Content, progress and pedagogy of the module

Disclaimer. This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

PURPOSE
Increasingly large amounts of data are being collected that relate to societal and industrial processes. This data spans a variety of structures and includes tabular data and more complex, graph-structured data.

Successful value creation from such data involves data integration, preparation, and management, in addition to data analysis and machine learning  that supports data exploration and knowledge extraction. These are typically data and computationally intensive processes that call for advanced data processing, data analysis and machine learning.

Learning objectives

Knowledge

• Knowledge about data preparation and processing techniques relevant for the project domain.

• Knowledge about relevant data analysis and machine learning methods for solving data science problems involving data ranging from tabular data to  complex, graph-structured data.

Skills

• Identify a concrete and relevant application domain for which one or more suitable data sources are available.

• Develop a data science solution for an application domain.

• Perform data preparation and apply data analysis and machine learning methods and analyze the results.

• Document the developed solution and the results, including the knowledge gained about the domain.

Competences

• Reason about relevant techniques and methodologies for integrating and processing available data.

• Reason about relevant data analysis and machine learning methods.

• Reflect on the applied methods and techniques as well as the results obtained.

Type of instruction

Project work

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 450 hours.

Exam

Exams

Additional information

Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854