Aim: M1 intends to provide an opportunity to sample the core techniques of data science, understand their intuition and application cases. It also aims at showing best practice of how to select specific and appropriate methods for the particular data science project, as well as how to efficiently and autonomously acquire further knowledge of the rapidly evolving field. Insights and techniques learned in this module can be applied to real-world problems in, e.g. marketing (How do you classify customers who are likely to spend a lot?), management (How do you identify performance bottlenecks in the organisation?) or finance (Is this person likely to default on their mortgage?).
This module is an introduction to the main ideas behind (social) data science, and the essential principles and techniques in the data scientist's toolbox. It aims at providing a broad overview by taking a "bird's eye perspective" and presenting a range of topics briefly instead of focusing on a single topic in depth. The Introduction to Social Data Science will survey the foundational issues in data science, namely:
Upon completion of the module students will have built a solid and expandable knowledge foundation in modern data science and will have acquired a broad range of skills enabling them to carry out own data analysis projects. Students will be capable of autonomously managing and evaluating complex projects and problems associated with data management, description, and analysis
|Name of exam||M1: Applied Data Science and Machine Learning|
|Type of exam|
Oral examGroup examination with max. 6 students.
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination Policies and Procedures|
|Danish title||M1: Applied Data Science and Machine Learning|
|Language of instruction||English|
|Location of the lecture||Campus Aalborg|
|Responsible for the module|
|Study Board||Study Board of Economics|
|Department||Department of Business and Management|
|Faculty||The Faculty of Social Sciences|