Probabilités et statistiques

Les cours Probabilités et statistiques enseignent des compétences permettant de déterminer si des données sont significatives ou non, notamment l'optimisation, l'inférence et les tests. Les cours présentent également d'autres méthodes d'analyse de tendances dans des données et leur utilisation pour prévoir, comprendre et améliorer des résultats.

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Learn SQL Basics for Data Science
University of California, Davis
Learn SQL Basics for Data Science
Spécialisation
Statistics with R
Duke University
Statistics with R
Spécialisation
Methods and Statistics in Social Sciences
University of Amsterdam
Methods and Statistics in Social Sciences
Spécialisation
Business Statistics and Analysis
Rice University
Business Statistics and Analysis
Spécialisation
Biostatistics in Public Health
Johns Hopkins University
Biostatistics in Public Health
Spécialisation
Statistics with Python
University of Michigan
Statistics with Python
Spécialisation
Statistical Analysis with R for Public Health
Imperial College London
Statistical Analysis with R for Public Health
Spécialisation
Просто о статистике (с использованием R)
Saint Petersburg State University
Просто о статистике (с использованием R)
Spécialisation
Design of Experiments
Arizona State University
Design of Experiments
Spécialisation
Анализ данных
Novosibirsk State University
Анализ данных
Spécialisation

    Questions fréquentes sur Probabilités et statistiques

  • Probability is the study of the likelihood an event will happen, and statistics is the analysis of large datasets, usually with the goal of either usefully describing this data or inferring conclusions about a larger dataset based on a representative sample. These two branches of mathematics can be considered two sides of a coin: statistics help you to understand the past, and probability helps you use that knowledge to predict the future!

    Statistics and probability are essential tools for data science. These skills enable you to determine whether your data collection methods are sound, derive relevant insights from massive datasets, build analytic models that produce usable results, and much more. Important concepts and skills in the data science context include sampling distributions, statistical significance, hypothesis testing, and regression analysis.