- Execute 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 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
- Evalute, communicate and defend results of data analysis
- Solve real-world data analysis problems following a well-structured process
Required prerequisite knowledge
|Project work and oral presentation||1/1||A - F|
Both project and oral examination must be done before final grade is given. Each group member can receive a different grade based on their performance during the oral examination.
If a student fails the projectwork , he/she have to take this part again next time the subject is lectured.
- Course coordinator
- Antorweep Chakravorty
- Programme coordinator
- Nina Egeland
- Head of Department
- Tom Ryen
Method of work
Computer Science - Master's Degree Programme
Last updated: 14.07.2020