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 2024-2025. Please note that changes may occur.
Course code
DAT540
Version
1
Credits (ECTS)
10
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
Content
Learning outcome
Knowledge :
- 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.
Skills:
- 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
Recommended prerequisites
Exam
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 |
Written exam is digital.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.