Utbildningsinfo
IN ENGLISH
Malmö universitet
Course syllabus autumn 2025
Course syllabus autumn 2025
Title
Big Data Analytics on Cloud Computing Infrastructures
Swedish title
Storskalig dataanalys med molnbaserad datorinfrastruktur
Course code
DA636E
Credits
15 credits
Grading scale
UA / Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U)
Language of instruction
English
Decision-making body
Faculty of Technology and Society
Establishment date
2022-03-23
Syllabus approval date
2022-03-23
Syllabus valid from
2023-08-28
Entry requirements
  1. Bachelor of Science in computer science or related subjects.
  2. At least 15 credits in programming.
  3. At least 7.5 credits in mathematics.
  4. Knowledge equivalent to English 6 at Swedish upper secondary level
  5. Passing grade in the course Artificial Intelligence for data science
Level
Advanced level
Main field
Computer Science
Progression level
A1F
Progression level in relation to degree requirements
The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).
Course objectives
The purpose of this course is that the student develops a deeper understanding of big data analytics with cloud computing infrastructures, and how software is made available in cloud services. The student also develops his or her ability to handle big data processing using Apache Spark.
Course contents
The course contains the following elements:
  • Ecosystem for big data processing
  • Large-scale data storage (including cloud file systems, cloud object stores, archival storage)
  • Data analytics with Apache Spark
  • Spark’s programming model with RDD
  • Spark applications with Hadoop/AWS
  • Spark SQL
  • Alternatives to SQL-based databases for big data
  • Streaming with Spark
  • Machine learning with Spark MLlib
  • Advanced real-world applications with Spark
Learning outcomes
Knowledge and understanding
For a passing grade the student shall be able to:
  • Demonstrate an in-depth understanding of the data flow programming model for distributed computations for Big Data applications
  • Distinguish between traditional and large-scale database management systems
  • Describe components and programming models used in building big data analysis systems
Competence and abilities
For a passing grade the student shall be able to:
  • Use cloud-based platforms and implement techniques for large-scale data management
  • Analyse large-scale data management problems and construct data-driven models based on open-source frameworks
  • Productionize the trained models by deploying them to the cloud
  • Verbally and in writing present work within Big Data Analytics on Cloud Computing Infrastructures
Evaluation abilities and approach
For a passing grade the student shall be able to:
  • Assess the characteristics of large-scale data frameworks and determine when such frameworks are applicable or not
Learning activities
Lectures, computer laboratories, seminars, project work.
Assessment
Requirements for pass, the course is assessed through:
  • Report and oral presentation in group projects (7 credits, UG),
  • Laboratory assignments (3 credits, UG) and
  • Written examination (5 credits, UA).
An A-E pass requires that all parts have been completed and passed. The final grade is based on the written examination.
Course literature
  • A. Teller, M. Pumperla, M. Malohlava (2015). Advanced Analytics with Spark: Patterns For Learning From Data at Scale. O'Reilly
  • S. Amirgodshi, M. Rajendran, B. Hall, S. Mei (2017), Mastering Machine Learning with Apache Spark 2.x. Packt Publishing
  • A collection of scientific articles will be added to the above mentioned literature.
Course evaluation
The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).
Interim rules
When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.
Additional information
The syllabus is a translation of a Swedish source text.