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Cursus: INFOB2DA
INFOB2DA
Data analytics
Cursus informatieRooster
CursuscodeINFOB2DA
Studiepunten (ECTS)7,5
Categorie / Niveau2 (Bachelor Verdiepend)
CursustypeCursorisch onderwijs
VoertaalEngels
Aangeboden doorFaculteit Betawetenschappen; Undergraduate School Bètawetenschappen;
Contactpersoondr. M.R. Spruit
Telefoon+31 30 2533708
E-mailM.R.Spruit@uu.nl
Docenten
Docent
dr. A.A.A. Qahtan, PhD
Overige cursussen docent
Docent
dr. M.R. Spruit
Overige cursussen docent
Contactpersoon van de cursus
dr. M.R. Spruit
Overige cursussen docent
Blok
2  (09-11-2020 t/m 05-02-2021)
Aanvangsblok
2
TimeslotB: DI-ochtend, DO-middag, DO-namiddag
Onderwijsvorm
Voltijd
OpmerkingPlease refer to http://www.cs.uu.nl/education/vak.php?vak=INFOB2DA for the latest course information.
Cursusinschrijving geopendvanaf 14-09-2020 t/m 27-09-2020
AanmeldingsprocedureOsiris
Inschrijven via OSIRISJa
Inschrijven voor bijvakkersJa
VoorinschrijvingNee
Na-inschrijvingJa
Na-inschrijving geopendvanaf 26-10-2020 t/m 27-10-2020
WachtlijstJa
Plaatsingsprocedureadministratie onderwijsinstituut
Cursusdoelen
Learning objectives:  

On successful completion of this course, students are able to:
  1. Evaluate different data analytics processes and their differentiating key aspects.
  2. Understand the steps of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) for data analytics applications.
  3. Apply selected techniques and algorithms to a data set from a task-oriented perspective using the CRISP-DM.
  4. Use external data sources in analyses to derive new insights.
  5. Relate the potential negative impact of data quality problems to each step of the CRISP-DM process.
Assessment:
  • Assignments: Students need to complete a minimal number of five out of six assignments to pass the course. One repair assignment is available, if students have missed two assignments in total. It is not possible to repair multiple assignments.
  • Mid-term exam (50%)
  • Final exam (50%)
To have successfully finished INFOWO is a strong recommendation for enrollment in this course.

 
Inhoud
 
Applied data analytics is a multidisciplinary field where you will learn insights needed to make sense of data, research, and observations from everyday life.

You will learn how to apply a data-driven approach to problem solving, but will not only learn about tools, methods, and techniques, or the latest trends, but also more generic insights: why do certain approaches work, why the field is so popular, what common mistakes are made, and so on.

The lectures will provide the theoretical background of how a data analytics process should be performed according to industry standards.

Furthermore, we discuss an overview of popular data analytics techniques to help match techniques with information needs, including applications of text mining and data enrichment.

Format:
  • Two 2-hour lectures per week
  • One 2-hour tutorial per week
Competenties
-
Ingangseisen
-
Verplicht materiaal
Boek
Peng, Roger D., and Elizabeth Matsui. 2016. The Art of Data Science: A Guide for Anyone Who Works with Data. Lulu.com. https://leanpub.com/artofdatascience.
Aanbevolen materiaal
Artikelen
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Springer Texts in Statistics. Springer. https://doi.org/10.1007/978-1-4614-7138-7.
Artikelen
Chapman et al. (2000). “CRISP-DM 1.0: Step-by-Step Data Mining Guide.” Technical report. The CRISP-DM consortium. ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf
Werkvormen
Hoorcollege

Werkcollege

Toetsen
Eindresultaat
Weging100
Minimum cijfer-

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