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
Vakcode:INFOB3DA
Studiepunten:7.5 ECTS
Periode:periode 2 (week 46 t/m 4, d.w.z. 9-11-2015 t/m 29-1-2016; herkansing week 12)
Timeslot:B
Deelnemers:tot nu toe 83 inschrijvingen
Rooster:De officiële roosters staan ook in Osiris
Docenten:Dit is een oud rooster!
vormgroeptijdweekzaaldocent
college          Marco Spruit
Marjan Askari
Wienand Omta
     
werkcollege groep 1        Vincent Menger
Irene Hinssen
   
groep 2        Zhengru Shen
Donatas Rasiukevicius
  
Tentamen:
week: 51di 19-12-201711.00-13.00 uurzaal: EDUC-BETA
week: 5di 30-1-201811.00-13.00 uurzaal: EDUC-ALFAaanvullende toets
Inhoud:
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 CRoss-Industry Standard Process for Data Mining (CRISP-DM)
  4. Select appropriate 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:
  • 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]
  • Wu et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1–37. [@Springer]
Toetsvorm:The final grade will be determined based on the following course components:
  1. Project 1: start project pitch: 20%
  2. Written exam: 30%
  3. Project 2: final project report: 50%
In addition, 0.5 bonus points will be granted to the final grade of the selected Top-4 start project team members (based on the start project pitch). Similarly, 0.5 penalty points will be subtracted from the final grade of the lowest ranking Top-3 start project team members.
Inspanningsverplichting voor aanvullende toets:In order to qualify for the additional exam, you need to have scored at least a 4.0 ('onafgerond') for each graded course component.
In addition, you need to have completed the MOOC Data Scientist's Toolbox successfully and have sent the certificate of proof (or similar written confirmation) to the course email address.
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 in small project teams on several DA assignments, using free-choice problems/datasets and several software 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 most likely be taught in Dutch, but the course materials will be in English.
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