Application deadline

Type of course

Course content

Objectives of the course

Knowledge and comprehension:

• Knowledge of the process of Data Science.
• Understanding of different computational tools that can be used to gather and analyze data.
• Understand and explore conceptual challenges of inferential reasoning with data.

Skills:

Be able to identify interesting Data Science opportunities, questions and data sources.

Be able to write code that extracts data from various relevant sources.

Be able to write code that manipulates and transforms data.

Be able to write code that visualize data.

Be able to write code that model relationships in data.

Be able to use code that produces insight from data, and the principles behind reproducible code/projects.

Competence:

The student should be able to develop competence that adds value to data in the following five fields of Data Science:

• Data collection - data wrangling and cleaning to get data suitable for analysis.
• Data management - manipulating data consistently.
• Exploratory data analysis - generating hypotheses and building intuition from data.
• Prediction or statistical learning from data.
• Communication - present the extraction of knowledge and insights from data.

Language of instruction

Teaching methods

Assessment

Assessment will be based on a portfolio of obligatory assignments, and a portfolio project. The portfolio project can be submitted as a part of a group. The portfolio should showcase the ability to ask an interesting scientific or business relevant question, to gather and clean relevant data, to apply some meaningful analytical analyses, and to showcase or visualize the results in an engaging, digestible manner.

A graded scale of five marks from A to E for pass and F for fail. Only one overall grade is given for the course. There will not be a re-sit exam for this course.

Recommended reading/syllabus