Optional course in Data Science Master's Program. Also suitable for Computer Science Master's Program students.

The student must have basic programming skills, skills to work with command line tools in Linux, and basic knowledge in database systems.

Basic courses in programming and computer architecture are helpful to make the contents of the course easier to approach. However, apart from basic programming skills the course is self-contained.

This course focuses on big data platforms and on key algorithmic ideas and methods used to implement them. After completing this course you are able to list many of the key technologies used in big data processing and to select suitable methods for solving challenging big data processing tasks using cloud computing technologies. You will also be able to compare the scalability and fault tolerance implications of using the selected methodologies.

First or second year of Master's curriculum.

Autumn term, period 1.

Advanced topics in cloud computing with emphasis on scalable big data platforms employed in cloud computing. Key cloud technologies and their algorithmic background. Main topics are distributed computing, Warehouse-Scale Computers, fault tolerance in distributed systems, distributed file systems, distributed batch processing with the MapReduce and the Apache Spark computing frameworks, and distributed cloud based databases.

The course material will consist of lecture materials and exercises provided by the lecturer.

As optional reading the students can read the following freely available textbook that unfortunately covers only a part of the course contents but also has extra material beyond the course scope:

Luiz André Barroso, Urs Hölzle, Parthasarathy Ranganathan:
The Datacenter as a Computer: Designing Warehouse-Scale Machines, Third Edition
Synthesis Lectures on Computer Architecture
October 2018, 189 pages,
https://doi.org/10.2200/S00874ED3V01Y201809CAC046

Course will be a MOOC course with contents available in the Web, automated home assignment for practical hands-on use of Big Data platforms such as Spark.

Both home exercises and an exam are required to pass the course.

Taking this course does not give students access to a University of Helsinki user account. If you want to view and share information about your studies after completing the course, sign up for the Oma Opintopolku -service maintained by the Finnish National Agency for Education. The ECTS will be displayed within two days in the Oma Opintopolku -service after credits have been registered to the University of Helsinki. To register for the Oma Opintopolku – service you must identify yourself by using Finnish bank identification codes, mobile certificate or certificate card.

Contact information:

• The online learning environment: mooc@cs.helsinki.fi
• Open University course enrollment: avoinyo-tietojenkasittelytiede@helsinki.fi
• Content of the course: teacher in charge of the course, Keijo Heljanko (keijo.heljanko@helsinki.fi)

This course is a massive open online course (MOOC), which means that the course material is available to everyone. For further information about the course, please visit the online learning environment. The online learning environment contains the material and instructions necessary for completing the course.

The course involves assignments and an exam that are completed in the online learning environment.

How to participate?

1. Complete the assignments in the online learning environment. Course materials (without ECTS) are available to everyone without officially enrolling on the course. Please see details on course home exercise deadlines in the learning environment.

2. Complete the online exam. Please see details on exam schedule in the learning environment.

3. Enroll on the course through the Open University to receive credits. You will be given instructions for Open University course enrollment through email after you have completed the course assignments and the exam in the MOOC online learning environment.

Prof. Keijo Heljanko

Optional course in Data Science Master's Program.