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Program Overview
Program Overview
The University of Copenhagen offers a course titled "Big Data Systems (BDS)" with the code NDAK18001U. This course aims to provide participants with an understanding of the technologies used in computer systems for Big Data management.
Course Description
The course covers both traditional methods used in parallel database systems, real-time stream processing systems, transactional database systems, as well as modern technologies of cloud computing and massively parallel data analysis platforms. The main topics included in the course are:
- Parallel database systems
- Massively parallel data analysis
- Fast stream processing systems
- Distributed transaction processing
- Fault-tolerance
- Scalability
- Event-based systems
Learning Outcome
Upon completion of the course, participants will have:
Knowledge of
- Theories and techniques in parallel database systems
- Theories and techniques in data stream processing systems
- Theories and techniques in distributed transactional systems
- Design of and trade-offs in the modern systems introduced in the course
Skills to
- Develop programs and apply tools for big data management and analysis and deploy them on a cloud computing platform
- Report work done with Big Data systems in a clear and precise language, and in a structured fashion
Competences to
- Design, implement, deploy and optimise Big Data systems
- Analyse solutions in Big Data systems
- Discuss research articles related to Big Data systems with colleagues
- Plan and execute groups projects with Big Data systems and report the findings
Literature
The course literature is available on Absalon when the course is set up.
Recommended Academic Qualifications
The course builds on the knowledge acquired in the course NDAK15006U Advanced Computer Systems (ACS). Working knowledge of Java and C#, including concurrency and communication mechanisms, is required. Notions of UNIX / shell scripting are helpful but not required. Academic qualifications equivalent to a BSc degree are recommended.
Teaching and Learning Methods
The course includes lectures, seminars, and discussions.
Workload
The workload for the course is as follows:
- Lectures: 28 hours
- Preparation: 50 hours
- Project work: 127 hours
- Exam: 1 hour
- Total: 206 hours
Exam
The exam consists of two elements:
- Group project assignments (3-5) with individual defence in the exam week
- Oral examination in the exam week The individual project assignments defence and oral exam should be carried out in one session. An overall grade will be given by taking both elements into account.
Aid
All aids are allowed.
Marking Scale
The marking scale is a 7-point grading scale.
Censorship Form
There is no external censorship, and several internal examiners are used.
Re-exam
The re-exam is the same as the ordinary exam. The oral examination and project assignments defense will take place on the re-exam date. Resubmission of project assignments is allowed no later than three weeks before the re-exam date. Project assignments not redone will be transferred with the original assessments.
Criteria for Exam Assessment
The criteria for exam assessment are based on the learning outcomes.
Course Information
- Language: English
- Course code: NDAK18001U
- Credit: 7.5 ECTS
- Level: Full Degree Master
- Duration: 1 block
- Placement: Block 4
- Schedule: C
- Course capacity: 50
- Study board: Study Board of Mathematics and Computer Science
- Contracting department: Department of Computer Science
- Contracting faculty: Faculty of Science
- Course Coordinators: Yongluan Zhou
- Lecturers: Yongluan Zhou
Additional Information
The course is also available as continuing and professional education. The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
