This module intends to provide an opportunity to sample the core techniques of data science and machine learning, 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?).
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. More specifically the module will cover foundations of data manipulation, exploratory data analysis, supervised and unsupervised machine learning. Students will be capable of autonomously managing and evaluating complex projects and problems associated with data management, description, and analysis.
The objective is that the student after the module possesses the necessary knowledge on:
The objective is that the student after the module possesses the necessary skills in:
The objective is that the student after the module possesses the necessary competences in:
For information see § 17.
|Name of exam||Introduction to Data Handling, Exploration & Applied Machine Learning|
|Type of exam|
Oral examIndividual examination.
|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||Introduktion til datahåndtering, udforskning og anvendt maskinlæring|
|Language of instruction||English|
|Location of the lecture||Campus Aalborg|
|Responsible for the module|
|Study Board||Study Board of Economics and Business Administration|
|Department||AAU Business School|
|Faculty||Faculty of Social Sciences and Humanities|