Week 10

SVD and Applications: MCA and CCA

Important

• Session I Buffon (103A) Wednesday 13h30-15h
• Session II Sophie Germain (2012) Friday 9h00-10h30
• Session II bis Olympe de Gouges (130) Friday 15h45-17h15
• Calendar

Very important

During session II bis (Friday afternoon), each pair will have 5 minutes to present their results from Homework I.

Each pair will have to prepare one or two slides and ashort pitch commenting the pictures.

Labs

• Lab 10 Multiple Correspondence Analysis as application of extended SVD
• Lab 10 bis Canonical Correspondence Analysis

We still rely on the previous labs

• Lab 1 - Introduction to R and RStudio
• Lab 2 - Introduction to Data Visualization using Gapminder dataset
• Lab 3 - Working with dplyr
• Lab 4 Univariate categorical variables
• Lab 5 Univariate numeric variables
• Lab 6 - Linear Regression I
• Lab 6 - Linear Regression II
• Lab 7 - Linear Regression II
• Lab 8 Principal Component Analysis as an application of SVD
• Lab 9 Correspondence Analysis as an application of extended SVD

Further work

Review the content of the two labs. Work out every part you do not already know. Report an issue if you are unhappy with the proposed solutions/hints.

Further reading

• Bin Yu and Rebecca Barter, Veridical Data Science
• R for data science
• Advanced R
  • S3 classes
  • S4 classes

Logistics

: you will work with the R programming language in this course.

You need either to install R, RStudio and VS Code on your computer, or to use your posit-cloud account.

Install Quarto on your computer to render the .qmd files.

Please follow the instructions here to install R, RStudio, VS Code, and Quarto or to access posit-cloud.

Please activate your ENT account (follow the instructions on Moodle). You will be able to access the PostGres server.

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