MAE4000 – Data Science

Schedule, syllabus and examination date

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Course content

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. 

Learning outcome

Knowledge

  • 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

Skills

  • run basic data management, visualization, and analysis techniques using the open source statistical software environment R

Competence

  • 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

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.

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. Inquiries about this can be directed to studieinfo@cemo.uio.no.

Teaching

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

Obligatory course components:

  • 80% attendance requirement for the lectures and computer labs
  • 2 assignments that will form later the basis for components to be delivered in your portfolio exam
  • Completion of the 2 assignments is a prerequisite for being allowed to submit the final portfolio. Students will receive feedback on these assignments. The reworked versions of the assignments will form part of the portfolio exam. Detailed information about the obligatory assignments will be given in Canvas

Examination

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:

Previous exams

Use of sources and citation

You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.

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.

Evaluation

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

Credits

10

Level

Master

Teaching

Every autumn

Examination

Every autumn

Teaching language

English