|Data Analytics is a level-3 bachelor course which assumes you have completed the Scientific Research Methods (INFOWO) and Imperative (INFOIMP) 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. In this data analytics course, you will learn to:
For an overview of the schedule with lecture topics and the assignments, please see the course documentation on Blackboard.
Explain why Life Sciences & Health in particular is a relevant domain for applying Data Analytics (DA).
Evaluate different DA processes and their differentiating key aspects.
Apply the steps of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) on data analytics applications.
Apply selected techniques and algorithms to a data set from a task-oriented perspective using the CRISP-DM.
Analyze semi-structured and unstructured data, for example using text analysis.
Use external data sources in analyses to derive new insights.
Relate the potential negative impact of data quality problems to each step of the CRISP-DM process.
There is a total of 6 individual assignments. These assignments are not graded, however, you have to complete a minimal number of 5 to pass the course. Note that 1 repair assignment is available, if you have missed 2 assignments in total. Note that it is not possible to repair multiple assignments.
The final grade will be determined based on the following course components:
Note that the minimum grade of each of these exams is a 5. If for one of the exams your grade is between a 4 and a 5, you can repair that specific exam during the repair session. Note that it is not possible to repair both exams.
Mid-term exam: 50%
Final exam: 50%
Applied data analytics is a multidisciplinary 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. 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.
The course consists of lectures and individual (weekly) assignments. The answers to the assignments are to be submitted to the appropriate section of Blackboard.
The main text for this course is Peng and Matsui (2016), which is is available as PDF, e-book, paperback, but you can also read the latest version online at https://bookdown.org/rdpeng/artofdatascience/.
Peng, Roger D., and Elizabeth Matsui. 2016. The Art of Data Science: A Guide for Anyone Who Works with Data. Lulu.com. https://leanpub.com/artofdatascience.
The second text we use is a technical report by Chapman et al. (2000).
Chapman, Pete, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer, and Rudiger Wirth. 2000. “CRISP-DM 1.0: Step-by-Step Data Mining Guide.” Technical report. The CRISP-DM consortium. ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf.
Lastly, we will use a (small) part of the work by James et al. (2013), which is a standard work in (advanced) data analysis courses. Note that all are available for free online, however, you can also buy copies.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Springer Texts in Statistics. Springer. https://doi.org/10.1007/978-1-4614-7138-7.
Competenties-Ingangseisen |Verplicht materiaal|
|Peng, R. and Matsui, E. (2015). The Art of Data Science: A Guide for Anyone Who Works with Data.|
|Chapman, P. et al. (2000). CRISP-DM 1.0 Step-by-step Data Mining Guide.|
|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.|
|Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54.|