- Second cycle
- 1 semester
- Data structures
- Computer programming – both Object-Oriented Programming and Procedural Programming
- Algorithm design and analysis
- Distributed programming (IE501513)
- Statistics and probabilities
1. Introduction to Big Data Analytics (BDA)
- What is BDA?
- Why BDA?
- Data Mining and Machine Learning
Theme 1 will establish a fundamental understanding of BDA for the discussions in later themes.
2. CPU architectures for BDA
- Data input
- Basic algorithms
- Knowledge presentation
Theme 2 will discuss on BDA techniques using CPU architectures.
3. GPU/FPGA architectures for BDA
- Introduction to GPU and FPGA programming
- CUDA and OpenCL
- BDA using GPU or FPGA
Theme 3 will discuss on BDA techniques using GPU/FPGA architectures.
4. Implementation of BDA applications
Theme 4 will include practical exercises and projects in BDA techniques using CPU or GPU or FPGA architectures.
- Lectures, exercises, obligatory project assignments.
Learning outcome – Knowledge
- Have knowledge in the principles and challenges in the design and development of BDA technology
- Have knowledge in the advantages of different architectures regarding BDA applications
Learning outcome – Skills
- Be able to apply appropriate techniques in different BDA problems
Learning outcome – General competence
- Be able to discuss and communicate the possibilities and limitations in the field of BDA
- All obligatory project assignments must be graded D or better in order to get access to the exam.
- Oral exam
- Semester assignment, semester paper, project assignment and similar
- Based on the student’s mastering of topics covered in lectures, and a demostration of mandatory (individual) exercises/projects.
- Grading A-F. Grade F is a fail