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Cursus: INFOB3DA
INFOB3DA
Data analytics
Cursus informatieRooster
CursuscodeINFOB3DA
Studiepunten (ECTS)7,5
Categorie / Niveau3 (Bachelor Gevorderd)
CursustypeCursorisch onderwijs
VoertaalEngels
Aangeboden doorFaculteit Betawetenschappen; Undergraduate School Bètawetenschappen;
Contactpersoondr. M.J.S. Brinkhuis
Telefoon0302534183
E-mailm.j.s.brinkhuis@uu.nl
Docenten
Contactpersoon van de cursus
dr. M.J.S. Brinkhuis
Overige cursussen docent
Docent
dr. M.J.S. Brinkhuis
Overige cursussen docent
Docent
dr. M.R. Spruit
Overige cursussen docent
Blok
2  (13-11-2017 t/m 02-02-2018)
Aanvangsblok
2
TimeslotB: DI-ochtend, DO-middag, DO-namiddag
Onderwijsvorm
Voltijd
OpmerkingPlease refer to http://www.cs.uu.nl/education/vak.php?vak=INFOB3DA for the latest course information.
Cursusinschrijving geopendvanaf 18-09-2017 t/m 01-10-2017
AanmeldingsprocedureOsiris
Inschrijven via OSIRISJa
Inschrijven voor bijvakkersJa
VoorinschrijvingNee
Na-inschrijvingJa
Na-inschrijving geopendvanaf 23-10-2017 t/m 24-10-2017
WachtlijstJa
Plaatsingsprocedureadministratie onderwijsinstituut
Cursusdoelen
At the end of the course, students should be able to:
  1. Discuss why Life Sciences & Health in particular is a relevant domain for applying DA
  2. State at least three DA processes and discuss their differentiating key aspects
  3. Apply the steps of the CRoss-Industry Standard Process for Data Mining (CRISP-DM)
  4. Apply selected techniques and algorithms to model a dataset from a task-oriented perspective
  5. Structure semi-structured and unstructured data
  6. Integrate external data to evaluate uncovered and derive new knowledge
  7. Relate the potential impact of data quality problems to each step of the DA process
Inhoud
In this Data Analytics (DA) course you will learn how to apply a data-driven approach to problem solving within the Life Sciences & Health domain. Throughout the workshops you will work on several individual DA assignments, on predefined problems/datasets, using R tools. The lectures will provide the theoretical background of how a DA process should be performed according to industry standards. Furthermore, we discuss an overview of popular DA techniques to help match techniques with information needs, including applications of text mining and data enrichment.

The course will be taught in English.
For complete and current course information, please refer to the departmental course page at http://www.cs.uu.nl/education/vak.php?stijl=2&vak=INFOB3DA.
Ingangseisen
Verplicht materiaal
Boek
Peng, R. and Matsui, E. (2015). The Art of Data Science: A Guide for Anyone Who Works with Data.
Handleiding
Chapman, P. et al. (2000). CRISP-DM 1.0 Step-by-step Data Mining Guide.
Aanbevolen materiaal
Artikelen
Vleugel, A., Spruit, M., & Daal, A. van (2010). Historical data analysis through data mining from an outsourcing perspective: the three-phases method. International Journal of Business Intelligence Research, 1(3), 42–65.
Artikelen
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54.
Werkvormen
Hoorcollege

Werkcollege

Toetsen
Eindresultaat
Weging100
Minimum cijfer-

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