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Presentation
Presentation
The aim of this course is for students to gain exposure to multivariate data and the main tools for analyzing it in academic and real-world contexts, using computational resources such as R.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
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Year | Nature | Language
Year | Nature | Language
2 | Mandatory | Português
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Code
Code
ULHT6638-5699
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
S1. Random vectors: vector of means and covariance matrix S2. Multivariate normal distribution and hypothesis tests S3. Dimensionality reduction methods: principal component analysis, factor analysis and multidimensional scaling S4. Hierarchical and non-hierarchical cluster analysis methods: k-means and k-medoids
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Objectives
Objectives
L1. Characterize and correctly interpret multivariate data L2. Identify the appropriate multivariate data analysis techniques for each type of problem and the nature of the data L3. Know the multivariate normal distribution and its properties L4. Apply multivariate data dimensionality reduction techniques L5. Apply cluster analysis techniques L6. Use computer resources such as R
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Teaching methodologies and assessment
Teaching methodologies and assessment
The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises.
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References
References
Apontamentos das aulas e textos de apoio facultados
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Office Hours
Office Hours
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Mobility
Mobility
No
