Big Data Analytics, 6 credits

Big Data Analytics, 6 hp

TDDE31

Main field of study

Information Technology Computer Science and Engineering Computer Science

Course level

Second cycle

Course type

Programme course

Examiner

Olaf Hartig

Director of studies or equivalent

Patrick Lambrix

Education components

Preliminary scheduled hours: 42 h
Recommended self-study hours: 118 h

Available for exchange students

Yes
Course offered for Semester Period Timetable module Language Campus ECV
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Chinese 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Chinese (Data Science and Machine Intelligence) 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, French 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, French (Data Science and Machine Intelligence) 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, German 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, German (Data Science and Machine Intelligence) 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Japanese 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Japanese (Data Science and Machine Intelligence) 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Spanish 8 (Spring 2021) 2 3 English Linköping E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Spanish (Data Science and Machine Intelligence) 8 (Spring 2021) 2 3 English Linköping E
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering 8 (Spring 2021) 2 3 English Linköping E
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering (Data Science and Machine Intelligence) 8 (Spring 2021) 2 3 English Linköping E
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2021) 2 3 English Linköping E
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2021) 2 3 English Linköping E
6CDDD Computer Science and Engineering, M Sc in Engineering (Medical Informatics) 8 (Spring 2021) 2 3 English Linköping E
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2021) 2 3 English Linköping E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2021) 2 3 English Linköping E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (Medical Informatics) 8 (Spring 2021) 2 3 English Linköping E
6MICS Computer Science, Master's Programme 2 (Spring 2021) 2 3 English Linköping E
6MICS Computer Science, Master's Programme (AI and Data Mining) 2 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Chinese 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Chinese (Specialization Computer Science and Engineering) 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - French 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - French (Specialization Computer Science and Engineering) 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - German 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - German (Specialization Computer Science and Engineering) 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Japanese 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Japanese (Specialization Computer Science and Engineering) 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Spanish 8 (Spring 2021) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Spanish (Specialization Computer Science and Engineering) 8 (Spring 2021) 2 3 English Linköping E
6CIII Industrial Engineering and Management, M Sc in Engineering 8 (Spring 2021) 2 3 English Linköping E
6CIII Industrial Engineering and Management, M Sc in Engineering (Computer Science and Engineering Specialization) 8 (Spring 2021) 2 3 English Linköping E
6CITE Information Technology, M Sc in Engineering 8 (Spring 2021) 2 3 English Linköping E
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2021) 2 3 English Linköping E
6CITE Information Technology, M Sc in Engineering (Medical Informatics) 8 (Spring 2021) 2 3 English Linköping E
6MMAT Mathematics, Master's Programme 2 (Spring 2021) 2 3 English Linköping E
ECV = Elective / Compulsory / Voluntary

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Master's Programme in Computer Science
  • Computer Science and Engineering, M Sc in Engineering
  • Industrial Engineering and Management - International, M Sc in Engineering
  • Industrial Engineering and Management, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Applied Physics and Electrical Engineering - International, M Sc in Engineering
  • Applied Physics and Electrical Engineering, M Sc in Engineering
  • Master's Programme in Mathematics

Prerequisites

Basic database course. Data mining or machine learning course.

Intended learning outcomes

After completed course, the student should on an advanced level be able to:

  • collect and store Big Data in a distributed computer environment
  • perform basic queries to a database operating on a distributed file system
  • account for basic principles of parallel computations
  • use the MapReduce concept to parallelize common data processing algorithms
  • be able to modify standard machine learning models in order to process Big Data
  • use tools for machine learning for Big Data

 

Course content

The course introduces main concepts and tools for storing, processing and analyzing Big Data which are necessary for professional work and research in data analytics.

  • Introduction to Big Data: concepts and tools
  • Basic principles of parallel computing
  • File systems and databases for Big Data
  • Querying for Big Data
  • Resource management in a cluster environment
  • Parallelizing computations for Big Data
  • Machine Learning for Big Data

Teaching and working methods

The teaching comprises lectures and computer exercises. 
Lectures are devoted to presentations of theories, concepts and methods. 
Computer exercises provide practical experience of manipulation with Big Data. 

 

Examination

TEN1Written exam3 creditsU, 3, 4, 5
LAB1Labs3 creditsU, G

Grades

Four-grade scale, LiU, U, 3, 4, 5

Course literature

Article collection.

