Syllabus

Applied Big Data and Cloud Computing

Code
GIK2Q3
Points
7.5 Credits
Level
First Cycle
School
School of Information and Engineering
Subject field
Information Systems (IKA)
Group of Subjects
Informatics/Computer and Systems Sciences
Disciplinary Domain
Technology, 100%
This course can be included in the following main field(s) of study
Information Systems1
Microdata Analysis2
Progression indicator within (each) main field of study
1G2F
2G2F
Approved
Approved, 20 May 2021.
This syllabus is valid from 07 July 2021.
Download as pdf To course page

Learning Outcomes

Upon completion of the course the students will be able to:

Knowledge and understanding
  • Explain concepts and terminology related to Big Data and Cloud Computing.
  • Describe the challenges with Big Data analysis and techniques used to perform Big Data analysis in the Cloud.
  • Describe different types of Cloud platforms and their advantages and disadvantages for Big Data analysis such as scalability and performance in different contexts.

Skills and abilities
  • Use a cloud-based platform to store, update, and manage Big Data.
  • Create data-driven models for Big Data analysis based on existing frameworks.
  • Apply data-driven models by deploying them to the Cloud.

Judgement and evaluation
  • Suggest and provide reason for a suitable Cloud solution for a Big Data analysis problem.

Course Content

The course covers data science concepts, techniques, and tools to support Big Data analysis. The course also covers popular cloud platforms as well as design and development of cloud applications. As well, the course includes a project on Big Data analytics with Cloud Computing infrastructures, basics of Cloud Computing, Big Data analysis, cloud infrastructure, monitoring and control of Cloud / Big Data solutions.

Assessment

Project work (3.5 credits), presentation of laboratory assignments (2 credits), and seminars (2 credits)

Forms of Study

Lectures, seminars, laboratory work, supervision, and project work.

Grades

The Swedish grades U–VG.

Project work (U, G, VG), Presentation of laboratory assignments (U, G), Seminars (U, G).
To pass the course with distinction (VG), students require a grade of VG in the project work.

Prerequisites

  • At least 60 credits in the main field of studies of Informatics, including the courses Object-orienterad Design and Problem Solving, 7.5 credits, Artificial intelligence, 7.5 credits, Database Systems, 7.5 credits and Data Science & machine learning, 7.5 credits