Описание: In the information age, it is critical that we understand the implications and exposure of the activities and data documented on the Internet. Improved efficiencies and the added capabilities of instant communication, high-speed connectivity to browsers, search engines, websites, databases, indexing, searching and analytical applications have made information technology (IT) and the Internet a vital issued for public and private enterprises. The downside is that this increased level of complexity and vulnerability presents a daunting challenge for enterprise and personal security. Internet Searches for Vetting, Investigations, and Open-Source Intelligence provides an understanding of the implications of the activities and data documented by individuals on the Internet. It delineates a much-needed framework for the responsible collection and use of the Internet for intelligence, investigation, vetting, and open-source information. This book makes a compelling case for action as well as reviews relevant laws, regulations, and rulings as they pertain to Internet crimes, misbehaviors, and individuals’ privacy. Exploring technologies such as social media and aggregate information services, the author outlines the techniques and skills that can be used to leverage the capabilities of networked systems on the Internet and find critically important data to complete an up-to-date picture of people, employees, entities, and their activities. Outlining appropriate adoption of legal, policy, and procedural principles—and emphasizing the careful and appropriate use of Internet searching within the law—the book includes coverage of cases, privacy issues, and solutions for common problems encountered in Internet searching practice and information usage, from internal and external threats. The book is a valuable resource on how to utilize open-source, online sources to gather important information and screen and vet employees, prospective employees, corporate partners, and vendors.
Автор: Maoguo Gong; Qing Cai; Lijia Ma; Shanfeng Wang; Yu Название: Computational Intelligence for Network Structure Analytics ISBN: 9811045577 ISBN-13(EAN): 9789811045578 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.- Network Community Discovery with Evolutionary Single-objective Optimization.- Network Community Discovery with Evolutionary Multi-objective Optimization.- Network Structure Balance Analytics with Evolutionary Optimization.- Network Robustness Analytics with Optimization.- Real-world Cases of Network Structure Analytics.- Concluding Remarks.
Описание: This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.
Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximise YOUR business.
Yes, you have customers that love your product. However, you're having trouble finding new customers and captivating their attention. You realized you're also losing customers, and you have no clue what you can do to prevent this from happening. How do I stand out in a crowd of businesses? How do I target my perfect client and make them choose ME? If this sounds like you, Data Analytics for Businesses if the guide you need.
This book will walk you through the fundamental principles of data science and how to apply the "data analytic mindset" when approaching your business. You will learn how to extract valuable insights from data sources you ALREADY HAVE, and make informed business decisions to help you achieve your goals.
With real-world examples of how to apply data analytics to your business, this book does what others fail to do. Break the process down step by step, so you can optimize unique parts of your business; such as improving customer loyalty or reducing churn. This guide also helps you understand the many data-mining techniques in use today.
Discover the value of applied data science for business decision-making. You'll learn how to think data-analytically, and make connections between data sources to unveil insights you've never imagined. In this book you will learn:
✔︎ Why every company should be leveraging Data Analytics ✔︎ The difference between Big Data, Data Science and Data Analytics. ✔︎ How to achieve your goals by applying data-analytical thinking to your business ✔︎ The recommended data mining techniques for each of your business goals. ✔︎ The most important thing to remember when extracting knowledge from your data. ✔︎ How to use data analytics to improve brand loyalty and customer experience. ✔︎ How to hire the best data scientist, and more.
If you are overwhelmed by this whole new topic of data analytics, don't be. This guide is designed for beginners, with all the guidance you need to understand the fundamentals of harnessing data analytics for your business. So even if you have never heard about data analytics until today, I promise we will walk through this step- by-step.
By the end of this, you'll be able to think analytically and make informed business decisions. This book illustrates how EASY it is to find success by just applying a few principles.
So stop reading this description, and start reading Data Analytics for Businesses instead. Scroll up, and CLICK BUY now
If you want to learn about data science and big data, then keep reading... Two manuscripts in one book:
Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning
Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining
This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in.
There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today.
Some of the topics covered in part 1 of this book include:
What is Data Science?
What Exactly Does a Data Scientist Do?
A Look at What Data Analytics Is All About
What is Data Mining and How Does It Fit in with Data Science?
Regression Analysis
Why is Data Visualization So Important When It Comes to Understanding Your Data?
How to work with Database Querying
A Look at Artificial Intelligence
What is Machine Learning and How Is It Different from Artificial Intelligence?
What is the Future of Artificial Intelligence and Machine Learning?
And much more
Some of the topics covered in part 2 of this book include:
What is big data, and why is it important?
The five V's behind big data
How big data is already impacting your life, and where big data may be headed
How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things
How companies and governments are using predictive analytics to get ahead of the competition or improve service
How big data is used for fraud detection
How big data can train intelligent computer systems
The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics
Upcoming trends in big data that are sure to have a large impact on your future
Artificial intelligence, and how big data drives its development
What machine learning is and how it is tied to big data
The relationship between big data, data analytics, and business intelligence
Insights into how big data impacts privacy issues
The pros and cons regarding big data
And much, much more
So if you want to learn more about data science and big data, click the "add to cart" button
Описание: This edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. This book will appeal to students, professors, and researchers working in the fields of data science, computational social science, and social network analysis.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Описание: ★This book includes 2 Manuscripts★Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximize YOUR business.
Автор: Gong Maoguo, Cai Qing, Ma Lijia Название: Computational Intelligence for Network Structure Analytics ISBN: 9811351678 ISBN-13(EAN): 9789811351679 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.- Network Community Discovery with Evolutionary Single-objective Optimization.- Network Community Discovery with Evolutionary Multi-objective Optimization.- Network Structure Balance Analytics with Evolutionary Optimization.- Network Robustness Analytics with Optimization.- Real-world Cases of Network Structure Analytics.- Concluding Remarks.
Автор: Ratcliffe Jerry H. Название: Intelligence-Led Policing ISBN: 113885901X ISBN-13(EAN): 9781138859012 Издательство: Taylor&Francis Рейтинг: Цена: 7654.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book offers a comprehensive and engaging introduction to the concepts, processes and practice of intelligence-led policing; drawing on a range of case studies of police operations, it provides a timely synthesis of research, policy and practice.
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