Objectifs

This course is only open to BA students.

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.

Contenus

Different areas of statistics will be covered in this course as for example:

- data, exploring data and information visualisation

- machine learning techniques

- data mining (knowledge discovery in databases)

- big data analytics (different methodological training in data science)

- business analytics and management statistics

- statistical thinking

Références

Cairo, A. (2021). How Charts Lie: Getting Smarter about Visual Information. W. W. Norton & Company: New York

Efron, B. & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press: Cambridge

James, G., Witten, D., Hastie, T. & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd Edition). Springer Series in Statistics: New-York

Rosling, H. (2018). Factfulness: Ten Reasons We're Wrong About the World-and Why Things Are Better Than You Think. Flatiron Books: New York

Wickham, H. & Grolemund, G. (2017). R for Data Science. O′Reilly: Sebastopol

Zumel, N. & Mount, J. (2020). Practical Data Science with R (2nd Edition). Manning Publications: New York

Pré-requis

Common basis in probability and statistics

Evaluation

1ère tentative

Examen:

Sans examen (cf. modalités)  

Evaluation:

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:

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