Description

This course aims to cultivate a deep understanding of statistical concepts and their practical application in solving real-life scenarios. The curriculum is structured to foster interactive learning and develop critical thinking skills necessary for data analysis. The course is divided into three main parts:

1. Recap of Probability Knowledge:

   • Application of probability concepts to real-life scenarios.

   • Bayes' theorem, discrete and continuous space, probability density function, cumulative density function, expected value, variance, joint distribution, marginal distribution, covariance, correlation, and various types of distributions.

2. R for Data Analysis: Introduction to the R programming language:

• Writing of R programs for the analysis of datasets, helped by visualization of results. Exploration ofimportant R libraries for graphical representation and table manipulation

1. Strategic Planning: Integration of statistical thinking in strategic decision-making:

   • Application of statistical tools to analyze complex datasets and inform strategic planning initiatives.

   • Simulation real-world scenarios and devise data-driven strategies.

Compétences requises

Bibliographie

• An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

• R for Data Science by Hadley Wickham and Garrett Grolemund.

• Statistical Thinking for Non-Statisticians in Drug Regulation by Richard Kay.

• Probability and Statistics for Computer Scientists by Michael Baron.

• Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan by John K. Kruschke.

Contacts

Responsable(s) de l'enseignement

• Samer El Zant