Data + Design: A Simple Introduction to Preparing and Visualizing Information

Visualizing Data is about visualization tools that provide deep insight into the structure of data. But the book is much more than just a compendium of useful tools. It conveys a strategy for data analysis that stresses the use of visualization to thoroughly study the structure of data and to check the validity of statistical models fitted to data.

The book demonstrates this by reanalyzing many data sets from the scientific literature, revealing missed effects and inappropriate models fittedto data.

This book explains important data concepts in simple language. Think of it as an in-depth data FAQ for graphic designers, content producers, and less-technical folks who want some extra help knowing where to begin, and what to watch out for when visualizing information.

In this book you will find innovative ideas to unlock the relationships in your own data and create killer visuals to help you transform your next presentation from good to great.

The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks.

Enormous quantities of data go unused or underused today, simply because people can't visualize the quantities and relationships in it.

Title Data + Design: A Simple Introduction to Preparing and Visualizing Information
Author(s) Trinna Chiasson, Dyanna Gregory, et al.
Publisher: Infoactive (2014)
Hardcover/Paperback N/A
eBook HTML, PDF (299 pages, 3.8 MB)
Language: English
ISBN-10: N/A
ISBN-13: N/A
Download: https://infoactive.co/data-design

Data Mining and Analysis: Fundamental Concepts and Algorithms

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics.

This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks.

With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike. Key features: • Covers both core methods and cutting-edge research • Algorithmic approach with open-source implementations • Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas • Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference • Supplementary website with lecture slides, videos, project ideas, and more.

Title Data Mining and Analysis: Fundamental Concepts and Algorithms
Author(s) Mohammed J. Zaki, Wagner Meira, Jr.
Publisher: Cambridge University Press (May 12, 2014)
Hardcover 562 pages
eBook PDF (607 pages, 9.9 MB)
Language: English
ISBN-10: 0521766338
ISBN-13: 978-0521766333
eBook: http://www.dataminingbook.info/pmwiki.php/Main/BookDownload