Big Data Analytics2017VT
|
|
Course plan
Lectures
Recommended for
The course was last given
new
Goals
After completed the course, the student should 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 MapReduce concept to parallelize common data processing algorithms
- account for how standard machine learning models should be modified in order
to process Big Data
- use tools for machine learning for Big Data
Prerequisites
Recommended: databases, machine learning
Contents
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 Big Data
- Resource management in a cluster environment
- Parallelizing computations for Big Data
- Machine Learning for Big Data
Organization
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. Homework and independent study are a necessary complement to the course.
Literature
Articles and book chapters.
Lecturers
Patrick Lambrix, Christoph Kessler,Jose Pena, Valentina Ivanova, Zlatan Dragisic
Examiner
Patrick Lambrix, Christoph Kessler,Jose Pena
Examination
Labs. Exam.
Credit
6hp
Organized by
Comments
Page responsible: Director of Graduate Studies