Mathematics for Data Science Probability Statistical Inference Programming in R Numerical Methods for Data Science

The main course objectives are: 1.Understand and analyze multidimensional data sets, including techniques for handling and interpreting data in multiple dimensions. 2. Gain proficiency in principal component analysis, a method for reducing the dimensionality of data while preserving its important features. 3. Explore various distance measures and joint metrics used to quantify similarities and differences between data points in multidimensional space. 4. Learn and apply multidimensional scaling techniques to visualize and understand the underlying structure of complex data sets. 5. Develop the skills to perform cluster analysis, a method for identifying meaningful groups within data based on similarity. 6. Study correspondence analysis and its application in exploring relationships between categorical variables in multidimensional data.

1. Multidimensional data sets 2. Principal component analysis 3. Distances and joint metrics 4. Multidimensional scaling 5. Cluster analysis 6. Correspondence analysis

Learning activities: Theoretical classes Practical classes Tutorials Team work Individual work of the student In-person evaluation tests Methodology to be used: Theoretical classes with support material available on Aula Global. Problem solving classes. Computational practices in computer rooms. Oral exhibitions Tutorial regime: Individual tutorials throughout the course.