- 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.
EduSinglePage
Course offered as a part of a programme
Course, master’s level
15 credits
Malmö | daytime | 50%
1 September 2025 - 18 January 2026
Code for the course: DA636E
About the course
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.
This course is offered as part of programme:
Course content
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
Entry requirements
1. Bachelor of Science (at least 180 higher education credits) in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.
2. Knowledge equivalent to English 6 at Swedish upper secondary level.
3. At least 15 credits in programming.
4. At least 7.5 credits in mathematics.
5. Passing grade in the course Artificial Intelligence for data science (DA631E)
Course 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).
