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Big Data Analytics

2017VT

Status Active - open for registrations
School National Graduate School in Computer Science (CUGS)
Division IDA
Owner Patrick Lambrix
Homepage http://www.ida.liu.se/~patla00/courses/BDA/

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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

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Page responsible: Director of Graduate Studies
Last updated: 2012-05-03