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Data Science Methods for MADS RUG - Summary 2021-2022
Summary of the required articles of the course Data Science Methods for MADS of the University of Groningen (2021-2022). Included material is:
- Chapter 11.4 Evaluation of Statistical Models from Blattberg (2008)
- Kubler et al. (2017) Machine Learning and Big Data, chapter 19
- The predictive ability of different customer feedback metrics for retention, De Haan et al. (2015)
- Neslin et al. (2006) Defection detection: measuring and understanding the predictive accuracy of customer churn models
- Lemmens and Croux (2006) Bagging and boosting classification trees to predict churn
- Holtrop et al. (2017) No future without the past? predicting churn in the face of customer privacy
- Elements of statistical learning, Hastie et al. Chapter 4, 7 and 9.4
- Misra et al. (2019) Dynamic online pricing with incomplete information using multi armed bandit experiments
- Russo et al. A tutorial on Thomson Sampling Chapters 1-4
- AI ethics guidelines, up to and including chapter II
- ALTAI, including requirement 4, 5 and 7
- Soltys et al. (2015), Ensemble methods for uplift modelling
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Specifications
- School: Rijksuniversiteit Groningen
- Course: Marketing
- Subject: Data Science Methods for MADS
- All documents for this course ›
- Year of study: 2021-2022
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- Section: Summaries
- Made on: 01-17-2022
- Type: .pdf
- Pages: 45
- Language: English
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Subjects of Marketing - Rijksuniversiteit Groningen
More Marketing ›companies, brands, and consumers consumer psychology customer management customer models data engineering for mads data science methods for mads digital marketing intelligence international marketing market models marketing communication retail & omnichannel marketing statistical learning in marketing strategic marketing
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