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.

Learning objectives

Knowledge

Through the course, the students must acquire knowledge of models, methods, techniques and tools for a coherent data analytics solution, for example:

Data Warehousing, including

• Integration of many data sources
• Cleaning and pre-processing of data 
• Building a data warehouse: Extract, Transform, Load (ETL) 
• Data warehouse tools

Multidimensional databases and On-line Analytical Processing (OLAP), including

• Multidimensional modeling 
• Handling 
• OLAP queries 
• OLAP tools

Descriptive data analysis, including

• Histograms 
• Correlation plots 
• Tools for descriptive data analysis

Basic data mining, including

• Cluster analysis (clustering) 
• Association rules (association rules) 
• Tools for data mining

Basic machine learning models for predicting data, including

• Basic classification models 
• Linear regression  
• Data prediction tools

The students must be able to relate critically and reflexively in relation to these theoretical subjects

Skills

After completing the module, students must:

• be able to use models, methods, techniques and tools from the above-mentioned areas to identify, analyse, assess and come up with proposals for solving specific problems in practice
• be able to argue for the relevance of the chosen models, methods, techniques and tools as well as for the prepared solution proposal
• be able to reflect on the significance for the context in which the solution forms part

Competences

Concretely, it is expected that, after completing the course, the students will be able to:

• Model, design and implement an analytical data warehouse (data warehouse) with appropriate schemas and/or storage formats using multidimensional modeling  
• Integrate, clean, pre-process and transform data from several different data sources, including using Extract-Transform-Load tools
• Analyze data using On-Line Analytical Processing (OLAP) descriptive data analysis techniques and tools
• Finding patterns in data using data mining techniques and tools 
• Predict data using basic machine learning models and tools

Type of instruction

The training shall be organised according to the general teaching forms referred to in § 17

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 300 hours.

Exam

Exams

Additional information

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