Level of Study
This course has a limited capacity, enrolment is based on application. Application deadline is Thursday in week 33 for the autumn semester. Please see this page for more information: https://www.uib.no/en/matnat/53431/admission-courses-limited-capacity
Objectives and Content
This course teaches the first steps into data science using the program package R. The course starts with data import from various sources into R, and presents different structures allowing to structure and transform data. Moreover, focus lies in data visualization approaches by means of various R packages. Basic modelling techniques will also be introduced, but without going into the underlying mathematical theory. Last, ethical aspects of data handling will be addressed.
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
The student prossesses knowledge about
- different real-world data sources
- the statistical program R and various of its packages
The student is abe to
- import data into R and clean/prepare data for graphical analysis
- transform and visualize data
- manage data and obtain reproducible results
- identify potential ethical problems related to data treatment
The student is able to
- wrangle, vizualize and explore previously unknown data from various sources
- easily adopt to programming challenges with R in more advanced courses
Required Previous Knowledge
Recommended Previous Knowledge
Credit Reduction due to Course Overlap
INF161: 5 ECTS.
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences.
Teaching and learning methods
On average 2h of lecture and 4 hours of computer lab sessions per week.
Compulsory Assignments and Attendance
Two compulsory assignments. Compulsory assignments are valid for 0 subssequent semesters.
Forms of Assessment
The forms of assessment are: Portfolio assessment
- compulsory assignment 1 (50 % of the grade)
- compulsory assignment 2 (50 % of the grade)
Only in teaching semester.
Type of assessment: Portfolio assessment
- Examination system
- Digital exam