Recent advances in computer science and decision science have consolidated the role of statistics as a key discipline for extracting knowledge from data, giving rise to what we now know as data science. Today"s university students will face a job market that increasingly demands skills such as identifying credit card fraud, predicting product demand, optimising distribution routes, managing inventories accurately, dynamically adjusting prices according to customer profiles and market conditions, simulating economic scenarios, discerning consumer preferences and, in general, making scientific decisions in complex situations.

‘Statistics and Data Science with R’ is intended as a practical and structured guide to getting started in data analysis with R, one of the most widely used programming languages in both academic and professional settings. It combines statistical theory with practical examples in R, facilitating progressive and rigorous learning. It is ideal for university degrees and specialised courses in Statistics, Data Science, Applied Economics or Quantitative Methods, helping teachers to structure their classes around real problems and to teach statistical programming from the ground up. The authors of this book are professors in the field of Quantitative Methods for Economics and Business, and belong to the Department of Applied Economics at the Faculty of Economics and Business at the University of Zaragoza. They are also members of the Zaragoza Multi-Criteria Decision Group (GDMZ) research group