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Cursus: BETA-B1PYT
BETA-B1PYT
Programmeren met Python
Cursus informatie
CursuscodeBETA-B1PYT
Studiepunten (EC)7,5
Cursusdoelen
After finishing the course successfully, the students will be able to:
  • think computationally about data analysis problems,
  • decompose problems into the individual steps needed to solve them,
  • describe the analysis workflow in the form of UML diagrams,
  • find and use existing tools and libraries to implement the individual steps,
  • implement the overall workflow in Python, and
  • deliver tested, documented and maintainable Python programs.
Inhoud
This course is an introduction to computational thinking about data analysis problems and the implementation of data analysis programs with Python. It starts at the very basics and is explicitly intended for students who have no or only little programming experience.
Computational thinking is about expressing problems and their solutions in ways that a computer could execute. It is considered one of the fundamental skills of the 21st century. To develop student’s computational thinking skills for data analysis problems, the course will cover ways for systematically approaching such problems (CRISP-DM model, reference processes), abstract program description techniques (UML diagrams) and elementary software design principles (reuse, modularization).
Programming is the process of designing and building an executable computer program for accomplishing a specific computing task. The course will introduce students to programming with Python, which is currently one of the most popular programming languages in data science. After familiarization with the basics (input and output, variables, data types, data structures, conditional branching, loops, functions, etc.) the course will address more advanced topics, such as access to web services, statistical analyses with the pandas package and data visualization with the matplotlib package.
Every lecture will be accompanied by a practical BYOD lab session where students can work on the weekly homework assignments with support of the teaching assistants. To practice the work with more complex, realistic data analysis problems, students will furthermore work on small group projects during the course, and present their results at the end.
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