Norwegian

Course description

• Course content
• Learning outcome
• Admission
• Prerequisites

• Overlapping courses
• Teaching
• Examination

Course content

The course provides insight into selected contemporary relevant topics within Data Science. Students gain practical experience with data analysis and industry relevant algorithms and technologies for data analysis. Course content follows developments in the field.

Learning outcome

After completing the course you will have:

• a general overview of the most contemporary relevant and industry relevant technologies and methods in Data Science;
• theoretical knowledge and practical experience in data analysis and applied statistics;
• experience with the use of distributed systems for storage and / or processing;
• the expertise to carry out a real project in data analysis, from data collection to data-driven decision-making guidance.

Admission

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

Prerequisites

Formal prerequisite knowledge

In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:

• Mathematics R1 (or Mathematics S1 and S2) + R2

And in addition one of these:

• Physics (1+2)
• Chemistry (1+2)
• Biology (1+2)
• Information technology (1+2)
• Geosciences (1+2)
• Technology and theories of research (1+2)

The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies (in Norwegian).

Recommended previous knowledge

• INF1000 - Introduction to object-oriented programming / INF1100 - Introduction to programming with scientific applications, 
• INF2220 - Algorithms and data structures

and / or:

• STK1100 - Probability and statistical modelling, 
• STK1110 - Statistical methods and data analysis 1,
• STK2100 - Machine learning and statistical methods for prediction and classification / STK2120 - Statistical Methods and Data Analysis 2 (discontinued), 
• STK3100 - Introduction to generalized linear models

Overlapping courses

10 credits overlap with STK-INF4000 – Selected Topics in Data Science (discontinued)

Teaching

3 hours of lectures, 2 hours of open group and oracle services when needed.

Upon the attendance of three or fewer students, the lecturer may, in conjunction with the Head of Teaching, change the course to self-study with supervision.

Examination

Mandatory project tasks and assignments/presentations.

A project which counts 60% and a final exam counts 40%. Both parts must be passed and in the same semester. In order to sit the exam, all the compulsory assignments in the subject must be approved.

Final project and oral or written examination. The form of examination will be settled by 15 February.

Language of examination

Subjects taught in English will only offer the exam paper in English.

You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Explanations and appeals

• Explanation of grades and appeals

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.