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Computational thinking

Course code:INFOMCTH
Credits:7.5 ECTS
Period:period 3 (week 6 through 15, i.e., 3-2-2020 through 9-4-2020; retake week 27)
Timeslot:D
Participants:up till now 16 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture          Anna-Lena Lamprecht
Mel Chekol
Contents:

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 your computational thinking skills for data analysis problems, the course covers 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 introduces you 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 addresses more advanced topics, such as access to web services, statistical analyses with the pandas package and data visualization with the matplotlib package.

Every lecture is followed by a practical BYOD lab session where you can work on the accompanying exercises with support of the teaching assistants. To practice the work with more complex, realistic data analysis problems, you will furthermore work on a small group project during the course, and present your results at the end.

After finishing the course successfully, you 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.

Literature:

All course material will be provided in digital form.

Exam form:

To be admitted to the exam, you need to have submitted answers (serious attempts) to at least 80% of the exercises.

The grade for the course will be the weighted average of the grades for:

  • Mid-term (30%, individual)
  • Final exam (30%, individual)
  • Project (40%, group work)

To pass the course, all three parts need to be graded with 4 or better, and the weighted average of all parts has to be 6 or better.

Minimum effort to qualify for 2nd chance exam:

To qualify for the retake exam, the grade of the original must be at least 4.

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