Data deep dive

Postgraduate course

ECTS credits
20
Course code
DSC316
Teaching language
Engelsk
Resources
Schedule

Course description

Objectives and Content

The course aims to develop a deeper understanding of at least one (or more) data types used in the real world. It contains methods from data collection, data preparation, data analysis, understanding biases in data and how to reduce them, as well as interpretation of results.

This course is linked to the project course DSC399K and will convey a deeper understanding of data types used in the master's project.

Learning Outcomes

Upon completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student should be able to

  • explain one type of data thoroughly, including the entire data-generating process, steps in data preparation, data analyses, and resulting biases in data and interpretation.
  • compare different approaches for the analysis of the selected data type.

Skills

The student should be able to

  • preprocess and manage real world data sets
  • analyze and visualize real-world data sets

General competence

The student should be able to

  • create written scientific reports as well as critically assess specialist literature.
  • hold oral presentations on their own work.
  • reflect on central ethical and scientific issues in own and others' work

ECTS Credits

20 ECTS

Level of Study

Master

Semester of Instruction

Autumn

Place of Instruction

Bergen

Required Previous Knowledge
None

Recommended Previous Knowledge
None

Credit Reduction due to Course Overlap
None

Access to the Course
The course is open for students with admission to our study programme in Data Science (integrated Master’s), 5 years

Teaching and learning methods
Lectures, presentations, project

Compulsory Assignments and Attendance
Oral presentation. Must be approved

Forms of Assessment

The subject uses the following form of assessment:

  • Compulsory written report

Both the compulsory written report and oral presentation will be on data type or data types containing the following aspects for each data type:

  • data collection
  • data preparation
  • data processing
  • data analysis
  • interpretation of results

Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Assessment Semester
Autumn

Reading List
The reading list will be available within July 1st for the autumn semester and December 1st for the spring semester.

Course Evaluation
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department

Examination Support Material
None

Programme Committee
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB, or contact studieveileder@ii.uib.no.

Course Administrator
The Faculty of Science and Technology represented by the Department of Informatics is the course administrator for the course and study programme.

Course description

Contact information

Study adviser:

studieveileder@ii.uib.no

Department of Informatics