Algorithms for Big Data Processing, Course, - Luleå University of Technology

COURSE SYLLABUS

Algorithms for Big Data Processing 7.5 credits

Algoritmer för stora datamängder

Second cycle, D7036E

Version

Course syllabus valid: Spring 2022 Sp 3 - Present

The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level

Second cycle

Grade scale

G U 3 4 5

Subject

Computer Science

Subject group (SCB)

Computer Technology

Entry requirements

The student should have knowledge about basic algorithms and data structures, discrete mathematics, and probability. For example: D0012E - Algorithms and Data Structures, M0009M - Discrete Mathematics and S0001M - Mathematical Statistics.

More information about English language requirements

Selection

The selection is based on 20-285 credits

Course Aim

After completing the course the student should be able to:

demonstrate knowledge of the disciplinary foundation and of proven experience in the design and analysis of algorithms and data structures for large data sets
demonstrate the ability to construct, analyze and critically evaluate various algorithmic solutions for large data sets with respect to correctness, efficiency, and reliability
show knowledge of mathematical tools for designing and analyzing algorithms for large data sets
demonstrate insight in the scientific state of the art in algorithms for large data sets
demonstrate the ability to model, predict and evaluate the events even with limited information

Contents

Topics covered include: paradigms and models, data stream algorithms, parallel algorithms, cache-oblivious algorithms.

Realization

Each course occasion´s language and form is stated and appear on the course page on Luleå University of Technology's website.

Lectures, laborations/projects, and seminars.

Examination

If there is a decision on special educational support, in accordance with the Guideline Student's rights and obligations at Luleå University of Technology, an adapted or alternative form of examination can be provided.

Seminars, assignments, compulsory attendance, oral and written presentation of projects. The final grade will be based on performance in all modules.

Examiner

Jingsen Chen

Literature. Valid from Spring 2016 Sp 3 (May change until 10 weeks before course start)

Scientific publications (conference papers and journal articles).

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Course offered by

Department of Computer Science, Electrical and Space Engineering

Modules

Code	Description	Grade scale	Cr	Status	From period	Title
0003	Project and assignments	G U 3 4 5	5.00	Mandatory	S22
0004	Seminars and attendence	U G#	2.50	Mandatory	S22

Study guidance

Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established

by Jonny Johansson, HUL SRT 16 Feb 2015

Last revised

by Jonny Johansson, HUL SRT 17 Feb 2021

Published:17 December 2014

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