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Course module: INFOMCTH
INFOMCTH
Computational thinking
Course infoSchedule
Course codeINFOMCTH
ECTS Credits7.5
Category / LevelM (Master)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Science; Graduate School of Natural Sciences;
Contact persondrs. J.D. Fokker
Telephone+31 30 2534118
E-mailJ.D.Fokker@uu.nl
Lecturers
Lecturer
dr. H.L. Bodlaender
Feedback and availability
Other courses by this lecturer
Teaching period
3-GSNS  (05/02/2018 to 20/04/2018)
Teaching period in which the course begins
3-GSNS
Time slotB: TUE-morning, THU-afternoon
Study mode
Full-time
Enrolment periodfrom 30/10/2017 up to and including 26/11/2017
Course application processOsiris
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 22/01/2018 up to and including 18/02/2018
Waiting listYes
Course placement processadministratie onderwijsinstituut
Course goals
After finishing the course successfully, the students will be able to: 
  • analyse data analysis problems from a computational perspective,
  • decompose problems into the individual steps needed to solve it,
  • describe the analysis workflow in the form of UML diagrams,
  • find and use existing tools to implement the individual steps, and
  • implement the overall workflow in Python.
Content
This course is an introduction to computational thinking about data analysis problems, meant for students with little programming experience. Following a problem-based learning approach, they will learn how to get from a data analysis problem to an abstract workflow description and finally to a concrete software program that solves the problem. The course will cover standard processes for approaching data analysis problems (CRISP-DM model), abstract workflow description techniques (UML diagrams), elementary software design principles (reuse, modularisation), and basic programming skills (using the popular Python language). Finally, it will also address workflow management systems and the FAIR data principles.

This is an obligatory course for GSLS students with an Applied Data Science profile.
Entry requirements
You must have a valid study entrance permit
Required materials
-
Recommended materials
Handouts
Most literature will be handed out during the course.
Instructional formats (attendance required)
Lecture (Required)

Tests
Final result
Test weight100
Minimum grade-

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