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Business Analytics Training with R

Course Introduction

This Data science course will empower you with machine learning and predictive analytics, data mining, data manipulation, Data ingestion & data visualization techniques.

You will learn to apply these techniques using R programming language in real-life case studies. Case studies are the best way to master any skill.

We have included small to reasonable sized case studies to make your learning closest to the industry needs.

Who should attend this course?

This course is designed for professionals, who wish to make a career in the existing field of Business Analytics (also referred to as data science or data analytics). You should attend this course, if you are:

  • • Any professional, who has interest in problem solving and willing to learn some programming (R is not as difficult as Java)
  • • A business analyst, who has been in the industry for a few years and wishes to up-skill for career growth
  • • A developer who wishes to enter the exciting world of Data science

We have another course on Business Analytics, want to know more about:

If you are looking to have a Business Analytics Training in Python, drop us an email for a special pricing at

What are the pre-requisites for this course?

You don’t need to have a programming back ground to do this course.

Course Highlights

Case-studies based training

Learn from Industry Professionals

Certification based on PAT

LMS Portal

Recorded Sessions

32-Hrs of

Course Syllabus

Introduction to business analytics

• What is analyt ics

• Need of Analytics

• Business analytics vs business analysis

• Business intelligence vs Data Science

• Data Analyst Vs Business Analyst

• Types of Analytics

• Tools for Analytics

Industry use cases

• Retai l Indust r y

• Heal thcare Indust r y

• Market ing Anal yt ics

• Web Analyt ics

Introduction to R Language

• What is R?

• Data science & R

• Components of R

• Instal l ing R

• Using command l ine in R

• Int roduct ion to R Studio ( IDE)

• Finding Help & solving issues in R

R Language Constructs

• Data t ypes in R

• Program St ructure in R

• Flow Cont rol : For loop

• I f condi t ion

• Whi le condi t ions and repeat loop

• Debugging tools

• Concatenat ion of Data

• Combining Vars , cbind, rbind

• Sappl y, appl y, tappl y funct ions

• Bui l t - in funct ions in R

R data structures

• Int roduct ion to Data St ructure in R

• Vectors

• Lists

• Scalars

• Data Frames

• Mat r ices

• Ar rays

• Factors

• Use of data s t ructures in di f ferent condi t ions

• Advantage of us ing a par t icular approach

File Handling in R

• Fi le operat ions in R

• Reading f i le

• Wr i t ing to a f i le

• Impor t ing and expor t ing a f i le

Using R for data visualization

• Fi le operat ions in R

• Reading f i le

• Wr i t ing to a f i le

• Impor t ing and expor t ing a f i le

Using R for data visualization

• What is Data Visual izat ion?

• Data Visual izat ion tools in the market

• Using graphical funct ions in R for data visual izat ion

• Line Plots

• Bar Plots

• Bar Plots for Populat ion

• Pie char t

• Plot t ing wi th base graphics

• Plot t ing wi th Lat t ice graphics

• Plot t ing and color ing in R

Basics of statistics

• Bas ics of stat ist ics

• Concept of sample & populat ion

• Conf idence Interval

• Quant i tat ive Vs Qual i tat ive Anal ys is

• Normal & Standard Deviat ions

Statistics with R

• Comput ing basic stat ist ics

• Bus iness Hypothes is Test ing concepts

• Bas ics of stat ist ical model ing

• Logist ic Regression

• Compar ing means of two samples

• Test ing a cor relat ion for signi f icance

• Test ing a propor t ion

• Classical tests ( t ,z,F)

• Anal ys is of var iance (ANOVA)

• Summar izing Data

• Data Munging Bas ics

• Cross tabulat ion

Class Exercises & Assignments

Linear Regression

• What is regression

• Dependenc y of var iables

• What is l inear regress ion

• Understand wi th examples

• Least Squares Model (Obtaining the Bes t f i t l ine)

• Out l iers and Inf luent ial Observat ions

• Class Exercise

Logistics Regression

• What is Logist ics regression

• Understanding the model

• Logist ics regress ion & class i f icat ion problems

Class Exercise & Case study

Predictive Analytics Part-I

• What is Naïve Bayes Algor i thm

• Use case (Scenar io of usage)

• Why another model for class i f icat ion

• Understanding the algor i thm

Case Study

Predictive Analytics II

• What is dec ision t ree

• Need for dec ision Tree

• Use case (Scenar io of usage)

• How does i t Work?

