URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2IAB1&collegejaar=2025&taal=enDescription
Course setup- Two lectures of two hours per week
- One lab sessions of two hours per week
- Online homework, consisting of weekly theory and programming exercises in weeks 1-6 of the course
- A challenge-based component integrated in an assignment
- A written exam
Grading
- Written exam – 90% of the final grade
- Assignment – graded using the designations Good (GO), Pass (PA), Fail (FL) or No Show (NS)
- Online homework – 10% of the final grade
Students must obtain PA (Pass) or GO (good) for the assignment to be able to pass the course. Students who got a FL (fail) or a NS (no show) for the assignment, will be awarded a final grade of NMR (not met requirements).
Objectives
General learning goals
- Use basic statistical concepts and techniques and perform appropriate statistical tests
- Choose and apply suitable visualization techniques
- Analyze and model data using linear regression, clustering, decision tree mining and association rules learning
- Read and make simple database schemes and simple queries to a database.
- Clean data, choose and apply data transformations, data reduction, and data discretization
- Understand, interpret, and document data and information in the context of realistic scenarios, structure open problems along the phases of the data life cycle, formulate hypotheses, incorporate ethical considerations and reflect on analysis trengths and limitations
- Use tools for implementing data engineering tasks (Python with Jupiter Notebooks) in a structured way
- Choose and communicate interesting findings in the language understandable for their end user (visually or textually).