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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
Olaf HartigDirector of studies or equivalent
Patrick LambrixEducation components
Preliminary scheduled hours: 42 hRecommended self-study hours: 118 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse offered for
- Master of Science in Computer Science and Engineering
- Master of Science in Industrial Engineering and Management
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Industrial Engineering and Management - International
- Master of Science in Applied Physics and Electrical Engineering - International
- Master's Programme in Computer Science
- 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
| 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.
About teaching and examination language
The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows:
- If teaching language is “Swedish”, the course as a whole could be given in Swedish, or partly in English. Examination language is Swedish, but parts of the examination can be in English.
- If teaching language is “English”, the course as a whole is taught in English. Examination language is English.
- If teaching language is “Swedish/English”, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English depending on teaching language.
Other
The course is conducted in a manner where both men's and women's experience and knowledge are made visible and developed.
The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point.
The course is campus-based at the location specified for the course, unless otherwise stated under “Teaching and working methods”. Please note, in a campus-based course occasional remote sessions could be included.
If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.
Department
Institutionen för datavetenskapCourse 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) |
|
X
|
X
|
LAB1
TEN1
|
databases, parallel programming, machine learning |
|
| 1.5 Insight into current research and development work |
X
|
|
|
databaser |
||
| 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 |
|
|
|
course is given in English |
||
| 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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