Case study

Predictive Analytics III

• What is clus ter ing

• Use case (Scenar io of usage)

• Cluster ing Models

• K-means c luster ing

• Case study

• Agglomerat ive hierarchical Method

• Divisive hierarchical Method

• Dens i t y based cluster ing method (DBSCAN)

Predictive Analytics IV

• What is assoc iat ion anal ys is

• Need of assoc iat ion anal ys is

• Use cases (Scenar ios where used)

• I tem sets

• Associat ion Rule – Af f ini t y Anal ysis

• Suppor t & conf idence measure

• The Apr ior i pr inc iple

Choosing the model

• How to choose the r ight model

• Framework and parameters for f inding the best f i t

• Put t ing i t together - The f inal case s tudy

Course Preview

Introduction to
Business Analytics

Business Analytics, Data Science &
R – Programming

Upcoming Batches Schedule


Sat - Sun ( Online Class )
07:30 AM - 09:30 AM ( IST )
2,600 Discount


Sat - Sun ( Online Class )
07:30 AM - 09:30 AM ( IST )
2,600 Discount


Sat - Sun ( Online Class )
07:30 AM - 09:30 AM ( IST )
2,600 Discount

Business Analytics Vs Data Analytics

Business Analytics and data analytics are two of the most used terms. Are they different or they used to refer to the same concept?

Business Analytics and Data Analytics are essentially the same and are used to refer to the same field and domain. They essentially refer to the science of making sense of data for creating value for businesses. Use of statistical modelling and predictive analytics techniques are the cornerstones of analytics along with other types of analytics like descriptive analytics, prescriptive analytics and diagnostic analytics.

Free Tutorials

Free Selenium Tutorials

What is Data science?

An article written by our Data science expert explaining the role of a data scientist using the case of “People You may know” feature on Facebook/LinkedIn. Written in a simple to understand manner, it will be a good start for you. Read More

Free Selenium Tutorials

Understanding data science (Video)

This is a recording of a webinar, conducted by a Data scientist working with Adobe Systems. In this webinar, he provides a thorough understanding of the data science, the project framework and techniques used in data science. Watch Video

Free Selenium Tutorials

What is machine learning? (Video)

In this webinar recording, the speaker explains the concepts of machine learning and artificial intelligence. A useful insight into the world of Machine learning. Watch Video

Free Selenium Tutorials

What is descriptive analytics?

Descriptive analytics is one of the types of Business analytics. In this article, you will learn about the types of analytics specifically descriptive analytics, with the help of an example. Read More

Faculty & Technical Support

As a student you can ask questions with the trainers even after the classes. Simply send an email to You will get the answer as soon as possible.
Please note that our trainers are working professionals and sometimes may be busy with their office work.

Case Studies

Financial Industry Case Study


This case study is a real-life case study and will provide you the application of data science in real-life scenario.

Marketing Case study


Marketing is one of the domains where analytics play an important role. Analyzing customer data for customer profiling and segmentation and many more is an important arsenal in the hands of a marketing manager.

Retail Industry Case study


Retail industry always deals with large amount of transactional data on an hourly basis and the industry analyzes the data and applies the data science principles to understand customer behavior in order to earn customer loyalty. In this case study, we would be looking at a business scenario and how it is done?

E-Commerce case study


E-commerce industry has undergone rapid changes in the last decade or so and that’s for good. Amazon has been a pioneer in the using predictive analytics to suggest books to visitors of the website. In this case study, we would be looking at how e-commerce industry has used data science principles to win over customers.

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