Introduction to Data Science (DAT540)
The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.
Course description for study year 2023-2024. Please note that changes may occur.
Semester tution start
Number of semesters
Language of instruction
- Execute/Develop tools to load, parse, clean, transform, merge, reshape, and store data.
- Compare regular Python, NumPy, and Pandas data structures and choose one for the given problem. Use the IPython shell and Jupyter notebook for exploratory computing.
- Execute/Develop simple machine learning or data mining algorithms.
- Organize data analysis following CRiSP-DM and Data Science Process
- Build engaging visualizations of data analysis using matplotlib
- Optimize data analysis applying available structure and methods
- Evaluate, communicate and defend results of data analysis
General qualifications :
- Solve real-world data analysis problems following a well-structured process
Required prerequisite knowledge
Project work and written exam
|Form of assessment||Weight||Duration||Marks||Aid|
|Project work in groups||3/5||Letter grades|
|Written exam (Multiple Choice)||2/5||3 Hours||Letter grades|
Project Work in GroupsThe project is completed in groups. Project work is to be performed in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the lecturer.A project report, including source code, contributes to the grade. If a student fails the project work, he/she has to take this part again the next time the subject is lectured.