Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics by Dan A. Simovici
English | PDF(True) | 2008 | 611 Pages | ISBN : 1848002009 | 12.8 MB
The maturing of the field of data mining has brought about an increased level of mathematical sophistication. Such disciplines like topology, combinatorics, partially ordered sets and their associated algebraic structures (lattices and Boolean algebras), and metric spaces are increasingly applied in data mining research. This book presents these mathematical foundations of data mining integrated with applications to provide the reader with a comprehensive reference.

Methods for the Analysis of Asymmetric Proximity Data
English | 2021 | ISBN: 9811631719 | 203 Pages | PDF EPUB | 15 MB
This book provides an accessible introduction and practical guidelines to apply asymmetric multidimensional scaling, cluster analysis, and related methods to asymmetric one-mode two-way and three-way asymmetric data. A major objective of this book is to present to applied researchers a set of methods and algorithms for graphical representation and clustering of asymmetric relationships. Data frequently concern measurements of asymmetric relationships between pairs of objects from a given set (e.g., subjects, variables, attributes,...), collected in one or more matrices. Examples abound in many different fields such as psychology, sociology, marketing research, and linguistics and more recently several applications have appeared in technological areas including cybernetics, air traffic control, robotics, and network analysis. The capabilities of the presented algorithms are illustrated by carefully chosen examples and supported by extensive data analyses. A review of the specialized statistical software available for the applications is also provided. This monograph is highly recommended to readers who need a complete and up-to-date reference on methods for asymmetric proximity data analysis.

Foundations of Data Intensive Applications - Large Scale Data Analytics under the Hood
pdf | 6.26 MB | English | Isbn:‎ 1119713021 | Author: Kamburugamuve, Supun;Ekanayake, Saliya; | Year: 2021

Beginner's Guide to Data Science
pdf, epub, azw3 | 2.74 MB | English | Isbn:‎ B09CF3V9ZC | Author: Muhammed Ali, Sahla | Year: 2021

S.T.A. DATA 3Muri Pro 13.1.0.0 | 323.9 mb
Languages Supported: Italiano, English, Français, Deutsch, Ελληνικά, Albanian
The software developer S.T.A. DATA is pleased to announce the availability of 3Muri version 13.1 is a software for analysis of structures built in masonry and mixed materials through a non-linear (pushover) and static analysis.

English | 2021 | ASIN : B09CN2F35T | 215 pages | PDF, AZW3, EPUB, MOBI | 11.93 MB
you interested in learning data science with Python? Do you want to know what you need to get started? Then you have picked up the right guide.
As more and more data becomes available and accessible, we need to find bigger and better ways of processing it. That's where data science comes in. It is the future of AI, and that makes it important to understand, if not learn. It's also important to understand the value data science can add to businesses, and by the end of this guide, you will know what it is, how it works, and how it can use data to extract meaningful, valuable insights.

Data Science and SDGs: Challenges, Opportunities and Realities
English | 2021 | ISBN: 9811619182 | 211 Pages | PDF EPUB | 21 MB
The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning.

English | 2021 | ASIN : B0987H12YL | 55 pages | PDF, AZW3, EPUB, MOBI | 6.66 MB
Customers need a hyper-personalized experience and Marketers are always on the lookout for a solid platform that can provide a unified and comprehensive customer profile that can be rightly addressed and serviced.
The answer is a unique digital platform, which serves the very need of marketers and provides a hyper-personalized customer profile, is a Customer Data Platform.

English | 2021 | ISBN: 0367753553 | 297 pages | True PDF | 15.85 MB
Big Data Analytics and Intelligent Techniques for Smart Citiescovers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume.
This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection.

English | 2021 | ISBN: 1492062413 | 263 pages | True PDF | 49.32 MB
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.
Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.