MAE4000 – Data Science
In this course, you will learn to work with the core concepts and techniques of descriptive and inferential statistics that function as foundations for formulating and implementing successful data-based analysis strategies to perform evidence-based research.
You will be introduced to the essentials of basic programming and use of syntax-based data analysis as instantiated in the open-source statistical and graphic software environment R.
The course covers the following five key topics:
1. Data Management: wrangling & auditing
2. Descriptive Statistics
3. Data Visualization and Representations (i.e., plots, tables, diagrams)
4. Probability and Randomness
5. Statistical Inference & Design
Throughout the course, attention will be given to issues regarding questionable research practices and research ethics.
- recognize the challenges with respect to data collection, data quality, and alignment between research questions and the data
- recognize descriptive statistics as basic summaries of specific data features
- recognize that sampling variability and uncertainty are ubiquitous
- run basic data management, visualization, and analysis techniques using the open source statistical software environment R
- Manage a core dataset by wrangling it into shape for specific data-analyses and performing an audit to document and clean unexpected irregularities
- Visualize data paying attention to basic quality criteria to increase clarity and communication value
- Perform and communicate basic data analyses taking into consideration features of the study design and inferential uncertainty
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.
This course is a compulsory part of the master's programme Assessment, Measurement and Evaluation. Students on exchange on master's level at UiO or enrolled in other UiO master's programmes may be given admission if there is room in the course. Questions about this should be directed to email@example.com.
Ph.d. candidates can apply to the Ph.d version of the course: UV9290
This course combines lectures and computer labs with data analysis tasks in statistical software environments.
Obligatory course components ("faglig krav") autumn 2020:
A diverse set of small and moderate assignments is used to keep track of student progress throughout the course.
The learning management system CANVAS is used for providing detailed information on & delivery of the assignments.
Successful completion of each assignment is a prerequisite for being allowed to submit the final portfolio exam.
The exam consists of a portfolio exam consisting of three components:
1. Data wrangling and auditing component
2. Data visualization product and critique component
3. Data report component
The delivery of each component will take the form of a brief report comprising the R code and related output based on an individualized dataset.
Each component counts for one third of the final grade and you need to pass on each component to be able to pass the exam. You have to pass all three components in the same semester for the exam result to be valid.
Previously, the exam format in this course was a 4-hour written exam. Click here to see previously given exam questions:
Use of sources and citation
Language of examination
The examination text is given in English, and you submit your response in English.
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.