R For Marketing Research and Analytics Training
Rated 3.0/5 based on 182 customer reviews

R For Marketing Research and Analytics Training

A comprehensive course for data analysts looking to develop or improve skills in R for marketing applications

Contact Course Advisor schedules

Modes of Delivery


Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.


This course provides an introduction to marketing research and analytics using R. While the course assumes no particular domain, techniques and examples are provided for a variety of disparate verticals, including banking, retail, and telecommunications. The first day of the course covers an introduction to R and its applicability to marketing analysis, with particular emphasis on translating existing solutions from SAS to R. The second day provides a gentle introduction to marketing analytics topics in R such as brand perception, principal component analysis (PCA), exploratory factor analysis (EFA), multidimensional scaling (MDS). Finally, the last day of the course covers real-world applications such as clustering and classification, market basket analysis, and choice modeling.

On successful completion of the course, you will receive a Course Completion Certificate from KnowledgeHut.

What you will learn:
  • Map understanding of data analytics techniques from SAS to R
  • Marketing analytics techniques in R
  • Real-world applications of marketing analytics in R
You will also get:
  • Comprehensive, downloadable courseware
  • In-depth case studies for better retention
  • Course completion certificate
  • 1 credit per hour of learning

Key Features

3 days of quality interactive learning
Course completion certificate
Learn to leverage the power of R for marketing research
Practical and efficient methods for applying R to analyse data
Downloadable comprehensive courseware
Hands-on exercises to cement your learning


  • Comparison to SAS or other statistical packages
  • Why R and When R
  • Overview of R language
  • Basic Objects
  • Data Frames
  • Loading and Saving Data
  • Visualizing Data


  • Sample functions
  • Regression Analysis of Exam Grades
  • Scalars, Vectors, Arrays, and Matrices introduction
  • Vector operations
  • Filtering
  • Vector functions
  • Vector elements and equality
  • Creating Matrices and Arrays
  • Matrix Operations
  • Higher-dimensional Arrays

Q&A (15 min): (Day 1)

LUNCH (60 min): (Day 1)

  • Participants will be asked to create Vectors, Matrices, and Arrays
  • Participants will be asked to solve a simple regression problem
  • Participants will be asked to translate from SAS to R for basic data objects
  • Creating Lists and Data Frames
  • List and Data Frame Operations
  • Accessing List and Data Frame elements
  • Functions on Lists and Data Frames

BREAK (15 min): (Day 1)

  • Functions for Statistical Distributions
  • Linear Algebra Operations on Vectors and Matrices
  • Set Operations
  • Participants will be asked to apply statistical analysis to a dataset
  • Participants will be asked to apply linear algebra operations to a dataset
  • Participants will visualize the results of their statistical analysis
  • Simulating Data
  • Functions to Summarize a Variable
  • Summarizing Data Frames
  • Single Variable Visualization
  • Lattice vs ggplot2

BREAK (15 min)

  • Simulating Customer Data
  • Simulating Satisfaction Survey Data
  • Simulating Non-Response Data
  • Scatterplots and Associations Between Variables
  • Correlation testing

Q&A (15 min)

LUNCH (60 min)

  • Participants will be asked to simulate a customer dataset or use an existing one
  • Participants will be asked to do scatterplots between the variables of the dataset
  • Participants will be asked to run correlation tests on the dataset
  • Simulating Consumer Segment Data
  • Finding Descriptives by Group
  • Participants will be asked to perform association analysis on survey response data
  • Participants will be asked to compare groups using descriptives
  • Chi-square testing
  • Binomial testing and Confidence intervals
  • P and T-testing

BREAK (15 min)

  • Fitting linear models with lm
  • Fitting linear models with multiple predictors
  • Overfitting
  • Brand perception and rescaling data
  • Principal Component Analysis (PCA)
  • Exploratory Factor Analysis (EFA)
  • Multidimensional Scaling (MDS)
  • Collinearity

Q&A (15 min)

LUNCH (60 min)

  • Clustering using kmeans and other techniques
  • Classification using naïve Bayesian and random Forest
  • Identifying Potential Customers

BREAK (15 min)

  • Association Rules
  • Non-Transactional Data
  • Choice Modeling
  • Customer Heterogeneity
  • Participants will be asked to create a customer segmentation model from simulated or existing data

Our Students See All

The workshop was great. I learned the methodologies well and passed the exam.

Attended workshop in January 2018

Comprehensive training from the ground up, setting me up for a successful certification and effective adoption at the workplace.

Attended workshop in January 2018

I attended CSPO (Certified Scrum Product Owner) training session by Knowledgehut in Bangalore on Dec 21-22. The trainer did an excellent job of incorporating everyone’s experience and his experience in all the companies he worked on earlier. He even answered all doubts/questions separately, did not leave a single question unanswered.The facilities in the venue (Davanam Sarovar, Madiwala) was also managed very well.

Attended workshop in December 2017

I attended CSPO training in Goregaon(W) on January 9th&10th by prof. Sekhar burra. It was a great learning experience. Sekhar's approach to this training was amazing as he cleared in the start what to expect from this course and how it will help us streamline the process followed in our organization. It's very tough to cover all the things in 2 days but the way Sekhar manage it is really commendable. I would recommend this course to all the aspirants.

Attended workshop in January 2018
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Devraj Choudhuri

Project Manager at Office of the Secretary of Defense from Washington, DC, United states
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Navin Pothan

Executive Director at JP Morgan from New York, NY, United States
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Anupam Bhandari

Senior Associate Consultant at Infosys Limited from Bangalore, India
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Ashish Patil

Product Manager at Network18 Media & Investments Limited from Mumbai, India

Frequently Asked Questions

Candidates are required to have familiarity with data analytics in SAS or similar statistical software packages, and knowledge of basic machine learning concepts.

The open source language R is becoming a popular tool for carrying out data analysis, as it has several advantages over traditional tools like SAS. Widely considered to be the most comprehensive statistical analysis package available, R incorporates all of the standard statistical tests, models, and analyses, and also offers a simple language for managing and analysing data.

This course, conducted by industry experts, will give you deep insights into the use of R for predictive analysis and will help you to gain exposure to real-world case studies. Get set for a rewarding career in the field of marketing research and data analytics!

On successful completion of the course, you will receive a Course Completion Certificate from KnowledgeHut with Credits (1 credit per hour of training).

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding). Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details:

Please send in an email to, and we will answer any queries you may have!

Candidates are required to have familiarity with data analytics in SAS or similar statistical software packages, and knowledge of basic machine learning concepts.

This course is apt for

  • Digital Marketers
  • Market Researchers and
  • Product Managers, among others

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