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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
DAT346 - Techniques for large-scale data  
Tekniker för storskalig datahantering
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: MPDSC
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology

The course is full. For waiting list, please contact the director of studies:
Teaching language: English
Application code: 87114
Open for exchange students: No
Block schedule: A
Maximum participants: 30
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0119 Examination 4,0 c Grading: TH   4,0 c   03 Jun 2020 am J,  11 Oct 2019 am M   21 Aug 2020 pm J
0219 Written and oral assignments 3,5 c Grading: UG   3,5 c    

In programs

MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)


Alexander Schliep

  Go to Course Homepage


DAT345   Techniques for Large-scale Data


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

At least 15 credits in programming and at least 7.5 credits in databases, e.g. TDA357 Databases.


The aim of this course is to deepen the students' knowledge and skills and familiarize them with the technical and technological side of data science, including relevant data models, and software respectively hardware environments.

Learning outcomes (after completion of the course the student should be able to)

On successful completion of the course the student will be able to:

Knowledge and understanding
  • discuss important technological aspects when designing and implementing analysis solutions for large-scale data,
  • describe index structures and discuss their utility,
  • describe data models and software standards for sharing data on the web.
Skills and abilities
  • implement applications for transforming and analyzing large-scale data with appropriate software frameworks,
  • provide access and utilize structured data over the web with appropriate datamodels and software tools.
Judgement and approach
  • suggest appropriate computational infrastructures for analysis tasks and discuss their advantages and drawbacks,
  • discuss mechanisms for concurrency and recovery in database systems,
  • discuss the efficiency of query plans,
  • discuss large-scale data processing from an ethical point of view.


In particular, the course will include
  • an overview of computer architectures, algorithmic approaches, and  high-performance computing infrastructures with a focus on limitations for processing large-scale data,
  • an introduction to relevant frameworks for cluster computing with large-scale data,
  • implementation of data analysis tools on a cluster using Python and appropriate software frameworks,
  • index structures, query processing and optimisation; concurrency, recovery,
  • an overview of non-relational database technologies,
  • semantic web and related technologies,
  • an overview of ethical questions regarding large-scale data, e.g. with respect to licenses, accessibility, and anonymisation.


Lectures, computer lab sessions, and exercise sessions.


Course literature to be announced the latest 8 weeks prior to the start of the course.

Examination including compulsory elements

The course is examined by an individual written exam carried out in an examination hall, as well as mandatory written assignments, some of which will be carried out individually and some of which will be carried out in groups of up to 4 students. There will be non-obligatory individual assignments which grant bonus points for the written exam. These bonus points are valid for the whole academic year.

Page manager Published: Thu 04 Feb 2021.