ID2214 Programming for Data Science 7.5 credits
Programmering för data science
The course covers the following topics:
- Syntax and semantics for programming languages that are particularly suited for data science, e.g. Python, Julia.
- Routines for importing, combining, transforming and selecting data.
- Algorithms for handling missing values, discretisation and dimensionality reduction.
- Algorithms for supervised machine learning, e.g. naïve Bayes, decision trees, random forests.
- Algorithms for unsupervised machine learning e.g. k-means clustering.
- Libraries for data analysis.
- Evaluation methods and performance metrics.
- Visualising and analysing results.