Big Data Analytics, 6 credits (TDDE31)

Big Data Analytics, 6 hp

Main field of study

Information Technology Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Patrick Lambrix

Director of studies or equivalent

Patrick Lambrix

Available for exchange students

Yes
Course offered for Semester Period Timetable module Language Campus VOF
6MDAV Computer Science, Master's Programme 2 (Spring 2019) 2 3 English Linköping v
6MICS Computer Science, Master's Programme 2 (Spring 2019) 2 3 English Linköping v
6MICS Computer Science, Master's Programme (AI and Data Mining) 2 (Spring 2019) 2 3 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2019) 2 3 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2019) 2 3 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (Medical Informatics) 8 (Spring 2019) 2 3 English Linköping v
6CITE Information Technology, M Sc in Engineering 8 (Spring 2019) 2 3 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2019) 2 3 English Linköping v
6CITE Information Technology, M Sc in Engineering (Medical Informatics) 8 (Spring 2019) 2 3 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2019) 2 3 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2019) 2 3 English Linköping v

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
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering

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 examU, 3, 4, 53 credits
LAB1LabsU, G3 credits

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

Patrick Lambrix

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.

Other

Artikelsamling 2018.
TEN1 Written exam U, 3, 4, 5 3 credits
LAB1 Labs U, G 3 credits

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