Big Data Analytics, 6 credits (TDDE31)
Big Data Analytics, 6 hp
Main field of study
Information Technology Computer Science and Engineering Computer ScienceLevel
Second cycleCourse type
Programme courseExaminer
Olaf HartigDirector of studies or equivalent
Patrick LambrixAvailable for exchange students
YesMain field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse 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
TEN1 | Written exam | U, 3, 4, 5 | 3 credits |
LAB1 | Labs | U, G | 3 credits |
Grades
Four-grade scale, LiU, U, 3, 4, 5Course literature
Article collection.
Other information
Related courses: advanced data models and databases, parallel programming, multicore programming.
Department
Institutionen för datavetenskapDirector of Studies or equivalent
Patrick LambrixExaminer
Olaf HartigCourse website and other links
http://www.ida.liu.se/~TDDE31/Education components
Preliminär schemalagd tid: 42 hRekommenderad självstudietid: 118 h
Course literature
Övrigt
Artikelsamling 2018.Other
TEN1 | Written exam | U, 3, 4, 5 | 3 credits |
LAB1 | Labs | U, G | 3 credits |
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