Projets in Data Analytics for Decision Making
- Enseignant(s): J.Zuber
- Titre en français: Projets en analyse de données pour prise de décision
- Cours donné en: anglais
- Crédits ECTS: 6 crédits
- Horaire: Semestre d'automne 2020-2021, 2.0h. de cours + 2.0h. d'exercices (moyenne hebdomadaire)
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séances
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site web du cours
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Formations concernées:
Maîtrise universitaire ès Sciences en management, Orientation business analytics
Maîtrise universitaire ès Sciences en management, Orientation stratégie, organisation et leadership
Maîtrise universitaire ès Sciences en management, Orientation marketing
Maîtrise universitaire ès Sciences en management, Orientation comportement, économie et évolution -
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ObjectifsIntegrating the practice and theory of data science to case studies. Solving real live problems by applying adequate statistical methods. Studying different topics in statistics and data science in order to help students to develop statistical thinking. Learning from data or turning data into knowledge from planning for the collection of data and data management to exploratory data analysis, interpretation of statistical software outputs (tables and graphs), and presentation of results. A case study is proposed to a group of two students; they will have to write a report on their findings and to present it. ContenusDifferent areas of statistics will be covered in this course as for example: - data, exploring data and information visualisation - data mining (knowledge discovery in databases) - big data analytics (different methodological training in data science) - business analytics and management statistics - statistical thinking RéférencesBishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer: Berlin Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. New Riders, Pearson Education: San Francisco Cairo, A. (2019). How Charts Lie: Getting Smarter About Visual Information. W. W. Norton & Company: New York Groebner, D. F., Shannon, P. W. & Fry, P. C. (2017). Business Statistics, A Decision-Making Approach (10th Edition). Pearson International Edition: New Jersey Han, J., Kamber, M. & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd Edition). Morgan Kaufmann Publishers: San Diego Hastie, T., Tibshirani, R. & Friedman, J. H. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction (2nd Edition). Springer Series in Statistics: New-York James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer Series in Statistics: New-York Moore, D. S., McCabe, G. P. & Craig, B. (2007). Introduction to the Practice of Statistics (6th Edition). W. H. Freeman & Co.: New York Nolan, D. & Speed, T. (2001). Stat Labs, Mathematical Statistics Through Applications. Springer Texts in Statistics: New-York Rosling, H., Rosling, O., Rosling Rönnlund, A. (2018). Factfulness. Sceptre: London Wickham, H. & Grolemund, G. (2016). R for Data Science. O’Reilly: Sebastopol Zumel, N. & Mount, J. (2019). Practical Data Science with R (2nd Edition). Manning Publications: New-York
Pré-requisBachelor knowledges in statistics Evaluation1ère tentative
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