Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Onderwijs Informatica en Informatiekunde

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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 137 inschrijvingen
Rooster:De officiële roosters staan ook in Osiris
Docenten:Dit is een oud rooster!
college   di 11.00-12.4546-50 UNNIK-GROEN Matthieu Brinkhuis
Marco Spruit
do 15.15-17.0046 KBG-PANGEA
practicum groep 1        studentassistent RW
groep 2        studentassistent CJ
werkcollege groep 1 do 17.15-19.0046 DDW-0.42 CLZ Vincent Menger
47-51 BBG-201
2-4 BBG-201
groep 2 do 17.15-19.0046 UNNIK-211 Zhengru Shen
47-51 BBG-205
2-4 BBG-205
groep 3        Matthieu Brinkhuis
week: 16di 16-4-201911.00-13.00 uurzaal: EDUC-BETAaanvullende toets
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.

In the data analytics course, you will learn 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) on data analytics applications
  4. Apply selected techniques and algorithms to model a dataset from a task-oriented perspective
  5. Understand, prepare and analyze semi-structured and unstructured data, for example using text analysis
  6. Use external data sources in analyses to derive new insights
  7. Relate the potential negative impact of data quality problems to each step of the CRISP-DM 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]
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 Slack 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.

Data analytics is a unique 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. You will also learn that data analytics is part science and part `art', since in applying methods and searching for findings there is a creative component.

Throughout the workshops you will work on several individual DA assignments, on predefined problems/datasets, using R tools, with a focus on the Life Sciences & Health domain. However, many of these assignments allow for freedom for your own individual approach. Most assignments involve real-world and relevant data sets, often connected to active research.

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.