= BIGDATA - Big Data with NoSQL Databases =
== Level ==
Basic level - First cycle course
== Requirements ==
7.5 credits databases and 7 credits programming
== Aim ==
The overall objective of the course is to give the student knowledge about tools and models for managing large amounts of continuously growing and heterogeneous data from many diverse sources.

After completing the course, the student should be able to:
- identify the challenges and opportunities of Big Data
- describe data sources, types of data and properties of Big Data
- describe the modular architecture of the Hadoop framework
- analyze which forms of representations are appropriate with regard to type of data and application
- analyze the needs and effects of distributed storage and analysis
- manage the collection and storage of Big Data
- apply predictive modeling with Big Data

== Syllabus ==
The course discusses the motivations behind the development of Big Data and the technologies developed to handle the properties of Big Data. These can usually not be handled by traditional database management systems due to the volume, variation and speed of the data with which they are generated. Alternative forms of representations of data have therefore evolved within the NoSQL framework. The course addresses different approaches to NoSQL within Hadoop, which is a modular framework that allows distributed storage and analysis of large amounts of data. The course covers different data sources and types of data, including streaming data. The course also deals with predictive modeling with large amounts of data and gives examples of some typical applications.

== Outline ==
Half speed 
Level: undergraduate 
Credits: 7.5 

Lectures: 12 lectures x 2 hours 
Quizzes: 12 (6 theoretical + 6 practical)
Assignments: 3
Projects: 1 (practical project or literature review)
Written examination: 1

Quizzes and the written examination are individual. Assignments are carried out in groups of two students and projects are carried out in groups of four students.

Scheduled supervision is offered four times during the course. Additional supervision is offered via forums on the course platform.

Teaching is in English.