Statistical Thinking And Data Analysis

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

Catalogue2024-2025

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

MCC

Les épreuves indiquées respectent et appliquent le règlement de votre formation, disponible dans l'onglet Documents de la description de la formation.

Régime d'évaluation
ECI (Évaluation continue intégrale)
Coefficient
1.0

Évaluation initiale / Session principale - Épreuves

LibelléType d'évaluationNature de l'épreuveDurée (en minutes)Coéfficient de l'épreuveNote éliminatoire de l'épreuveNote reportée en session 2
Project workLangue utilisée pour l'épreuve : Anglais
SCA1
Practical Work/Project Langue utilisée pour l'épreuve : Anglais
SCPR1