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Cursus: INFOMCTH
INFOMCTH
Computational thinking
Cursus informatieRooster
CursuscodeINFOMCTH
Studiepunten (ECTS)7,5
Categorie / NiveauM (Master)
CursustypeCursorisch onderwijs
VoertaalEngels
Aangeboden doorFaculteit Betawetenschappen; Graduate School of Natural Sciences;
Contactpersoondr. A.L. Lamprecht
E-mailA.L.Lamprecht@uu.nl
Docenten
Docent
dr. A.L. Lamprecht
Overige cursussen docent
Contactpersoon van de cursus
dr. A.L. Lamprecht
Overige cursussen docent
Blok
3-GSNS  (05-02-2018 t/m 20-04-2018)
Aanvangsblok
3-GSNS
TimeslotB: DI-ochtend, DO-middag, DO-namiddag
Onderwijsvorm
Voltijd
Cursusinschrijving geopendvanaf 30-10-2017 t/m 26-11-2017
AanmeldingsprocedureOsiris
Inschrijven via OSIRISJa
Inschrijven voor bijvakkersJa
VoorinschrijvingNee
Na-inschrijvingJa
Na-inschrijving geopendvanaf 22-01-2018 t/m 18-02-2018
WachtlijstJa
Plaatsingsprocedureadministratie onderwijsinstituut
Cursusdoelen
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.
Inhoud
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.
Competenties
-
Ingangseisen
Je moet voldoen aan de volgende eisen
  • Toelatingsbeschikking voor de master toegekend
Verplicht materiaal
-
Aanbevolen materiaal
Handouts
Most literature will be handed out during the course.
Werkvormen
Hoorcollege

Toetsen
Eindresultaat
Weging100
Minimum cijfer-

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