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Categorical Data Analysis Course

Categorical Data Analysis Course
from 4 to 360h flexible workload
valid certificate in your country

What will I learn?

This Categorical Data Analysis Course provides practical skills to handle real-world categorical variables with confidence. You will learn to summarise and visualise categories, construct and interpret contingency tables, perform chi-square tests, and use logistic regression for binary outcomes. You’ll clean web session data, prepare variables, and deliver clear, actionable insights and recommendations for online retail decisions.

Elevify advantages

Develop skills

  • Logistic regression mastery: model binary outcomes and interpret odds ratios quickly.
  • Chi-square and Fisher tests: assess categorical associations confidently.
  • Contingency table analytics: compute effects, Cramér’s V, and visualise patterns.
  • Retail data prep skills: clean, encode, and validate e-commerce session data.
  • Executive-ready reporting: turn categorical findings into clear, actionable insights.

Suggested summary

Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload
Workload: between 4 and 360 hours

What our students say

Your classes are perfect. I got the one-year package and finally have the chance to follow several topics I'm interested in without having to switch platforms... thank you for everything you do, I've already recommended you to others...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons get straight to the point and how I can switch chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the way the videos are presented and transcribed, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast and easy to use. The variety of content and the extra videos really help with learning.
Giulio Carlo
Giulio CarloPrompt Engineering Student

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