Course: Data Science 4 credits: 5

Course code
BFVM23DATASCNC4
Name
Data Science 4
Study year
2023-2024
ECTS credits
5
Language
English
Coordinator
F. Feenstra
Modes of delivery
  • Lecture
Assessments
  • Graph Theory - Computer, organised by School
  • Multivariate Analysis - Assignment

Learning outcomes

 

  1. You can explain whether and how a life science data set corresponds to a graph  

  1. You can implement available graph-based algorithms to process data  

  1. You can explain whether and how a life science data set can be described by a multiset multilinear model  

  1. You can implement a specific multiset multilinear model for integrative modelling of data 

Content

This course introduces to relational models of data, with a focus on graphs and multilinear models. The course begins with an overview of graph theory, including the concepts of graphs, trees, adjacency matrix, directed acyclic graphs, paths and cycles, tree search, shortest path, random walks, Markov chains, sorting, and algorithmic complexity. The course then delves into the analysis of complex datasets using multivariate linear models, including multiple linear regression, partial least squares, canonical correlations, singular value decomposition, and principal component analysis. 

Throughout the course, students will learn various methods for investigating and assessing relational features and complex datasets using graphs and multilinear models, with applications to the life sciences. Students will also gain practical experience through programming assignments and data analysis projects.  

Included in programme(s)

School(s)

  • Institute for Life Science & Technology
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