Projets in Data Analytics for Decision Making
 Teacher(s): J.Zuber
 Course given in: English
 ECTS Credits: 6 credits
 Schedule: Autumn Semester 20192020, 2.0h. course + 2.0h exercices (weekly average)
 sessions
 course website

Related programmes:
Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership
Master of Science (MSc) in Management, Orientation Marketing
Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution
Master of Science (MSc) in Management, Orientation Business Analytics
ObjectivesIntegrating the practice and theory of statistics to case studies. Solving real live problems by applying adequate statistical methods. Studying different topics in statistics 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. ContentsDifferent 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 ReferencesBishop, 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 Groebner, D. F., Shannon, P. W. & Fry, P. C. (2014). Business Statistics, A DecisionMaking Approach (9th Edition). Pearson International Edition: New Jersey Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques (2nd 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: NewYork James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer Series in Statistics: NewYork Montgomery, D. C. (2012). Introduction to Statistical Quality Control (7th Edition). John Wiley & Sons: 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: NewYork 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. (2014). Practical Data Science with R. Manning Publications: NewYork
PrerequisitesBachelor knowledges in statistics EvaluationFirst attempt
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