IKT453 Intelligent Data Management

The course is connected to the following study programs

• Artificial Intelligence and The Internet of Things, Master's Programme
• Artificial Intelligence, 5-year master programme

Teaching language

English.

Course contents

This is an advanced course on intelligent data management. This course will provide relevant background and skills on the modern data management and data engineering methods that support large-scale data-driven decision making. In particular, we will explore intelligent data warehousing, data streaming, and information fusion technologies for scalable data interpretation and information sensemaking. We will elaborate on an infrastructure that facilitates efficient information consolidation and consider how concepts of information linkage and information fusion accelerate novel research directions in intelligent data management. Information fusion deals with reconstructing objects from multiple, possibly incomplete and inconsistent observations. The task of scalable information fusion is critical for interdisciplinary research where a comprehensive picture of the subject requires large amounts of data from disparate data sources. Overall, this course will provide a proper balance of the conceptual knowledge and practical skills in the cutting-edge intelligent data management technologies and explore their applicability limits.

Learning outcomes

Upon completion of the course students will be able to select, apply, as well as to develop data engineering tools appropriate for intelligent data processing, data analysis, and data-driven decision making. 

Upon completion of the course students will be able to 

• Understand major principles of intelligent data management technologies
• Understand major tradeoffs in design and development of a comprehensive data processing pipeline for data-driven decision making
• Design and develop advanced data warehousing solutions for data-driven decision making
• Design and develop intelligent data streaming systems
• Understand major tradeoffs in intelligent data processing under large-scale data-intensive scenarios  

Teaching methods

Lectures, labs, group assignments, supervised written assignments and self-study.

The workload for the average student is approximately 200 hours.

Evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as Single Standing Module

Yes, if there are places available

Admission Requirement if given as Single Standing Module

Admission requirements for the course are the same as for the master’s programme in ICT.

Assessment methods and criteria

 Graded assignments. Class project presentations. Graded marks.