Dr Pieter Schoonees

Pieter Schoonees

Assistant Professor
Rotterdam School of Management (RSM)
Erasmus University Rotterdam

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

  • Academic (3)
    • van Herk, H., Schoonees, P., Groenen, P., & 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

    • 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

    • Schoonees, P., 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

  • Academic (2)
    • Schoonees, P., Groenen, P., & 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

    • van Herk, H. H., Schoonees, P., Groenen, P., & van Rosmalen, JM. J. (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

  • Academic (3)
    • Schoonees, P. (Author). (2015). cds: Constrained Dual Scaling for Detecting Response Styles. Software, The Comprehensive R Archive Network.

    • Schoonees, P. (Author). (2015). lsbclust: Least-Squares Bilinear Clustering for Three-Way Data. Software, The Comprehensive R Archive Network.

    • 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: 2021/2022, 2020/2021
  • Code: BM05BAM
  • Level: Master

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

Supervised Machine Learning

  • Study year: 2021/2022, 2020/2021
  • Code: EBDS20102
  • Level: Master

Supervised Machine Learning

  • Study year: 2021/2022, 2020/2021
  • Code: EBDS20102-F
  • Level: Master

Unsupervised Machine Learning & Reinforcement Learning

  • Study year: 2021/2022, 2020/2021
  • Code: EBDS20103
  • Level: Master

Unsupervised Machine Learning & Reinforcement Learning

  • Study year: 2021/2022, 2020/2021
  • Code: EBDS20103-F
  • Level: Master

Applied Statistics 1

  • Study year: 2021/2022, 2020/2021, 2019/2020, 2017/2018, 2016/2017, 2015/2016, 2014/2015, 2013/2014, 2012/2013
  • Code: FEB11005X
  • Level: Bachelor 1

Past courses

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, 2012/2013
  • 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, 2012/2013
  • Code: BERMSS008
  • ECTS: 2