Introduction
The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.
Innhold
The ability to create, manage and utilize data has become one of the most important challenges for practitioners in almost all disciplines, sectors, and industries. In this course, students become familiar with basic tools and processes used in Data Science. Students work through the whole data lifecycle from loading, through cleaning and modeling, to storing the data. The work is performed using Python stack consisting ia of: IPython, NumPy, Pandas, Matplotlib, and Jupyter Notebooks. Students learn to structure their work using CRISP-DM and Data Science Process (Ask, Get, Explore, Model, Communicate and Visualize).
Læringsutbytte
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
10 ECTS in Programming, Databases or Software Engineering related courses.
Recommended prerequisites
Grunnleggende programmering (DAT120), Sannsynlighetsregning og statistikk 2 (STA500)
Exam
Prosjektarbeid og skriftlig eksamen
Prosjektarbeid i gruppe
Weight 3/5
Marks Bokstavkarakterer
Exam system Canvas
Skriftlig eksamen (Multiple Choice)
Weight 2/5
Duration 3 Timer
Marks Bokstavkarakterer
Exam system WISEflow
Written exam (multiple choice) is digital.
Project Work in Groups
The 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.
Arbeidsformer
The work will consist of 6 hours of lecture, scheduled laboratory, supervised group work per week. Students are expected to spend an additional 6-8 hours a week on self-study, group discussions, and development work.
Bruk av KI som studiestøtte i studiearbeidet
AI tools may be used in DAT540 as a learning aid, but must never substitute for the data-science competence the course builds — working through the whole data pipeline and understanding and explaining the code and analysis yourself is precisely what you must master. Productive uses include asking AI to explain a concept (e.g., how the CRISP-DM steps fit together, the difference between NumPy and Pandas structures, or what a simple machine-learning algorithm does), to review or debug code you have written yourself, or to suggest tests and edge cases. Any use of AI must always be disclosed in your submission as specified in the syllabus, and you remain responsible for critically evaluating whatever the AI produces.
Open for
Enkeltemner på masternivå ved Det teknisk-naturvitenskapelige fakultet
Data Science - master
Computational Engineering - master
Datateknologi - master
Industriell økonomi - master
Utveksling ved Det teknisk-naturvitenskapelige fakultet
Admission requirements
Must meet the admission requirements of one of the study programmes the course is open for.
Course assessment
The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.
The course description is retrieved from FS (Felles studentsystem). Version 1