Program Overview

---

Big Data Systems (BDS) Course Information

Course Description

The goal of this course is to give participants an understanding of the technologies in computer systems for Big Data management. It 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.

Course Content

The following main topics are contained in the course:

• Parallel database systems
• Massively parallel data analysis
• Fast stream processing systems
• Distributed transaction processing
• Fault-tolerance
• Scalability
• Event-based systems

Learning Outcomes

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

Teaching and Learning Methods

Lectures, seminars, and discussions.

Literature

See Absalon when the course is set up.

Recommended Prerequisites

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. Notions of UNIX / shell scripting are helpful, but not required. Academic qualifications equivalent to a BSc degree is recommended.

Exam Information

• ECTS: 7.5 ECTS
• Type of assessment: Written assignment
• Oral examination, 20 minutes without preparation
• Type of assessment details: The assessment is based on the following two elements:
  • Group project assignments (3-5) with individual defence in the exam week
  • Oral examination in the exam week
• Aid: All aids allowed
• Marking scale: 7-point grading scale
• Censorship form: No external censorship
• Several internal examiners

Re-exam

Same as the ordinary exam The oral examination and project assignments defense will take place on the re-exam date. Resubmission of project assignments 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

See Learning Outcome.

Course Type

Single subject courses (day)

Workload

• Category: Lectures
• Hours: 28
• Preparation: 50
• Project work: 127
• Exam: 1
• English: 206

Course Details

• Language: English
• Course number: NDAK18001U
• ECTS: 7.5 ECTS
• Programme level: Full Degree Master
• Duration: 1 block
• Placement: Block 4
• Schedule group: C
• Capacity: 50
• Study board: Study Board of Mathematics and Computer Science
• Contracting department: Department of Computer Science
• Contracting faculty: Faculty of Science
• Course Coordinator: Yongluan Zhou
• Teacher: Yongluan Zhou

University Information

• University of Copenhagen
• Nørregade 10
• 1165 København K

Services

• Press and media service
• Merchandise
• IT-support
• For suppliers

For Students and Employees

• KUnet

Job and Career

• Job portal
• Jobs for students
• Alumni

Emergencies

• Emergency response

Web

• Cookies and privacy policy
• Accessibility statement
• Information security

Connect with UCPH

Close menu