MAE4000 – Data Science

Schedule, syllabus and examination date

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Course content

In this course you will learn the core concepts and techniques that function as foundations for formulating and implementing successful data-based analysis strategies to perform evidence-based research.

The course covers the following seven key topics

  1. Randomness and Probability
  2. Data Management
  3. Descriptive Statistics and Representations (i.e., plots, tables, diagrams)
  4. Statistical Inference
  5. Statistical Research Design
  6. Statistical Models with focus on regression-based techniques
  7. Data Analysis Ethics

Learning outcome


  • realize the advantage of evidence-based decisions and the dangers inherent in acting on assumptions not supported by evidence
  • recognize the challenges with respect to data collection, data quality, and alignment between research questions and the data
  • recognize that variability and uncertainty are ubiquitous


  • run basic data analysis techniques using the open source statistical software environment R


  • perform and communicate basic data analyses taking into consideration
    • randomness and distributions
    • patterns and deviations (fit and residual)
    • mathematical models for patterns
    • model-data dialogue (diagnostics)


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.

All students in the Assessment, Measurement and Evaluation Master program have equal access to the course. Other qualified applicants may be considered if there is capacity.


This course combines lectures and computer labs with data analysis tasks in statistical software environments.

Obligatory course components:

  • 80% attendance requirement for the lectures
  • computer lab participation and completion of computer lab exercises


The exam consists of a 4 hour individual written examination covering all course material and topics.

You need to have successfully completed the obligatory course components before being allowed to sit the exam. If you do not fulfill these requirements, you must submit a written request to apply for an additional assignment prior to sitting the exam. The application must document stated reasons for absence beyond your control.

Previous exams

Digital examination

The written examination is conducted in the digital examination system Inspera. You will need to familiarize yourself with the digital examination arrangements in Inspera.

Read more about written examinations using Inspera.

Examination support material

You are allowed to bring and consult your own "cheat sheet". The cheat sheet can contain whatever course contents you find useful for yourself, but the sheet does need to fulfill the following requirements:

  • 1 page A4-format
  • Hand-written contents
  • Inside a plastic cover

You can write on both sides of the sheet of paper. Anything outside the requirements (e.g., a typed page, 2,5 pages or even a copy of the original), is not be permitted and will be taken away.

Language of examination

The examination text is given in English, and you submit your response in 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

Resit an 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.


In accordance with the UiO quality assurance system, the course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Periodic evaluation of MAE4000 Data Science Autumn 2018

Facts about this course






Every autumn


Every autumn

Teaching language