R For Marketing Research and Analytics Training
Rated 4.0/5 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

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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.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.

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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 trainer was very knowledgeable and gave all the necessary tips to attend for the test. However, the training material would've been good if some more examples we're given... Otherwise all good.

Attended workshop in January 2018

The training was very engaging and informative. The scrum framework in general and the Product Owner role in detail were explained by actually applying the framework to the training. Though the attendees were from many different professional backgrounds and had different knowledge levels about Scrum, the trainer was able to touch upon each and every question that was asked. I would definitely recommend taking the training with Knowledgehut.

Attended workshop in July 2018

The best professional training I have ever attended! A lot of positive energy and concepts from the trainer "Stuart Mitchell". Also, the facilities and arrangements were excellent.

Attended workshop in July 2018

This training, that I attended was with around 70-80 people. the trainer Madhur, ho man he is so awesome, he helped us correct our thought on Scrum, he never made us feel that we were in a training it was more of a session where we were changing out thought and hence learning with ease. It was a really well-organized session, and at a nice hotel. it was a 100% result. now I am a CSM. Thank you.

Attended workshop in July 2018
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Srividya Jana

Assistant Manager Operations at Deutsche Bank from Hyderabad, India
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Steffen Alm

Development Coordinator at Contecs Engineering Services from Berlin, Germany
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Arindam Das

Software Engineer at GlobalLogic from Chennai, India
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Praksah Sharma

Team Lead at Accenture from Bangalore, 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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