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Syllabus for

Academic year
DAT345 - Techniques for Large-scale Data  
Tekniker för storskalig datahantering
 
Syllabus adopted 2018-12-08 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology
Department: 37 - COMPUTER SCIENCE AND ENGINEERING

The course is full. For waiting list, please contact the director of studies: elke.mangelsen@chalmers.se
Teaching language: English
Open for exchange students: No
Block schedule: A
Maximum participants: 30

Course elements   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0117 Examination 4,0c Grading: TH   4,0c   05 Jun 2019 am SB   23 Aug 2019 pm J
0217 Written and oral assignments 3,5c Grading: TH   3,5c    

In programs

MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (elective)

Examiner:

Alexander Schliep

  Go to Course Homepage


 

Eligibility:


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 15 credits in programming and at least 7.5 credits in databases, t e TDA357 Databases

Aim

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 data models and software standards for sharing data on the web.
Skills and abilities
  • use Python to 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 advantages and drawbacks of different strategies for dissemination of data,
  • discuss large-scale data processing from an ethical point of view.

Content

The course will introduce
aspects of designing and implementing large-scale data science solutions. In particular
the course will include
  • a brief overview of computer architectures 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, 
  • 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 tolicenses, accessibility, and anonymisation.

Organisation

Lectures, computer lab sessions, and exercise sessions.

Literature

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.


Published: Fri 18 Dec 2009. Modified: Mon 28 Nov 2016