Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)
Big Data Analytics, 6 credits
Big Data Analytics, 6 hp
TDDE31
Main field of study
Information Technology Computer Science and Engineering Computer ScienceCourse level
Second cycleCourse type
Programme courseExaminer
Patrick LambrixDirector of studies or equivalent
Patrick LambrixEducation components
Preliminary scheduled hours: 40 hRecommended self-study hours: 120 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
| Course offered for | Semester | Period | Timetable module | Language | Campus | ECV | |
|---|---|---|---|---|---|---|---|
| 6CDDD | Computer Science and Engineering, M Sc in Engineering | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CDDD | Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CDDD | Computer Science and Engineering, M Sc in Engineering (Medical Informatics) | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CMJU | Computer Science and Software Engineering, M Sc in Engineering | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CMJU | Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6MICS | Computer Science, Master's Programme | 2 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6MDAV | Computer Science, Master's Programme | 2 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6MICS | Computer Science, Master's Programme (AI and Data Mining) | 2 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CITE | Information Technology, M Sc in Engineering | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CITE | Information Technology, M Sc in Engineering (AI and Machine Learning) | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
| 6CITE | Information Technology, M Sc in Engineering (Medical Informatics) | 8 (Spring 2018) | 2 | 1 | English | Linköping, Valla | E |
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
- Computer Science, Master's Programme
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
| LAB1 | Labs | 3 credits | U, G |
| TEN1 | Written exam | 3 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Related courses:
Advanced Data Models and Databases, Parallel Programming, Multicore Programming.
Department
Institutionen för datavetenskapDirector of Studies or equivalent
Patrick LambrixExaminer
Patrick LambrixEducation components
Preliminary scheduled hours: 40 hRecommended self-study hours: 120 h
Course literature
Other
- Artikelsamling 2018.
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB1 | Labs | 3 credits | U, G |
| TEN1 | Written exam | 3 credits | U, 3, 4, 5 |
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 |
X
|
|
|
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 |
|
|
|
|||
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.