Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Data analytics

Website:website met extra informatie
Studiepunten:7.5 ECTS
Periode:periode 2 (week 46 t/m 5, d.w.z. 13-11-2017 t/m 2-2-2018; herkansing week 16)
Deelnemers:tot nu toe 123 inschrijvingen
Rooster:De officiële roosters staan ook in Osiris
college   di 11.00-12.4546-50 UNNIK-GROEN Matthieu Brinkhuis
Marco Spruit
do 15.15-17.0046 KBG-PANGEA
practicum groep 1        Rob de Wit
groep 2        Cas Jongerius
werkcollege groep 1 do 17.15-19.0046 HFG-611AB Vincent Menger
47-51 HFG-611AB
2-4 HFG-611AB
groep 2 do 17.15-19.0046 RUPPERT-011 Zhengru Shen
47-51 RUPPERT-033
2-4 RUPPERT-033
groep 3 do 17.15-19.0046 RUPPERT-119 Matthieu Brinkhuis
47-51 RUPPERT-119
2-4 RUPPERT-119
week: 51di 19-12-201711.00-13.00 uurzaal: EDUC-BETA
week: 5di 30-1-201811.00-13.00 uurzaal: EDUC-ALFAaanvullende toets
Nota bene:Er is geen recente vakbeschrijving beschikbaar.
Onderstaande tekst is een oude vakbeschrijving uit collegejaar 2016/2017
Inhoud:Data Analytics is a level-3 bachelor course which assumes you have completed the Scientific Research Methods (INFOWO) and Imperative or Mobile Programming (INFOB1MOP), or similar external courses. If you do not have elementary experience on statistics or programming yet, be aware that you will need to put in significantly more time than 20 hours per week in order to be able to complete this course. We therefore advise you not to enroll, then, even though it is formally not prohibited.

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 Data Analytics (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
See the course website for more info.
Literatuur:Kan veranderen!
  • Peng, R. and Matsui, E. (2015). The Art of Data Science: A Guide for Anyone Who Works with Data. [softcopy @LeanPub; hardcopy @Lulu]
  • Chapman, P. Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., and Wirth, R. (2000). CRISP-DM 1.0 Step-by-step Data Mining Guide. [@IBM]
  • Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54. [@AAAI]
  • 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. [@IGI]
Werkvorm:Throughout the course, you are given a number of individual assignments. The answers to the assignments are to be submitted to the appropriate section of the infob3da forum.
Toetsvorm:The final grade will be determined based on the following course components:
  1. Mid-term exam: 40%
  2. Individual assignments: 45%
  3. Participation (including peer review): 15%
Inspanningsverplichting voor aanvullende toets:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.
Beschrijving: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.