Overview of Course

Our Business Analytics with R training is designed to equip you with the skills needed to analyze business data using the powerful R programming language. Through hands-on exercises and practical examples, you'll learn how to perform data visualization, data manipulation, and statistical analysis.

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Course Highlights

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In-depth training on R programming language and its application

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Hands-on learning through practical exercises and real-world case studies

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Comprehensive coverage of statistical analysis techniques and more<br /><br />

Key Differentiators

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    Real-world Projects

    We work with experts to curate real business scenarios as training projects

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Skills You’ll Learn


R programming language fundamentals


Data visualization using R libraries like ggplot2


Statistical analysis techniques, including hypothesis testing and regression analysis


Data manipulation and cleaning techniques using dplyr and tidyr


Practical application of R to real-world business scenarios

Training Options

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1-on-1 Training

On Request
  • Option Item Access to live online classes
  • Option Item Flexible schedule including weekends
  • Option Item Hands-on exercises with virtual labs
  • Option Item Session recordings and learning courseware included
  • Option Item 24X7 learner support and assistance
  • Option Item Book a free demo before you commit!
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Corporate Training

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  • Option Item Everything in 1-on-1 Training plus
  • Option Item Custom Curriculum
  • Option Item Extended access to virtual labs
  • Option Item Detailed reporting of every candidate
  • Option Item Projects and assessments
  • Option Item Consulting Support
  • Option Item Training aligned to business outcomes
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Course Reviews


  • Understand Business Analytics and R
  • Knowledge on the R language
  • Community and ecosystem
  • Understand the use of ‘R’ in the industry
  • Compare R with other software in analytics
  • Install R and the packages useful for the course
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio and Various GUI
  • Use the ‘R help’ feature in R
  • Knowledge about the worldwide R community collaboration.

  • The various kinds of data types in R and its appropriate uses
  • The built-in functions in R like: seq(), cbind (), rbind(), merge()
  • Knowledge on the various Sub-setting methods in R
  • Summarize data by using functions like: str(), class(), length(), nrow(), ncol() in R
  • Use of functions like head(), tail() for inspecting data
  • Indulge in a class activity to summarize data in R

  • The various steps involved in R for Data Cleaning
  • Functions used in R for Data Inspection
  • Tackling the problems faced during Data Cleaning
  • Uses of the functions like grep(), sub()
  • Coerce the data in R
  • Uses of the apply() functions.

  • Import data from spreadsheets and text files into R
  • Import data from other statistical formats like sas7bdat and spss into R
  • Package installation used for database import
  • Connect to RDBMS from R using ODBC and basic SQL queries in R
  • Basics of Web Scraping in R

  • Understanding the Exploratory Data Analysis (EDA)
  • Implementation of EDA on various datasets in R
  • Box plots
  • Understanding the cor() in R
  • EDA functions like summarize()
  • llist()
  • Multiple packages in R for data analysis
  • The Fancy plots like Segment plot and HC plot in R.

  • Understanding on Data Visualization
  • Graphical functions present in R
  • Plot various graphs in R like table plot, histogram, box plot
  • Customizing Graphical Parameters to improvise the plots
  • Understanding GUIs like Deducer and R Commander
  • Introduction to Spatial Analysis in R

  • Introduction to Data Mining in R
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms in R
  • K-means Clustering.

  • Data Mining: Association Rule Mining And Sentiment Analysis
  • Association Rule Mining in R
  • Sentiment Analysis.

  • Linear Regression in R
  • Logistic Regression in R

  • Anova
  • Predictive Analysis.

  • Decision Trees in R
  • Algorithm for creating Decision Trees
  • Greedy Approach: Entropy and Information Gain
  • Creating a Perfect Decision Tree in R
  • R Classification Rules for Decision Trees
  • Concepts of Random Forest in R
  • Working of Random Forest in R
  • Features of Random Forest in R
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Target Audience:

  • Individuals looking to develop their skills in data analytics
  • Professionals in the field looking to enhance their existing knowledge of business analytics
  • Data analysts and business analysts looking to learn R programming language for data analysis
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  • Basic understanding of statistics and data analysis concepts
  • Prior experience with programming languages is not required
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Benefits of the course:

  • Develop expertise in R programming language and its application to business analytics
  • Acquire in-demand skills for career growth opportunities
  • Work on real-world projects to build a strong portfolio
  • Gain practical experience through hands-on exercises and case studies
  • Access to dedicated mentorship and support
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Exam details to pass the course:

  • No exams are required to pass the course
  • Participants are required to complete all assignments and projects to receive a certificate of completion
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Certification path:

  • No certification is required to learn this course
  • Participants will receive a certificate of completion upon successfully finishing the course
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Career options:

  • Data Analyst
  • Business Analyst
  • Data Scientist
  • Business Intelligence Analyst
  • Marketing Analyst
  • Why should you take th

Why should you take this course from Skillzcafe:

Why should you take this course from Skillzcafe:
  • Bullet Icon Comprehensive course content developed by industry experts
  • Bullet Icon Hands-on learning through practical exercises and real-world case studies
  • Bullet Icon Access to dedicated mentorship and support
  • Bullet Icon Flexible learning options with online classes and self-paced learning modules
  • Bullet Icon Certificate of completion upon successful course completion


The Business Analytics with R Training course is a 60-hour program.

Prior experience with programming languages is not required, but a basic understanding of statistics and data analysis concepts is recommended.

The course covers a range of projects, including data cleaning and preparation, exploratory data analysis, hypothesis testing, and regression analysis. Projects are designed to be applicable to real-world business scenarios.

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