Other information

Related courses: advanced data models and databases, parallel programming, multicore programming.

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Patrick Lambrix

Examiner

Olaf Hartig

Course website and other links

http://www.ida.liu.se/~TDDE31/

Education components

Preliminary scheduled hours: 42 h
Recommended self-study hours: 118 h

Course literature

Other

  • Artikelsamling 2018.
Code Name Scope Grading scale
TEN1 Written exam 3 credits U, 3, 4, 5
LAB1 Labs 3 credits U, G

Other

Artikelsamling 2018.

Note: The course matrix is not fully translated to English.

I U A Modules Comment
1. ÄMNESKUNSKAPER
1.1 Kunskaper i grundläggande matematiska och naturvetenskapliga ämnen
Grundläggande matematiska begrepp
Basic mathematical concepts
1.2 Kunskaper i grundläggande (motsvarande G1X) teknikvetenskapliga ämnen
X
X
LAB1
TEN1
Programmering, modellering, databasteknik
Programming, modeling, database technology
1.3 Fördjupade kunskaper (motsvarande G2X), metoder och verktyg inom något/några teknik- och naturvetenskapliga ämnen
X
X
LAB1
TEN1
databaser, parallelprogrammering, maskininlärning
databases, parallel programming, machine learning
1.4 Väsentligt fördjupade kunskaper (motsvarande A1X), metoder och verktyg inom något/några teknik- och naturvetenskapliga ämnen
1.5 Insikt i aktuellt forsknings- och utvecklingsarbete
2. INDIVIDUELLA OCH YRKESMÄSSIGA FÄRDIGHETER OCH FÖRHÅLLNINGSSÄTT
2.1 Analytiskt tänkande och problemlösning
X
X
LAB1
TEN1
Modellering, algoritmdesign
Modeling, algorithm design
2.2 Experimenterande och undersökande arbetssätt samt kunskapsbildning
X
X
LAB1
Datorlaborationer
Labs
2.3 Systemtänkande
X
X
LAB1
TEN1
Utifrån ett problem välja modell och lösning
Choosing solutions for problems
2.4 Förhållningssätt, tänkande och lärande
X
X
LAB1
TEN1
Kreativt och kritiskt tänkande
Creative and critical thinking
2.5 Etik, likabehandling och ansvarstagande
TEN1
Research-related content
3. FÖRMÅGA ATT ARBETA I GRUPP OCH ATT KOMMUNICERA
3.1 Arbete i grupp
LAB1
Laborationer i grupp
Labs in pairs
3.2 Kommunikation
LAB1
Skriftlig rapport för varje laboration
written reports for labs
3.3 Kommunikation på främmande språk
Kursern ges på engelska
4. PLANERING, UTVECKLING, REALISERING OCH DRIFT AV TEKNISKA PRODUKTER OCH SYSTEM MED HÄNSYN TILL AFFÄRSMÄSSIGA OCH SAMHÄLLELIGA BEHOV OCH KRAV
4.1 Samhälleliga villkor, inklusive ekonomiskt, socialt och ekologiskt hållbar utveckling för kunskapsutveckling
LAB1
Laborationsdata från SMHI
Used lab data from SMHI
4.2 Företags- och affärsmässiga villkor
4.3 Att identifiera behov samt strukturera och planera utveckling av produkter och system
X
X
LAB1
TEN1
Modelering
Modeling
4.4 Att konstruera produkter och system
X
X
LAB1
algoritmdesign
algorithm design
4.5 Att realisera produkter och system
X
X
LAB1
implementation
implementation
4.6 Att ta i drift och använda produkter och system
5. PLANERING, GENOMFÖRANDE OCH PRESENTATION AV FORSKNINGS- ELLER UTVECKLINGSPROJEKT MED HÄNSYN TILL VETENSKAPLIGA OCH SAMHÄLLELIGA BEHOV OCH KRAV
5.1 Samhälleliga villkor, inklusive ekonomiskt, socialt och ekologiskt hållbar utveckling
5.2 Ekonomiska villkor för kunskapsutveckling
5.3 Att identifiera behov samt strukturera och planera forsknings- eller utvecklingsprojekt
5.4 Att genomföra forsknings- eller utvecklingsprojekt
5.5 Att redovisa och utvärdera forsknings- eller utvecklingsprojekt

This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page.

There are no files available for this course.