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Establishment
Language of instruction
English
Teaching content
QUANTITATIVE METHODS
Training officer(s)
F.CHATEAU
Stakeholder(s)
Matthieu BUISINE, Frédéric CHATEAU
Présentation
Prerequisite
Students should be aware of some basic concepts in statistics (variance, cross tables, conditional probabilities), management (marketing) and micro-economy. They also should be informed with multivariate descriptive basic algorithms (PCA, linear model) or have ideas on these topics.
Goal
At the end of the course, the student should be able to:
- Build a data based predictive strategy, formalize a scoring problem
- Carry out a research relying on some discriminant analysis methods and decision trees.
- Evaluate the performance, control the reliability and accuracy of a score
This course aims at giving students a global contractor’s competence AND basic autonomy to address a scoring issue
- Build a data based predictive strategy, formalize a scoring problem
- Carry out a research relying on some discriminant analysis methods and decision trees.
- Evaluate the performance, control the reliability and accuracy of a score
This course aims at giving students a global contractor’s competence AND basic autonomy to address a scoring issue
Presentation
Key words : Data Mining – Scoring – Big Data – Machine learning – Data Science
- Introduction to data-based marketing, risk management and predictive techniques
- Introduction to scoring, ROI and simulation for targeted actions
- Discriminant analysis, Decision trees and Scores.
- Use of a statistical software: data management & statistical methods – carry out a data research and reports
- Interpretation of scores efficiency and reliability
- Alternative statistical or computing approaches: neural networks, k-NN, SVM, random forest.
- Introduction to Text Data, NLP, AI and Text Mining
- Loyalty, up-selling, risk (event, loss and premium), appetence, data strategy
- Introduction to data-based marketing, risk management and predictive techniques
- Introduction to scoring, ROI and simulation for targeted actions
- Discriminant analysis, Decision trees and Scores.
- Use of a statistical software: data management & statistical methods – carry out a data research and reports
- Interpretation of scores efficiency and reliability
- Alternative statistical or computing approaches: neural networks, k-NN, SVM, random forest.
- Introduction to Text Data, NLP, AI and Text Mining
- Loyalty, up-selling, risk (event, loss and premium), appetence, data strategy
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours interactif | 6,67 | ||
| Coaching | 4,00 | ||
| Cours PBL | 6,67 | ||
| Travail personnel | |||
| Group Project | 24,00 | Project teams of 2 or 3 students | |
| Individual Project | 6,67 | 4 practical sessions personal reports based on team work (peer learning in practical sessions) | |
| Autoformation | |||
| E-Learning | 2,40 | ||
| Overall student workload | 48,00 | ||
Evaluation
Assessment mainly relies on students’ competences and ability to engage (and preferably achieve) a data based research. A short MCQ (1h) evaluates student’s knowledge derived from their data experiences. Individual assessments of practical sessions are based on students collaborative work (peer problem solving)
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Contrôle continu | |||
| Participation | 0,00 | 4 | 25,00 |
| Examen (final) | |||
| Examen écrit | 1,50 | 1 | 15,00 |
| Autres | |||
| Projet Collectif | 0,00 | 1 | 60,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
Biernat & Lutz: Data Science - fondamentaux et études de cas. Eyrolles 2017 - tech. -
Provost & Foster: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking , O’Reilly Media, 2013 – an updated practical overview - business -
Stephane Tuffery: Data Mining and Statistics for Decision Making. John Wiley & sons, 2011 – finance industry oriented, management -
Hastie, Tibshirani & Friedman: The elements of statistical Learning, Springer Verlag, 2009 - science -
Provost & Foster: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking , O’Reilly Media, 2013 – an updated practical overview - business -
Stephane Tuffery: Data Mining and Statistics for Decision Making. John Wiley & sons, 2011 – finance industry oriented, management -
Hastie, Tibshirani & Friedman: The elements of statistical Learning, Springer Verlag, 2009 - science -