Big Data

1. Introduction to Big Data Analytics (BDA)

• Big Data application framework and methodology.

• Integration of data processing/cleaning, machine learning, data visualization.

Theme 1 will establish an overall framework for a BDA application for the discussions in later themes.

2. Big Data Framework

• Hadoop and MapReduce

• Spark

• Other frameworks

Theme 2 will discuss different BDA frameworks.

3. Implementation of BDA applications

Theme 3 will include practical exercises and projects with BDA technology and frameworks.

Knowledge:

• Have knowledge in the principles and challenges in the design and development of BDA technology.

• Have knowledge in the advantages of different frameworks regarding BDA applications.

Skills:

• Be able to apply appropriate techniques in different BDA problems.

• Hands-on skills on processing raw data.

• Hands-on skills on clustering data to identify meaningful patterns.

• Hands-on skills on applying relevant machine learning algorithms for prediction.

• Hands-on skills on developing data visualization.

• Be able to develop a BDA application/system with all the above-mentioned skills.


General competence:

• Be able to discuss and communicate the possibilities and limitations in the field of BDA.

• Understand the BDA needs from industries, and have the foundational knowledge to develop a solution.

Lectures, video lectures, exercises, mandatory project assignments. 

The number of mandatory project assignments with their due dates will be announced at the start of the course. All mandatory hand-in assignments have to be approved by the course coordinator in order to get access to the oral examination.