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Data Smart: Using Data Science to Transform Information into Insight
by Jordan GoldmeierA straightforward and engaging approach to data science that skips the jargon and focuses on the essentials In the newly revised second edition of Data Smart: Using Data Science to Transform Information into Insight, accomplished data scientist and speaker Jordan Goldmeier delivers an approachable and conversational approach to data science using Microsoft Excel’s easily understood features. The author also walks readers through the fundamentals of statistics, machine learning and powerful artificial intelligence concepts, focusing on how to learn by doing. You’ll also find: Four-color data visualizations that highlight and illustrate the concepts discussed in the book Tutorials explaining complicated data science using just Microsoft Excel How to take what you’ve learned and apply it to everyday problems at work and lifeA must-read guide to data science for every day, non-technical professionals, Data Smart will earn a place on the bookshelves of students, analysts, data-driven managers, marketers, consultants, business intelligence analysts, demand forecasters, and revenue managers.
Data Source Handbook: A Guide to Public Data
by Pete WardenIf you're a developer looking to supplement your own data tools and services, this concise ebook covers the most useful sources of public data available today. You'll find useful information on APIs that offer broad coverage, tie their data to the outside world, and are either accessible online or feature downloadable bulk data. You'll also find code and helpful links.This guide organizes APIs by the subjects they cover—such as websites, people, or places—so you can quickly locate the best resources for augmenting the data you handle in your own service. Categories include:Website tools such as WHOIS, bit.ly, and CompeteServices that use email addresses as search terms, including GithubFinding information from just a name, with APIs such as WhitePagesServices, such as Klout, for locating people with Facebook and Twitter accountsSearch APIs, including BOSS and WikipediaGeographical data sources, including SimpleGeo and U.S. CensusCompany information APIs, such as CrunchBase and ZoomInfoAPIs that list IP addresses, such as MaxMindServices that list books, films, music, and products
Data Spaces: Design, Deployment and Future Directions
by Edward Curry Simon Scerri Tuomo TuikkaThis open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces.The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces.The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy.The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing.The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical.
Data Stewardship for Open Science: Implementing FAIR Principles
by Barend MonsData Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.
Data Storage Architectures and Technologies
by Jiwu ShuData is a core asset in the current development of information technology and needs to be stored efficiently and reliably to serve many important real-world applications such as the Internet, big data, artificial intelligence, and high-performance computing. Generations of researchers and practitioners have continued to innovate the design of storage systems to achieve the goals of high performance, ease of use, and high reliability. This textbook provides a thorough and comprehensive introduction to the field of data storage. With 14 chapters, the book not only covers the basics of storage devices, storage arrays, storage protocols, key-value stores, file systems, network storage architecture, distributed storage systems, storage reliability, storage security, and data protection, but also provides in-depth discussions on advanced topics such as storage maintenance, storage solutions, and storage technology trends and developments (e.g., in-storage computing, persistent memory system, blockchain storage, and in-network storage system). For each section, the authors have attempted to provide the latest current academic and industry research progress that will help readers deepen their understanding and application of basic data storage concepts. This textbook is ideal for storage courses targeting upper-level undergraduate or graduate students in computer science and related disciplines. It also serves as a valuable reference for technical professionals.
Data Storage for Social Networks: A Socially Aware Approach (SpringerBriefs in Optimization)
by Duc A. TranEvidenced by the success of Facebook, Twitter, and LinkedIn, online social networks (OSNs) have become ubiquitous, offering novel ways for people to access information and communicate with each other. As the increasing popularity of social networking is undeniable, scalability is an important issue for any OSN that wants to serve a large number of users. Storing user data for the entire network on a single server can quickly lead to a bottleneck, and, consequently, more servers are needed to expand storage capacity and lower data request traffic per server. Adding more servers is just one step to address scalability. The next step is to determine how best to store the data across multiple servers. This problem has been widely-studied in the literature of distributed and database systems. OSNs, however, represent a different class of data systems. When a user spends time on a social network, the data mostly requested is her own and that of her friends; e.g., in Facebook or Twitter, these data are the status updates posted by herself as well as that posted by the friends. This so-called social locality should be taken into account when determining the server locations to store these data, so that when a user issues a read request, all its relevant data can be returned quickly and efficiently. Social locality is not a design factor in traditional storage systems where data requests are always processed independently. Even for today's OSNs, social locality is not yet considered in their data partition schemes. These schemes rely on distributed hash tables (DHT), using consistent hashing to assign the users' data to the servers. The random nature of DHT leads to weak social locality which has been shown to result in poor performance under heavy request loads. Data Storage for Social Networks: A Socially Aware Approach is aimed at reviewing the current literature of data storage for online social networks and discussing new methods that take into account social awareness in designing efficient data storage.
