Profile
Pieter Schoonees is an assistant professor in the Department of Marketing Management at RSM, Erasmus University. His expertise lie in the fields of computational statistics, machine learning and psychometrics. Pieter's research focuses on developing statistical and machine learning algorithms and applying these to secondary data. A special interest is the use of such techniques for the analysis of data gathered from neuroscienfic studies.
Publications
Article (4)
Academic (4)
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van Herk, H., Schoonees, P., Groenen, P., & van Rosmalen, J. (2018). Competing for the same value segments? Insight into the volatile Dutch political landscape. PLoS One (online), 13(1), [e0190598]. https://doi.org/10.1371/journal.pone.0190598
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Schoonees, P., Roux, N., & Coetzer, RLJ. (2016). Flexible Graphical Assessment of Experimental Designs in R: The vdg Package. Journal of Statistical Software, 74(3), 1-22. https://doi.org/10.18637/jss.v074.i03
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Schoonees, P., van de Velden, M., & Groenen, P. (2015). Constrained Dual Scaling for Detecting Response Styles in Categorical Data. Psychometrika, 80(4), 968-994. https://doi.org/10.1007/s11336-015-9458-9
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Schoonees, P. C., Groenen, P. J. F., & van de Velden, M. (2021). Least-squares bilinear clustering of three-way data. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-021-00475-2
Doctoral Thesis (1)
Internal (1)
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Schoonees, P. (2015). Methods for Modelling Response Styles. [Doctoral Thesis, Erasmus University Rotterdam]. Erasmus University Rotterdam (EUR).
Report (2)
Academic (2)
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Schoonees, P., Groenen, P., & van de Velden, M. (2015). Least-squares Bilinear Clustering of Three-way Data. (EI report series 2014-23 ed.) Econometric Institute. EI report series Vol. 2014-23
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van Herk, H., Schoonees, P., Groenen, P., & van Rosmalen, JM. (2015). Competing for the Same Value Segments: Explaining the Volatile Dutch Political Landscape. ERIM Report Series Research in Management. http://hdl.handle.net/1765/78753
Software (3)
Academic (3)
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Schoonees, P. (Author). (2015). cds: Constrained Dual Scaling for Detecting Response Styles. Software, The Comprehensive R Archive Network.
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Schoonees, P. (Author). (2015). lsbclust: Least-Squares Bilinear Clustering for Three-Way Data. Software, The Comprehensive R Archive Network.
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Schoonees, P. (Author). (2014). vdg: Variance Dispersion Graphs and Fraction of Design Space Plots. Software, The Comprehensive R Archive Network.
Courses
Machine Learning & Learning Algorithms
- Study year: 2022/2023, 2021/2022, 2020/2021
- Code: BM05BAM
- Level: Master
Supervised Machine Learning
- Study year: 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20102
- Level: Master
Supervised Machine Learning
- Study year: 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20102-F
- Level: Master
Unsupervised Machine Learning
- Study year: 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20103
- Level: Master
Unsupervised Machine Learning
- Study year: 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20103-F
- Level: Master
Applied Statistics 1
- Study year: 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2017/2018, 2016/2017, 2015/2016, 2014/2015, 2013/2014
- Code: FEB11005X
- Level: Bachelor 1
Past courses
Big Data Analytics for Marketing Insight
- Study year: 2021/2022, 2020/2021, 2019/2020, 2018/2019, 2017/2018, 2016/2017, 2015/2016
- Code: BMME063
- Level: Master, Master, Master, Master
Advanced R
- Study year: 2020/2021, 2019/2020
- Code: BERMSKL018
- ECTS: 2 Level: Master, PhD
Machine Learning
- Study year: 2020/2021, 2019/2020, 2018/2019, 2017/2018, 2016/2017, 2015/2016, 2014/2015, 2013/2014
- Code: FEM31002
- Level: Master
Artificial Intelligence: Machine learning for Business Analytics
- Study year: 2019/2020, 2018/2019, 2017/2018
- Code: DBA0008
Introduction to R
- Study year: 2019/2020, 2018/2019
- Code: BERMSKL017
- ECTS: 2 Level: Master
Programming & Visualization for Business Analytics
- Study year: 2019/2020, 2018/2019, 2017/2018, 2016/2017
- Code: DBA0002
Supervised Machine Learning
- Study year: 2019/2020
- Code: TI191
- ECTS: 3 Level: Master
Supervised Machine Learning
- Study year: 2019/2020
- Code: TI198
- Level: Master
Unsupervised & Reinforcement Machine Learning
- Study year: 2019/2020
- Code: TI192
- ECTS: 3 Level: Master
Unsupervised Machine Learning & Reinforcement Learning
- Study year: 2019/2020
- Code: TI199
- ECTS: 4 Level: Master
Advanced R
- Study year: 2018/2019, 2017/2018, 2016/2017
- Code: BERMSKL016
- ECTS: 1 Level: Master
Introduction to Data Analysis with R
- Study year: 2017/2018, 2016/2017
- Code: BERMSKL015
- ECTS: 1 Level: Master
Thesis Clinics Skills Course
- Study year: 2017/2018
- Code: BMRMMM-SKILLS
- Level: Master
Advanced Marketing Decision Models
- Study year: 2016/2017
- Code: BERMASC041
- ECTS: 7 Level: Master
Data Analysis with R
- Study year: 2013/2014
- Code: BERMSS008
- ECTS: 2
