|Website:||website met extra informatie|
|Periode:||periode 2 (week 46 t/m 4, d.w.z. 9-11-2015 t/m 29-1-2016; herkansing week 12)
|Deelnemers:||tot nu toe 83 inschrijvingen|
|Rooster:||De officiële roosters staan ook in Osiris|
|Docenten:||Dit is een oud rooster!
|week: 51||di 19-12-2017||11.00-13.00 uur||zaal: EDUC-BETA|
|week: 5||di 30-1-2018||11.00-13.00 uur||zaal: EDUC-ALFA||aanvullende toets|
At the end of the course, students should be able to:
See the course website for more info.
Discuss why Life Sciences & Health in particular is a relevant domain for applying Data Analytics (DA)
State at least three DA processes and discuss their differentiating key aspects
Apply the CRoss-Industry Standard Process for Data Mining (CRISP-DM)
Select appropriate techniques and algorithms to model a dataset from a task-oriented perspective
Structure semi-structured and unstructured data
Integrate external data to evaluate uncovered and derive new knowledge
Relate the potential impact of data quality problems to each step of the DA process
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:
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.
Project 1: start project pitch: 20%
Written exam: 30%
Project 2: final project report: 50%
|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.