TIES438 Big Data Engineering (5 cr)

Open the course unit brochure on Sisu
Study level:
Advanced studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Faculty of Information Technology
Curriculum periods:
2017-2018, 2018-2019, 2019-2020

Description

Content

During the course multiple facets related to the Big Data phenomenon will be studied. First, students will get introduced to large data sets and streaming data. Then, example storage solutions and processing algorithms will be studied. Finally, we will look into hardware considerations and apply the theory on real world datasets related to news, wikipedia, brain analysis, biology, chemistry, etc.

Students who wish to work on a problem specific to their own research should discuss this with the teacher at the beginning of the course.

Completion methods

The course is completed by implementing the assigned tasks. A small part of the evaluation is done by quizzes during the lectures.

Assessment details

The implementation of algorithms is intended to assist the students in their understanding of the course content.

Learning outcomes

After completion of this course the students will understand the concepts related to, and the intrinsic characteristics of big amounts of data. The student will then be able to evaluate algorithms and technology to deal with problems in which big amounts of data are involved.

Additional information

Students should attend the lectures and read the assigned materials. Further, the implementation of algorithms is intended to assist the students in their understanding of the course content.

Links

Description of prerequisites

The student should know how to program (at least programming 2) and be familiar with algorithms, data structures and computational complexity. Further, the student should have notion of sets, probability theory, linear algebra, and statistics.

Study materials

Literature

  • Motwani, and Raghavan. Randomized Algorithms. Cambridge, UK: Cambridge University Press, 1995. ISBN: 0521474655. (available in JYU trough EBSCOhost https://jyu.finna.fi/Record/jykdok.1485577 ); ISBN: 0521474655
  • Mining massive data sets - Anand Rajaraman, Jure Leskovec, Jeffrey D. Ullman free download from http://www.mmds.org/
  • https://jyu.finna.fi/Record/jykdok.1485577
  • http://www.mmds.org/

Completion methods

Method 1

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Parts of the completion methods
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Teaching (5 cr)

Type:
Participation in teaching
Grading scale:
0-5
Language:
English
No published teaching