Correspondence Analysis for Brand Personalities in R

Learn how to apply Correspondence Analysis to real-world brand personality data using R programming language.

  • Schedule

    9 – 11 May 2023

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

This course provides an introduction to Correspondence Analysis, a popular multivariate data analysis technique used to explore the relationship between categorical variables. In this course, we will focus on the use of CA for studying brand personalities, which refer to the set of human characteristics associated with a brand. The course will provide hands-on experience with the R programming language, one of the most widely used languages in data science, and its various packages for conducting CA. 

By taking this course, students will learn how to apply Correspondence Analysis to real-world brand personality data using R programming language. This will help students develop a practical understanding of the method and how it can be used in data analysis. Throughout this 3-day workshop, students will learn the basics of CA, including data preparation, interpretation of results, and visualization techniques. Whether students are interested in pursuing a career in data analysis or simply want to understand the relationship between two categorical variables, this course is an excellent starting point.

Upon completion of this workshop, you will be able to:

  • Understand the basics principles and concepts of Correspondence Analysis
  • Learn how to use R programming language to conduct CA
  • Develop skills in interpreting and visualizing Correspondence Analysis results
  • Apply CA to real-world case studies of brand personalities and competitors


  • Description of the workshop’s objectives
  • A brief explanation of correspondence analysis and area of implementation
  • Description of the workflow, tools, and setup
  • What is R?
  • Working with RStudio environment
  • Packages and loading libraries
  • Basic R data structure
  • Create contingency table from two categorical variables
  • Ballon plot
  • Mozaic plot
  • Concept of machine learning
  • Unsupervised vs Supervised Learning
  • Basic theory of CA: Chi-Square Test & Variance
  • Row Variables/Row Components
  • Column Variables/Column Components
  • Interpreting Biplot of CA


This testimonial video is taken after our previous Online Data Science Series: Time Series Analysis for Business Forecasting.


Our learning format is online-interactive, you will feel the interactive experience as if you were present in a physical classroom. You can access the class using your Zoom account on pre-defined dates.


    Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.


    Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.


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Workshops in this series are tailored to casual programmers and non-programmers that are taking their first steps into data science. It assumes no prior knowledge or academic background, and attendees will be introduced to the beautiful art of writing R / Python code to produce data visualization and build machine learning models. The workshop has a gentle learning slope that is designed with non-technical professionals and academics in mind.

Yes, you can still attend the workshop as it is a beginner-friendly workshop.

Our system will send you an email containing a link and details to join a Google Classroom.

Online learning will be conducted via Zoom.us, Link to join the Zoom Class will be announced via Google Classroom.

Learning materials can be obtain via Google Classroom

Yes, you will receive a certificate of completion.


Kinanty Tasya

Kinanty Tasya Octaviane is a Data Science Instructor at Algoritma Data Science School with expertise in Statistics, Research and Machine Learning implementation. Kinan’s passion for teaching and mentoring has led her to be involved in numerous data science projects, providing guidance and expertise to some of the biggest names in the industry.

Kinan has worked closely with Bank Central Asia and ADIRA to provide consultative data science training. Her knowledge and expertise have helped these companies make informed and strategic business decisions based on data.