A General Introduction to Data Analytics

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming

A 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 techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text s two main parts

Together 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.

Part I: Introductory Background

Chapter 1: What can we do with data?

Part II: Getting Insights from Data

Chapter 2: Descriptive statistics

Chapter 3: Descriptive Multivariate Analysis

Chapter 4: Data quality and pre–processing

Chapter 5: Clustering

Chapter 6: Frequent pattern mining

Chapter 7: Résumé and project on descriptive analytics

Part III: Predicting the Unknown

Chapter 8: Regression

Chapter 9: Classification

Chapter 10: Additional predictive methods

Chapter 11: Advanced predictive topics

Chapter 12: Résumé and Project on predictive analytics

Part IV: Popular Data Analytics Applications

Chapter 13: Applications for Text, Web and Social Media

João Mendes Moreira, PhD, is an assistant professor in the Faculty of Engineering at the University of Porto, Porto, Portugal and is also a researcher in LIAAD–INESC TEC, Porto, Portugal.

André de Carvalho, PhD, is a full professor in the Institute of Mathematics and Computer Science at the University of São Paulo, Brazil.

Tomá Horváth, PhD, is an assistant professor at the Faculty of Informatics of the Eötvös Loránd University in Budapest, Hungary, and is also associated with the Faculty of Science at the Pavol Jozef afárik University in Ko ice, Slovakia.