Profile
Pieter Schoonees is a lecturer and academic director at the Econometric Institute of the Erasmus School of Economics. His research focuses on developing statistical and machine learning algorithms for dimension reduction and cluster analysis.
Publications
Article (4)
Academic (4)
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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, 1001-1037. Advance online publication. https://doi.org/10.1007/s11634-021-00475-2
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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), Article 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
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. (2015). cds: Constrained Dual Scaling for Detecting Response Styles. Software, The Comprehensive R Archive Network.
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Schoonees, P. (2015). lsbclust: Least-Squares Bilinear Clustering for Three-Way Data. Software, The Comprehensive R Archive Network.
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Schoonees, P. (2014). vdg: Variance Dispersion Graphs and Fraction of Design Space Plots. Software, The Comprehensive R Archive Network.
Activities
Additional positions (1)
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Erasmus Quantitative IntelligenceStart date approval: 26 Oct 2022End date approval: 25 Oct 2025Place: ROTTERDAMDescription: Teaching in the post-master programme
Courses
Programming for Data Science and Marketing Analytics
- Study year: 2024/2025, 2023/2024, 2022/2023
- Code: FEM11151
Thesis Hub Master Econometrics & MS
- Study year: 2024/2025
- Code: FEM61008H
Machine Learning I
- Study year: 2024/2025
- Code: TIC10200
- Level: Master
Machine Learning II
- Study year: 2024/2025
- Code: TIC10203
- Level: Master
Machine Learning I
- Study year: 2024/2025
- Code: TIF20200
- Level: Master
Machine Learning II
- Study year: 2024/2025
- Code: TIF20203
- Level: Master
Past courses
Machine Learning 1
- Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20102
- Level: Master
Machine Learning 2
- Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20103
- Level: Master
Machine Learning I
- Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20102-F
- Level: Master
Machine Learning II
- Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021
- Code: EBDS20103-F
- Level: Master
Thesis Hub Master Econometrics & MS
- Study year: 2023/2024, 2022/2023
- Code: FEM61008
Applied Statistics 1
- Study year: 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2017/2018, 2016/2017, 2015/2016
- Code: FEB11005X
- Level: Bachelor 1
Big Data Analytics for Marketing Insight
- Study year: 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2018/2019, 2017/2018, 2016/2017, 2015/2016
- Code: BMME063
- Level: Master, Master, Master, Master
Machine Learning & Learning Algorithms
- Study year: 2022/2023, 2021/2022, 2020/2021
- Code: BM05BAM
- Level: 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
- 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
