Statistical Thinking And Data Analysis

Statistical Thinking And Data Analysis
Licence InformatiqueParcours Computer Sciences (UFAZ) (délocalisé en Azerbaïdjan)

ComposanteUFR de mathématique et d'informatique

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

3. 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

  • Basic understanding of mathematics and probability concepts.

  • Familiarity with programming fundamentals.

Compétences visées

  • Proficiency in statistical thinking and its application in solving real-life problems.

  • Competence in using the R programming language for data analysis.

  • Ability to formulate data-driven strategies based on statistical insights.

Disciplines

  • Informatique

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