Data Storytelling and Visualization with Tableau: A Hands-on Approach
by Parikshit Narendra Mahalle Prachi Manoj JoshiWith the tremendous growth and availability of the data, this book covers understanding the data, while telling a story with visualization including basic concepts about the data, the relationship and the visualizations. All the technical details that include installation and building the different visualizations are explained in a clear and systematic way. Various aspects pertaining to storytelling and visualization are explained in the book through Tableau. Features Provides a hands-on approach in Tableau in a simplified manner with steps Discusses the broad background of data and its fundamentals, from the Internet of Everything to analytics Emphasizes the use of context in delivering the stories Presents case studies with the building of a dashboard Presents application areas and case studies with identification of the impactful visualization This book will be helpful for professionals, graduate students and senior undergraduate students in Manufacturing Engineering, Civil and Mechanical Engineering, Data Analytics and Data Visualization.
Data Storytelling in Marketing: How to Tell Persuasive Stories Through Data
by Caroline FlorenceMarketers are storytellers, they write content, marketing strategies and devise internal communications, but unless these stories are evidence-based, they won't be believable or truly persuasive. Understanding how to use data to build and tell stories is an increasingly important part of the modern-day marketers' toolkit. Stories centered on robust evidence and credible data can withstand challenges, provide meaning, offer insight and engage audiences.This book is designed to plug the data storytelling skills gap and enable marketing professionals to cut through the data overload, join the data dots and create engaging narratives and content. Regardless of whether you're a data expert, data anxious or a data sceptic, this book will give you the tools to help you to communicate more effectively with your customers and your stakeholders. Written by expert trainer Caroline Florence, this book outlines how to build robust and compelling data stories. Drawing on her client work with companies such as Toyota, Lactalis, News UK, Mars Petcare and AXA, plus contributions from experts across data, insights, marketing and customer experience, this book provides a practical roadmap to increase your influence with data storytelling.
Data Storytelling with Altair and AI
by Angelica Lo DucaGreat data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data visualization toolkit.Take the fast track to amazing data presentations! Data Storytelling with Altair and AI introduces a stack of useful tools and tried-and-tested methodologies that will rapidly increase your productivity, streamline the visualization process, and leave your audience inspired. In Data Storytelling with Altair and AI you&’ll discover: • Using Python Altair for data visualization • Using Generative AI tools for data storytelling • The main concepts of data storytelling • Building data stories with the DIKW pyramid approach • Transforming raw data into a data story Data Storytelling with Altair and AI teaches you how to turn raw data into effective, insightful data stories. You&’ll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations—and you&’ll love how quick Generative AI makes the whole process! About the technology Every dataset tells a story. After you&’ve cleaned, crunched, and organized the raw data, it&’s your job to share its story in a way that connects with your audience. Python&’s Altair data visualization library, combined with generative AI tools like Copilot and ChatGPT, provide an amazing toolbox for transforming numbers, code, text, and graphics into intuitive data presentations. About the book Data Storytelling with Altair and AI teaches you how to build enhanced data visualizations using these tools. The book uses hands-on examples to build powerful narratives that can inform, inspire, and motivate. It covers the Altair data visualization library, along with AI techniques like generating text with ChatGPT, creating images with DALL-E, and Python coding with Copilot. You&’ll learn by practicing with each interesting data story, from tourist arrivals in Portugal to population growth in the USA to fake news, salmon aquaculture, and more. What's inside • The Data-Information-Knowledge-Wisdom (DIKW) pyramid • Publish data stories using Streamlit, Tableau, and Comet • Vega and Vega-Lite visualization grammar About the reader For data analysts and data scientists experienced with Python. No previous knowledge of Altair or Generative AI required. About the author Angelica Lo Duca is a researcher at the Institute of Informatics and Telematics of the National Research Council, Italy. The technical editor on this book was Ninoslav Cerkez. Table of Contents PART 1 1 Introducing data storytelling 2 Running your first data story in Altair and GitHub Copilot 3 Reviewing the basic concepts of Altair 4 Generative AI tools for data storytelling PART 2 5 Crafting a data story using the DIKW pyramid 6 From data to information: Extracting insights 7 From information to knowledge: Building textual context 8 From information to knowledge: Building the visual context 9 From knowledge to wisdom: Adding next steps PART 3 10 Common issues while using generative AI 11 Publishing the data story A Technical requirements B Python pandas DataFrameC Other chart types
