R-Square coefficient and RMSE as a strength of model
  • Prediction and confidence interval determination and application
  • Proviso of Regression
  • Dummy variables
  • Types of Regression: Linear and Logistic( Simple and multiple)
  • Sum of least squares
  • ROC and AUC curves
  • Homoscedasticity and Heteroscedasticity
  • Multicollinearity and vif
  • Confusion matrix
  • Techniques to improve accuracy and performance of regression models
  • Assignment

 Decision Trees and Random Forest Test

  • Introduction to Decision tree Algorithms and it’s applications
  • Classification and regression trees-CART models, ID3, C4.5
  • CHAID analysis
  • Building Decision Trees using R
  • Decision nodes and leaf nodes
  • Variable Selection, Parent and child nodes branching
  • Stopping Criterion
  • Tree pruning
  • Depth of a tree
  • Overfitting
  • Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
  • Regression using decision tree
  • Interpretation of a decision tree using If-else
  • Pros and cons of a decision tree
  • Introduction to Random forest test and it’s applications
  • Why Random forest test?
  • Tree bagging
  • Models and algorithms in Random Forest test
  • Training Data set Tree grouping and decision making on majority voting
  • Boosting algorithms-Gradient Boosting, Adaptive boosting-Adaboost, XP boost ( Advanced)
  • Accuracy estimation using cross-validation

KNN-algorithm:

  • What is KNN and why do we use it?
  • KNN-algorithm and regression
  • Curse of dimensionality and brief introduction to dimension reduction
  • KNN-outlier treatment and anomaly detection
  • Cross-Validation
  • Pros and cons of KNN

Support Vector Machines

  • Linear and Non-Linear SVM’s
  • SVM regression
  • Train time and Runtime complexities
  • Kernel Methods

Module 3: Unsupervised Learning:

  • Introduction to unsupervised learning?
  • Algorithms in unsupervised learning
  • Steps in unsupervised learning

 Dimensionality Reduction:

  • Introduction to dimensionality reduction and it’s necessity
  • Principal Component Analysis(PCA)
  • Singular Value Decomposition(SVD)
  • Kernel-PCA
  • Linear Discriminant Analysis