Tomás Horvath – 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 thoroughly refereed short papers, workshops and doctoral consortium papers of the 22th European Conference on Advances in Databases and Information Systems, ADBIS 2018, held in Budapest, Hungary, in September 2018.The 20 full and the 4 short workshop papers as well as the 3 doctoral consortium papers were carefully reviewed and selected from 54 submissions to the workshops and 6 submissions to the doctoral consortium. Furthermore, there are 10 short papers included, which were accepted for the main conference. The papers are organized according to the 6 workshops and the doctoral consortium: ADBIS 2018 short papers; First Workshop on Advances on Big Data Management, Analytics, Data Privacy and Security, BigDataMAPS 2018; First International Workshop on New Frontiers on Meta-data Management and Usage, M2U 2018; First Citizen Science Applications and Citizen Databases Workshop, CSADB 2018; First International Workshop onArticial Intelligence for Question Answering, AI*QA 2018; First International Workshop on BIG Data Storage, Processing and Mining for Personalized MEDicine, BIGPMED 2018; First Workshop on Current Trends in Contemporary Information Systems and Their Architectures, ISTREND 2018; Doctoral Consortium.

This book comprises the accepted papers presented at the 24th UK Workshop on Computational Intelligence (UKCI 2025) held at Edinburgh Napier University, Scotland, UK from 3rd to 5th September 2025. UKCI is the premier UK event for presenting leading research on diverse aspects of Computational Intelligence. This book covers papers in four main areas: Evolutionary Computing; General AI; Large Language Models and Machine Learning.It highlights recent research developments in the broad field of Computational Intelligence and is of interest researchers from the academic community as well as those in industry seeking a greater understanding of advances in both new Computational Intelligence techniques and applications.