Data Storytelling with Google Looker Studio: A hands-on guide to using Looker Studio for building compelling and effective dashboards
by Nicholas Kelly Sireesha PulipatiApply data storytelling concepts and analytical thinking to create dashboards and reports in Looker Studio to aid data-driven decision makingKey FeaturesGain a solid understanding of data visualization principles and learn to apply them effectivelyGet to grips with the concepts and features of Looker Studio to create powerful data storiesExplore the end-to-end process of building dashboards with the help of practical examplesBook DescriptionPresenting data visually makes it easier for organizations and individuals to interpret and analyze information. Looker Studio is an easy-to-use, collaborative tool that enables you to transform your data into engaging visualizations. This allows you to build and share dashboards that help monitor key performance indicators, identify patterns, and generate insights to ultimately drive decisions and actions. Data Storytelling with Looker Studio begins by laying out the foundational design principles and guidelines that are essential to creating accurate, effective, and compelling data visualizations. Next, you'll delve into features and capabilities of Looker Studio – from basic to advanced – and explore their application with examples. The subsequent chapters walk you through building dashboards with a structured three-stage process called the 3D approach using real-world examples that'll help you understand the various design and implementation considerations. This approach involves determining the objectives and needs of the dashboard, designing its key components and layout, and developing each element of the dashboard. By the end of this book, you will have a solid understanding of the storytelling approach and be able to create data stories of your own using Looker Studio.What you will learnUnderstand what storytelling with data means, and explore its various formsDiscover the 3D approach to building dashboards – determine, design, and developTest common data visualization pitfalls and learn how to mitigate themGet up and running with Looker Studio and leverage it to explore and visualize dataExplore the advanced features of Looker Studio with examplesBecome well-versed in the step-by-step process of the 3D approach using practical examplesMeasure and monitor the usage patterns of your Looker Studio reportsWho this book is forIf you are a beginner or an aspiring data analyst looking to understand the core concepts of data visualization and want to use Looker Studio for creating effective dashboards, this book is for you. No specific prior knowledge is needed to understand the concepts present in this book. Experienced data analysts and business intelligence developers will also find this book useful as a detailed guide to using Looker Studio as well as a refresher of core dashboarding concepts.
Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence
by Bernard MarrBRONZE RUNNER UP: Axiom Awards 2018 - Business Technology Category (1st edition)Data is an integral strategic asset for all businesses. Learn how to leverage this data and generate valuable insights and true business value with bestselling author and data guru Bernard Marr.Data has massive potential for all businesses when used correctly, from small organizations to tech giants and huge multinationals, but this resource is too often not fully utilized. Data Strategy is the must-read guide on how to create a robust, data-driven approach that will harness the power of data to revolutionize your business. Explaining how to collect, use and manage data, this book prepares any organization with the tools and strategies needed to thrive in the digital economy.Now in its second edition, this bestselling title is fully updated with insights on understanding your customers and markets and how to provide them with intelligent services and products. With case studies and real-world examples throughout, Bernard Marr offers unrivalled expertise on how to gain the competitive advantage in a data-driven world.
Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things
by Bernard MarrData is revolutionizing the way we all do business. Every business is now a data business and needs a robust Data Strategy. However less than 0.5% of all data is ever analysed and used, offering huge potential for organisations when trying to leverage this key strategic asset. What is the value of your data and how does it generate business value? Data Strategy, by bestselling author Bernard Marr, provides a clear blueprint showing what organizations need to do to define and execute an effective plan for one of their biggest strategic assets: data. It shows you how to: - define your strategic data assets and data audience - gather the required data and put in place new collection methods - get the most from predictive analytics and machine learning - have the right technology, data infrastructure and key data competencies - ensure you have an effective security and governance system in place to avoid huge financial, legal and reputational problems. Illustrated with case examples of organizations such as Walmart, RBS, Google and NASA, Data Strategy will equip any organization with the tools and strategies it needs to profit from big data, analytics and the Internet of Things.
Data Stream Mining & Processing: Third International Conference, DSMP 2020, Lviv, Ukraine, August 21–25, 2020, Proceedings (Communications in Computer and Information Science #1158)
by Sergii Babichev Olena Vynokurova Dmytro PeleshkoThis book constitutes the proceedings of the third International Conference on Data Stream and Mining and Processing, DSMP 2020, held in Lviv, Ukraine*, in August 2020.The 36 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections of hybrid systems of computational intelligence; machine vision and pattern recognition; dynamic data mining & data stream mining; big data & data science using intelligent approaches.*The conference was held virtually due to the COVID-19 pandemic.
Data Structure Practice: for Collegiate Programming Contests and Education
by Yonghui Wu Jiande WangCombining knowledge with strategies, Data Structure Practice for Collegiate Programming Contests and Education presents the first comprehensive book on data structure in programming contests. This book is designed for training collegiate programming contest teams in the nuances of data structure and for helping college students in computer-related
Data Structure Using C: Theory and Program
by Ahmad Talha Siddiqui Shoeb Ahad SiddiquiData Structures is a central module in the curriculum of almost every Computer Science programme. This book explains different concepts of data structures using C. The topics discuss the theoretical basis of data structures as well as their applied aspects. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan)
Data Structure and Algorithms Using C++: A Practical Implementation
by Sachi Nandan Mohanty Pabitra Kumar TripathyEveryone knows that programming plays a vital role as a solution to automate and execute a task in a proper manner. Irrespective of mathematical problems, the skills of programming are necessary to solve any type of problems that may be correlated to solve real life problems efficiently and effectively. This book is intended to flow from the basic concepts of C++ to technicalities of the programming language, its approach and debugging. The chapters of the book flow with the formulation of the problem, it’s designing, finding the step-by-step solution procedure along with its compilation, debugging and execution with the output. Keeping in mind the learner’s sentiments and requirements, the exemplary programs are narrated with a simple approach so that it can lead to creation of good programs that not only executes properly to give the output, but also enables the learners to incorporate programming skills in them. The style of writing a program using a programming language is also emphasized by introducing the inclusion of comments wherever necessary to encourage writing more readable and well commented programs. As practice makes perfect, each chapter is also enriched with practice exercise questions so as to build the confidence of writing the programs for learners. The book is a complete and all-inclusive handbook of C++ that covers all that a learner as a beginner would expect, as well as complete enough to go ahead with advanced programming. This book will provide a fundamental idea about the concepts of data structures and associated algorithms. By going through the book, the reader will be able to understand about the different types of algorithms and at which situation and what type of algorithms will be applicable.
Data Structures And Abstractions With Java
by Frank M. Carrano Timothy M. HenryUsing the latest features of Java 5, this unique object-oriented presentation introduces readers to data structures via thirty, manageable chapters. KEY FeaturesTOPICS: Introduces each ADT in its own chapter, including examples or applications. Provides aA variety of exercises and projects, plus additional self-assessment questions throughout. the text Includes generic data types as well as enumerations, for-each loops, the interface Iterable, the class Scanner, assert statements, and autoboxing and unboxing. Identifies important Java code as a Listing. Provides Notes and Programming Tips in each chapter. For programmers and software engineers interested in learning more about data structures and abstractions.
