TIES438 Big Data Engineering, 5 op [kotisivu]

Also the students whose rank is higher than 40 should come to the first lecture.

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.

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.

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.

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.

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

• Mining massive data sets - Anand Rajaraman, Jure Leskovec, Jeffrey D. Ullman free download from http://www.mmds.org/
• Optional: 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 )
• The online course https://www.coursera.org/course/mmds overlaps partially with the course.