Acquérir une compréhension solide de R, l'un des langages les plus utilisés pour l'analyse de données. Explorer les types de données fondamentaux et les valeurs unidimensionnelles, puis progresser vers des structures multidimensionnelles, y compris les matrices, les trames de données et les listes. Développer les compétences nécessaires pour appliquer R efficacement dans l'analyse statistique et la prise de décision basée sur les données.

Acquérir une expérience pratique de l'analyse de données avec R en apprenant à nettoyer, transformer et visualiser des ensembles de données. Explorer les flux de travail essentiels tels que la sélection et le filtrage des données, la gestion des valeurs manquantes et la synthèse des résultats. Développer la confiance dans la préparation des données pour l'extraction d'informations, la création de rapports et l'exploration statistique approfondie.

Master the art of writing and using functions in R, from basic syntax to advanced functional programming techniques. This course guides you through creating your own functions, leveraging anonymous functions, and applying functional programming concepts to solve real-world problems in R.

Une introduction pratique à la programmation R conçue pour les biologistes et les bioinformaticiens. Apprenez à analyser, visualiser et interpréter des données biologiques avec R, à l'aide d'exemples concrets issus de la génomique, de l'écologie et de la biologie expérimentale.

Master practical data science in R by learning data cleaning, modeling, evaluation, and machine learning workflows through hands-on code. Build fluency with R syntax, functions, and outputs for real-world data science tasks.

Master the essentials of control flow in R, including conditional statements, loops, and the powerful apply family of functions. This course is designed for beginners and will guide you through practical, real-world examples to help you write efficient and readable R code.

Master the essential data manipulation skills in R using dplyr, grouping and aggregation, and joining data frames. This course is designed for beginners and provides hands-on, real-world tasks to build your confidence in transforming and analyzing data for analytics.

Learn how economists use R to analyze macroeconomic and market data, build econometric models, and interpret results for policy and market decisions. Focus on regression, time series, and forecasting using real economic datasets.

Master rigorous statistical analysis in R: probability, inference, ANOVA, regression, simulation, and interpretation. Emphasizes statistical reasoning, uncertainty, and correct conclusions using R as a computational tool.

Master the essential data structures in R, including lists, data frames, and factors. Learn how to create, manipulate, and analyze data using R's powerful tools, with engaging explanations and real-world challenges designed for beginners.

Master the essentials of handling text, dates, and files in R. This course guides you through string manipulation, regular expressions, date-time operations, and file I/O, with engaging explanations and practical challenges for beginners.

Learn to analyze financial data in R: work with time series, compute returns, assess risk, analyze portfolios, and visualize and forecast market data using practical, code-driven examples.

Learn to apply R for practical marketing analytics: measure KPIs, analyze customers and cohorts, evaluate campaigns, and visualize insights to drive business decisions.

Master mathematical computation in R: express vectors, matrices, and mathematical structures in code, apply numerical methods, and solve equations and optimization problems with mathematical rigor.

Master the essentials of data visualization and reporting in R. Learn to create compelling graphics, perform exploratory data analysis, and communicate insights effectively using R's powerful tools.