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School of Data Science Management and Technology

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R Programming

Duration
48 Hours
1:1 Mentoring

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+91 9830247087

100% guidance through question bank by Industry Experts from Topmost Companies

Why Join Handson for this Course?

Through interactive exercises, you can learn and clear all our doubts related to this subject.

Understand each topic with real-life examples and analogies.

Learn to work with the versatile R language for scripting programming applications and much more.

Master the various activities and methodology used by R Programmers.

Develop hands-on skills using the most popular R libraries used by the experts.

Work with real-world datasets to enable stakeholders draw informed conclusions.

Program Overview

This course is designed to master techniques like data exploration, data visualization, and predictive analytics and descriptive analytics with the R programming language. The course covers the import and export of data in R, data structures in R, different statistical concepts, cluster analysis, and forecasting. Student will gain an understanding of analyzing data to help companies make more effective business decisions.

Key Features

Program Details

This Professional Certificate from Handson has a comprehensive course curriculum covering Statistics, data structures and more – with a great detail via our interactive learning model so that you are comfortable with any kinds of questions asked later on. Upon successfully completing this course, you will be able to fast track your career in the field of interest and it will help you to kick start into an exciting profession.

LEARNING PATH

  • History of R-language
  • Why to learn R-language
  • Importance of R-language
  • Installation and setup Environment
  • Packages interfaces and library
  • Expressions and Operations
  • Data Types and Data Structures- Vectors, Factors,Matrix, Dataframes,Lists
  • Vector Basics
  • Vector Operations
  • Vector Indexing and Slicing
  • Matrix Operations
  • Data Frame Indexing and Selection
  • Operations on Data Frame
  • CSV Files with R
  • Operators
  • Conditional Statements
  • Loops & Functions
  • Built-in R Features & Apply
  • Dates and Timestamps
  • Understanding & Working with Graph Libraries.
  • Overview of ggplot2
  • Histograms
  • Scatterplots
  • Bar Plot
  • Boxplots
  • 2 Variable Plotting
  • Sorting, Concatenation of Datasets
  • Concept of Hypothesis.
  • Null Hypothesis
  • Alternative Hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Intervals
  • Parametric Tests and Non Parametric Tests
  • One Sample T test
  • Two Independent Sample T test
  • Paired Sample T test
  • Chi-square Test for Independence of Attributes
  • Principal Component Analysis
  • Concept of Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Introduction to Machine Learning
  • Data Munging in R
  • Cyclical vs Seasonal Analysis
  • One Way Anova
  • Two Way Anova
  • Concept of Linear Regression.
  • Important features of a Straight line.
  • Method of least Square.
  • Assumptions of Classical Linear Regression Model
  • Understanding the Goodness of Fit
  • Test of Significance of the Estimated parameters.
  • Concept of multicollinearity
  • Concept of VIF
  • Concept of AutoCorrelation
  • Practical Application of Linear Regression using R.
  • Concept of Logistic Regression .
  • Differences between Linear Regression and Logistic Regression.
  • Logistic Regression Model.
  • ODDS AND ODDS RATIO-Mathematical Concepts
  • Concept of Concordant Pairs, Discordant Pairs, Tied Pairs.
  • Confusion Matrix and its Measures
  • Determining the Cut-Point Probability Level.
  • Receiver Operating Characteristic Curves
  • Practical Application of Logistic Regression using R.
  • Concept of Time Series and its Application
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
  • Concept of Decision Tree
  • Decision Tree Application using R.
  • Concept of K-Means Clustering.
  • Types of Cluster Analysis.
  • Concept of Linkage.
  • Ward’s Minimum Variance Criteria.
  • Clustering related Statistics-Semi-Partial R-Square,R Square
  • Graphical Representation of Cluster Analysis
  • Practical Application of Clustering using R.
  • Concept of Text Mining and Sentiment Analysis
  • Concept of Stopwords
  • Practical Application of Text Mining and Sentiment Analysis
  • Concept of Market Basket Analysis
  • Measures of Market Basket Analysis-Support,lift,Confidence
  • Advantages of Market Basket Analysis
  • Practical Application of Market Basket Analysis.

SKILLS COVERED

ADMISSION DETAILS

APPLICATION PROCESS

The application process consists of few simple steps. The  admission process will be completed after the payment is done by the selected candidates.

Submit Application

Submit the required documents for admission 

Application Review

Candidates will be selected based on their application

Admission

Selected candidates will get login credentials to begin the program

ELIGIBLE CANDIDATES

For admission to this global MBA program, candidates should have:

Any Graduate Candidate from any stream

Bachelor's degree with a minimum of 50% marks

Any aspiring Candidate from any functional area

ADMISSION GUIDENCE

We have a team of dedicated admissions counselors who are available to guide you as you apply to the program. They are available to:

Contact Us

+91 9830247087

ADMISSION FEE

We are dedicated to making our programs accessible and make it more economical.

Total Program Fee

₹ 15,000

(Incl. taxes)

For any fee related queries, please contact with our Admission Counselor in the above given number.

Apply Now

START APPLICATION
PROGRAM BENEFITS

FAQs

R is a programming language used in data science and statistical computing. It is a free software with a collection of libraries used as a data analysis tool.

  • R Programming excellent for statistical analysis.
  • Supports Various Data types and a large variety of libraries.
  • R performs various ML operations for developing various features of AI etc.

A completion certificate will be given from Handson upon completing this course.

Any Graduate or Professional from any stream/background who want to learn R Programming can join this course.

It is a Live Instructor-led training. Easy to use virtual classroom, Each class is recorded, review as much as you like.

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