Big Data and Large-Scale Computing

Professor in charge: Aristides Gionis
Other professors: Keijo Heljanko, Eero Hyvönen, Perttu Hämäläinen, Alex Jung, Kimmo Kaski, Samuel Kaski, Jaakko Lehtinen, Harri Lähdesmäki, Lauri Malmi, Ilkka Niemelä, Juho Rousu, Jari Saramäki, Jukka Suomela, Stavros Tripakis, Aki Vehtari
Extent: Long (55-65 credits) or compact (40-45 credits) major as CS track. Students taking a compact major take also a minor (20-25 credits). Students taking a long major may include an optional minor in their elective studies.

Objectives

The track on big data and large-scale computing provides the students with a strong background to cope with the challenges arising from the growth of data and information in our society. The track covers a wide range of topics in data management, data processing, algorithmics, data science, and data analysis. The teaching and instruction of the students is conducted by the leading experts in the focus areas of this track. Excellent students interested in pursuing doctoral studies after their M.Sc. degree can transfer to the Helsinki Doctoral Education Network in Information and Communications Technology (HICT).

Learning Outcomes

The track aims to educate professionals who are capable of dealing with the different aspects of data management and data analysis. The graduates of the track will be able to cope with the main big data challenges: collecting and storing data, dealing with data complexity and heterogeneity, developing efficient algorithms to process large datasets, building scalable systems in cloud platforms, employing distributed and parallel computing, discovering patterns and hidden structure in the data, building models and making inferences, and learning to visualize large datasets.

Content and Structure

The major consists of core courses, track compulsory courses, and optional computer-science courses. The purpose of the core courses is to ensure that all students in the major have a solid basic knowledge of computer science and software technology topics. The track courses provide deeper understanding of a specific topic and sufficient background knowledge for the Master's thesis in the track's area. After the core and track compulsory courses, most students will be left with quite a few credits for other computer-science courses.

Students have to select at least five courses from the major core course list, including the compulsory core course(s) defined by the track (bolded). The core courses can also be done as part of the Bachelor studies, which reduces the number of core course required at the Master level. Students who have completed equivalent courses at another university can be excused from taking the core courses with agreement of the professor in charge of the study track.

In addition to the major core courses, the students have to take the track compulsory course(s).

The track optional courses listed below are recommended but not required. The rest of the credits for the major can consist of any Master-level computer science courses.

Major core courses, compulsory major core courses bolded

CODE NAME CREDITS PERIOD/YEAR
CS-E3190 Principles of Algorithmic Techniques 5 I-II/1st year

CS-E3210

Machine Learning: Basic Principles

5

I-II/1st year

CS-E3220

Declarative Programming

5

V/1st year

CS-C3170

Web Software Development

5

II-III/1st year

CS-C3130

Information Security

5

I/1st year

CS-C3140

Operating Systems

5

I/1st year

CS-C3100

Computer Graphics

5

I-II/1st year

ELEC-E7851

Computational User Interface Design

5

II/1st year

Track compulsory courses

CODE NAME CREDITS PERIOD/YEAR

CS-E4600

Algorithmic Methods of Data Mining

5

I-II

CS-E4120

Scalable Cloud Computing

5

I-II

CS-E4610

Modern Database Systems

5

III-IV

Track optional courses

Code

Name

Credits

Period/Year

CS-E4580

Programming Parallel Computers

5

V

CS-E4800

Artificial Intelligence

5

III-IV

CS-E4830

Kernel Methods in Machine Learning

5

I-II

CS-E4520

Computer-Aided Verification and Synthesis

5

III-IV

CS-E4890

Deep Learning

5

II

CS-E4820

Machine Learning: Advanced Probabilistic Methods

5

III-IV

CS-E4850

Computer Vision

5

I-II

CS-E4840

Information Visualization

5

IV

CS-E4100

Mobile Cloud Computing

5

I-II

ELEC-E5510

Speech Recognition

5

II

ELEC-E5421

Convex Optimization for Engineers P

7

I-II

CS-E4500

Advanced Course in Algorithms

5

III-IV

CS-E4110

Concurrent Programming

5

I-II

CS-E4870

Research Project in Machine Learning and Data Science

10

varies

CS-E4003

Special Assignment in Computer Science

1-10

Agreed with the teacher

CS-E4004

Individual Studies in Computer Science

1-10

Agreed with the teacher

CS-E4002

Special Course in Computer Science: Query Processing and Optimization for Big Data (2017-2018)

1-10

II

Also optional courses can be included per agreement with a professor in charge of the track.

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