Computational Tools for Data Science

• Select or design algorithms for specific data science applications
• Compare, evaluate, and apply technologies for parallel and distributed applications.
• Implement algorithms for data science and apply them to real world problems
• Compare, evaluate, and apply computational tools and techniques for massive data sets.
• Lookup, explore, and evaluate relevant technologies and literature related to computational tools and techniques for massive data sets.
• Combine computational tools and techniques for massive data sets.
• Analyze scalability of computational tools and techniques for massive data sets.
• Argue clearly for the choices made when designing and developing solutions.

State of the art computational tools and techniques for massive data sets.

Solid programming experience. At minimum a course in programming plus additional experience from another course or programming project. The course uses Python.
The participants are expected to at least have basic experience
in Python. Tutorials are available on the net, e.g.,
https://docs.python.org/3/tutorial/. Only a short brush-up will be provided.

Lectures, exercises, and project work.

Minimum: 20.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.