Course: Programming 5 credits: 5

Course code
BFVM23PROGRAM5
Name
Programming 5
Study year
2023-2024
ECTS credits
5
Language
English
Coordinator
F. Feenstra
Modes of delivery
  • Lecture
Assessments
  • Programming 5 - Assignment

Learning outcomes

  • You identify parallelizable parts of a computational problem.  

  • You analyze the data and software requirements for parallelization of the problem and you adeptly select methods and technologies such as Hadoop, Spark, and other distributed computing frameworks. You provide sound justifications for your choice, or creatively adapt existing methods to develop solutions for the problem. 

  • You evaluate the obtained solution, adhering to the available data and software solutions and engineering best practices in the field. You identify possible improvements or if improvements are at all possible. You iteratively refine and optimize the solution to achieve the most optimal outcome.  

  • You demonstrate professionalism and deliver organized and responsible solutions to computational problems, adhering to FAIR and SE4ML principles. You show awareness of broader and/or commercial applications, emphasizing a practical implementation focus 

Content

This course provides an in-depth exploration of the practical implementation of big data computing, including the challenges of processing and analyzing large data sets, and the use of technologies such as Hadoop, Spark, and other distributed computing frameworks to address these challenges. The course also covers topics such as data modeling, data storage and retrieval, and data analysis techniques for big data. In addition, the course will include a focus on SE4ML, providing students with an understanding of best practices for software engineering in the context of machine learning. 

Throughout the course, students will have the opportunity to work on hands-on lab exercises and gain practical experience in the implementation of big data computing and SE4ML. 

By the end of the course, students will have gained a thorough understanding of big data computing, including the underlying technologies and practical considerations for implementation.  

Included in programme(s)

School(s)

  • Institute for Life Science & Technology
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