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
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2020) 2 3 English Linköping E
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2020) 2 3 English Linköping E
6CDDD Computer Science and Engineering, M Sc in Engineering (Medical Informatics) 8 (Spring 2020) 2 3 English Linköping E
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2020) 2 3 English Linköping E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2020) 2 3 English Linköping E
6MICS Computer Science, Master's Programme 2 (Spring 2020) 2 3 English Linköping E
6MICS Computer Science, Master's Programme (AI and Data Mining) 2 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Chinese 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Chinese (Specialization Computer Science and Engineering) 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - French 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - French (Specialization Computer Science and Engineering) 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - German 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - German (Specialization Computer Science and Engineering) 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Japanese 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Japanese (Specialization Computer Science and Engineering) 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Spanish 8 (Spring 2020) 2 3 English Linköping E
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Spanish (Specialization Computer Science and Engineering) 8 (Spring 2020) 2 3 English Linköping E
6CIII Industrial Engineering and Management, M Sc in Engineering 8 (Spring 2020) 2 3 English Linköping E
6CIII Industrial Engineering and Management, M Sc in Engineering (Computer Science and Engineering Specialization) 8 (Spring 2020) 2 3 English Linköping E
6CITE Information Technology, M Sc in Engineering 8 (Spring 2020) 2 3 English Linköping E
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2020) 2 3 English Linköping E
6CITE Information Technology, M Sc in Engineering (Medical Informatics) 8 (Spring 2020) 2 3 English Linköping E

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Computer Science and Engineering, M Sc in Engineering
  • Master's Programme in Computer Science
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Industrial Engineering and Management - International, M Sc in Engineering
  • Industrial Engineering and Management, 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 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 might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
1.1 Knowledge of underlying mathematics and science (G1X level)
X
Basic mathematical concepts
1.2 Fundamental engineering knowledge (G1X level)
X
X
LAB1
TEN1
Programming, modeling, database technology
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)
X
X
LAB1
TEN1
databases, parallel programming, machine learning
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)

                            
1.5 Insight into current research and development work

                            
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving
X
X
LAB1
TEN1
Modeling, algorithm design
2.2 Experimentation, investigation, and knowledge discovery
X
X
LAB1
Labs
2.3 System thinking
X
X
LAB1
TEN1
Choosing solutions for problems
2.4 Attitudes, thought, and learning
X
X
LAB1
TEN1
Creative and critical thinking
2.5 Ethics, equity, and other responsibilities
TEN1
Research-related content
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork
X
LAB1
Labs in pairs
3.2 Communications
X
LAB1
written reports for labs
3.3 Communication in foreign languages

                            
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
4.1 External, societal, and environmental context
X
LAB1
Used lab data from SMHI
4.2 Enterprise and business context

                            
4.3 Conceiving, system engineering and management
X
X
LAB1
TEN1
Modeling
4.4 Designing
X
X
LAB1
algorithm design
4.5 Implementing
X
X
LAB1
implementation
4.6 Operating

                            
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development

                            
5.2 Economic conditions for knowledge development

                            
5.3 Identification of needs, structuring and planning of research or development projects

                            
5.4 Execution of research or development projects

                            
5.5 Presentation and evaluation of research or development projects

                            

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