João Moreira – författare

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programmingA General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples.Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniquesA unique illustrative example that extends throughout all the chaptersExercises at the end of each chapter and larger projects at the end of each of the text’s two main partsTogether with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic.The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

This book constitutes the refereed proceedings of the 27th International Conference on Enterprise Design, Operations, and Computing, EDOC 2023, held in Groningen, The Netherlands, during October 30–November 3, 2023.The 12 full papers included in this book were carefully reviewed and selected from 36submissions. They were organized in topical sections as follows: Enterprise Modeling, Enterprise Architecture & Engineering, Model-Based Software Engineering, Enterprise Analysis with Process Mining, Process Improvement & Engineering, and Modeling in an Enterprise Context.