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
Lectures will be complemented by online resources and e-learning tools such as podcasting, online tutorials, and mini-assignments, as integral parts of the teaching methodology to enhance student engagement outside the classroom. Physical face-to-face time will be centred around the tacit and interactive components of the problem-solving processes.
|Prøvens navn||M1: Applied Data Science and Machine Learning|
Skriftlig og mundtligPortfolio exam:
60% obtained through various graded (and supervised peer-graded) problem sheets and mini-assignments throughout the module.
40% final internal evaluation seminar with oral presentation, peer-evaluation (opponent group), internal critique and discussion departing from the final assignment and presentation.
|Vurderingskriterier||Vurderingskriterierne er angivet i Universitetets eksamensordning|
|Engelsk titel||M1: Applied Data Science and Machine Learning|
|Studienævn||Studienævn for Økonomi|
|Institut||AAU Business School|
|Fakultet||Det Samfundsvidenskabelige Fakultet|