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 5, dwz 14-11-2016 t/m 3-2-2017; herkansing week 16)
Timeslot:B
Deelnemers:tot nu toe 99 inschrijvingen
Rooster:De officiële roosters staan ook in Osiris
Docenten:
vormgroeptijdweekzaaldocent
college   di 11.00-12.4546-50 KBG-ATLAS Matthieu Brinkhuis
Marco Spruit
    
2-4 KBG-ATLAS
do 15.15-17.0046-51 KBG-ATLAS
2-4 KBG-ATLAS
werkcollege groep 1 do 17.15-19.0046-51 BBG-223 Zhengru Shen
 
2-4 BBG-223
groep 2 do 17.15-19.0046-51 BBG-083 Vincent Menger
Rob de Wit
   
2-4 BBG-083
groep 3 do 17.15-19.0046-51 BBG-165 Matthieu Brinkhuis
  
2-4 BBG-165
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:
  • 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:To qualify for the second-chance exam, the average grades for the midterm exam and the individual assignments components must be at least 4. Please note that in addition, the second-chance exam is about all the course content, including the topics covered after the mid-term exam.
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.
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