SDG231 Data analytics for sustainable future

Contents and structure

Data analytics refers to obtaining useful information from raw data applying specialized computing methods.   Data analytics based systems facilitate processes like transformation, organization and modelling of data sets in order to draw conclusions and identify patterns.

This course will introduce key concepts in data analytics and information extraction; including specific algorithms and techniques for feature extraction, clustering, outlier detection, and prediction of unstructured data sets. By taking this course students will be given a broad view of the general issues surrounding unstructured and semi-structured data and the application of algorithms to such data. At a practical level students will have the opportunity to explore an assortment of data analytics techniques and apply them to problems involving real-world data.

The course is a part of the The Norwegian West Coast SDG educational initiative: Knowledge for Ethics and Sustainable Development.

Learning Outcome

Knowledge based learning outcome

• Key concepts, tools and approaches for data analytics on complex data sets
• Techniques for modelling and extracting features from large data sets
• Theoretical concepts and the motivations behind different data analytics approaches
• Knowledge relevant for SDG goal 3, SDG 4, SDG 9, SDG 11, and SDG 12

Skill based learning outcome

• Be able to design data
• Ability to apply acquired knowledge for understanding data and select suitable methods for data analysis
• Ability to apply  state-of-the-art data  analytics techniques

General competences

• Solve real-word data-mining, data-indexing and information extraction tasks
• Handle tasks of data analytics for sustainability such as data exploration and visualization, pattern mining, anomaly detection, and prediction and forecast

Entry requirements

None.

Teaching methods

Lectures and exercises.

Compulsory learning activities

One written hand-in.

Assessment

Written exam, 4 hours. Be aware that a written exam might be digital.

Grading scale is A-F where F is fail.

Examination support material

None

More about examination support material