Type of course

Admission requirements

PhD students and students at the Student Research Programme at UiT The Arctic University of Norway register for class and exam in Studentweb by February 1st.

Other applicants apply for the right to study by December 1st, please use UiTs application website.

The following course code must be used:

• Course code 9301 - Singular courses at PhD level

If granted the right to study, the candidates must register for class and exam in Studentweb by February 1st.

This course has a maximum capacity of 10 candidates. If there are more applicants than available seats, candidates will be given priority from category 1 to 3:

• PhD candidates and students at a Student Research Programme, both at The faculty of health science, UiT The Arctic University of Norway
• PhD candidates and students at a Student Research Programme at UiT or other universities
• Applicants who have minimum a master’s degree or equivalent, but have not been admitted to a PhD programme

Should we have to make priorities within category 1, students who have gotten the furthest in their course of study, will be given priority.

Course content

The course will develop advanced data analysis techniques using programming as a tool and allow candidates to employ sophisticated visualization options. Python and associated packages will be used as a basis. Candidates will master important concepts in coding and shown how coding can be used to process data and automate tasks, such as the reading and parsing of data files.

A significant portion of time will be devoted to data visualization methods including a thorough understanding of different plot types and how to implement these using code. Version control and other tools for project organisation and dissemination will also be covered. The course will focus on Python but key concepts and skills will be applicable to other languages.

A key aspect of the course will involve methods of retrieving datasets from the internet including an introduction to APIs (application programming interfaces). Development of web applications and graphical user interfaces (GUIs) for analysing data will be introduced.

Candidates should have experience analysing and visualizing data including basic statistical training. We recommend that candidates have some prior experience with coding, but it is not a requirement.

Objectives of the course

Having completed the course the candidates will obtain the following learning outcomes:

Knowledge:

• Understand fundamental programming concepts and strategies to solve problems (for-loops and conditional statements)
• Produce efficient and clean code using abstraction
• Find and evaluate resources to further skills
• Describe the principles and procedures underlying use of an API
• Evaluate the importance of modern initiatives in open science

Skills:

• Use Python and associated data analysis platforms to analyse data
• Use integrated development environments (IDEs) and associated features (e.g. help files, commenting, code blocks)
• Initialise and maintain projects using version control
• Develop and document data analysis pipelines at a high level
• Research and use diverse APIs and/or other methods of retrieving data from the internet
• Suggest and evaluate advanced data visualization methods of including options for interactivity

General Competence:

• Develop ways of thinking in a logical and systemic manner
• Document and report analysis projects
• Effectively use online sources to solve problems, retrieve data, and generate hypotheses

Language of instruction and examination

Teaching methods