Objectifs

Integrating 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.

Contenus

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

Références

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

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-requis

Bachelor knowledges in statistics

Evaluation

1ère tentative

Examen:

Sans examen (cf. modalités)  

Evaluation:

A case study is proposed to a group of two students. For solving a scientific or an economic problem, students have to analyse the data by applying the adequate statistical method, to summarize and interpret outputs (tables and graphs) provided by a statistical software (S-Plus or R) and to write a report on their findings. Groups will present their solutions, and supply their own handouts, outputs and materials.

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

Rattrapage

Examen:

Sans examen (cf. modalités)  

Evaluation:

Reexamination next session: a case study will be given with the same system.

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