Data Structures And Algorithm Analysis In C++
by Mark Allen WeissData Structures and Algorithm Analysis in C++ is an advanced algorithms book that bridges the gap between traditional CS2 and Algorithms Analysis courses. As the speed and power of computers increases, so does the need for effective programming and algorithm analysis. By approaching these skills in tandem, Mark Allen Weiss teaches readers to develop well-constructed, maximally efficient programs using the C++ programming language. This book explains topics from binary heaps to sorting to NP-completeness, and dedicates a full chapter to amortized analysis and advanced data structures and their implementation. Figures and examples illustrating successive stages of algorithms contribute to Weiss’ careful, rigorous and in-depth analysis of each type of algorithm.
Data Structures And Algorithms In Java
by Roberto Tamassia Michael T. Goodrich Michael H. GoldwasserThe design and analysis of efficient data structures has long been recognized as a key component of the Computer Science curriculum. Goodrich, Tomassia and Goldwasser's approach to this classic topic is based on the object-oriented paradigm as the framework of choice for the design of data structures. For each ADT presented in the text, the authors provide an associated Java interface. Concrete data structures realizing the ADTs are provided as Java classes implementing the interfaces. The Java code implementing fundamental data structures in this book is organized in a single Java package, net.datastructures. This package forms a coherent library of data structures and algorithms in Java specifically designed for educational purposes in a way that is complimentary with the Java Collections Framework.
Data Structures I Essentials
by Dennis SmolarskiREA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Data Structures I includes scalar variables, arrays and records, elementary sorting, searching, linked lists, queues, and appendices of binary notation and subprogram parameter passing.
Data Structures II Essentials
by Dennis C. SmolarskiREA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Data Structures II includes sets, trees, advanced sorting, elementary graph theory, hashing, memory management and garbage collection, and appendices on recursion vs. iteration, algebraic notation, and large integer arithmetic.
Data Structures and Abstractions with Java (Fourth Edition)
by Frank M. Carrano Timothy M. HenryA book for an introductory course in data structures, typically known as CS-2.
Data Structures and Algorithm Analysis in C++, Third Edition
by Dr Clifford A. ShafferWith its focus on creating efficient data structures and algorithms, this comprehensive text helps readers understand how to select or design the tools that will best solve specific problems. It uses Microsoft C++ as the programming language and is suitable for second-year data structure courses and computer science courses in algorithm analysis.Techniques for representing data are presented within the context of assessing costs and benefits, promoting an understanding of the principles of algorithm analysis and the effects of a chosen physical medium. The text also explores tradeoff issues, familiarizes readers with the most commonly used data structures and their algorithms, and discusses matching appropriate data structures to applications. The author offers explicit coverage of design patterns encountered in the course of programming the book's basic data structures and algorithms. Numerous examples appear throughout the text.
Data Structures and Algorithm Analysis in Java, Third Edition
by Dr Clifford A. ShafferWith its focus on creating efficient data structures and algorithms, this comprehensive text helps readers understand how to select or design the tools that will best solve specific problems. It uses Java as the programming language and is suitable for second-year data structure courses and computer science courses in algorithm analysis. Techniques for representing data are presented within the context of assessing costs and benefits, promoting an understanding of the principles of algorithm analysis and the effects of a chosen physical medium. The text also explores tradeoff issues, familiarizes readers with the most commonly used data structures and their algorithms, and discusses matching appropriate data structures to applications. The author offers explicit coverage of design patterns encountered in the course of programming the book's basic data structures and algorithms. Numerous examples appear throughout the text.
Data Structures and Algorithms Using C#
by Michael McmillanC# programmers: no more translating data structures from C++ or Java to use in your programs! Mike McMillan provides a tutorial on how to use data structures and algorithms plus the first comprehensive reference for C# implementation of data structures and algorithms found in the . NET Framework library, as well as those developed by the programmer. The approach is very practical, using timing tests rather than Big O notation to analyze the efficiency of an approach. Coverage includes arrays and array lists, linked lists, hash tables, dictionaries, trees, graphs, and sorting and searching algorithms, as well as more advanced algorithms such as probabilistic algorithms and dynamic programming. This is the perfect resource for C# professionals and students alike.