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Data Analytics for Decision Making

  • Teacher(s):  
  • Course given in: English
  • ECTS Credits:
  • Schedule: Autumn Semester 2018-2019, 2.0h. course + 2.0h exercices (weekly average)
      WARNING :   this is an old version of the syllabus, old versions contain   OBSOLETE   data.
  •  sessions
  • site web du cours course website
  • Related programmes:

 

Objectives

Integrating 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 develop statistical thinking.

Learning from data or turning data into knowledge from planning for the collection 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.

Contents

Different 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

References

Bishop, 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 Decision-Making 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: 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

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: 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. (2014). Practical Data Science with R. Manning Publications: New-York

Pre-requisites

Bachelor knowledges in statistics

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

CA graded: continuous assessment, final grade according to the following weighting system: 80% for the report and 20% for the presentation.

Retake

Exam:
Without exam (cf. terms)  
Evaluation:

CA graded: continuous assessment, final grade according to the following weighting system: 80% for the report and 20% for the presentation.



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