Course content

- Introduction to Big Data Analytics (BDA)- Data input, algorithms and presentation- Graphics Processing Units (GPU) and Field Programmable Gate Array (FPGA) for Big Data Analysis- Business related applications of Big Data (Export, patent and register data)

Learning outcome

Learning outcome - Knowledge- Have knowledge about the principles and challenges in the design and development of BDA technology.- Have knowledge of how Big Data techniques can be used in various business settings. Learning outcome - Skills- Be able to apply appropriate techniques in different BDA problems.- Hands-on skills in preprocessing raw data.- Hands-on skills in clustering data to identify meaningful patterns.- Hands-on skills in applying relevant machine learning algorithms for prediction. Learning outcome - General competence- Be able to discuss and communicate the possibilities and limitations in the field of BDA.- Understand the BDA needs of industries and business, and have foundation-level knowledge to develop a solution.

Learning methods and activities

The course consists of a combination of lectures, casework, mandatory assignments, presentations and discussions.

Compulsory assignments

• Project Assignments

Further on evaluation

Mandatory project assignments. If a student defers the final exam until the following academic year, then he or she must complete and pass a new individual assignment.

Specific conditions