- Table View
- List View
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman And Hall/crc The R Ser. #26)
by Deborah Nolan Duncan Temple LangEffectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts
Data Science Landscape: Towards Research Standards And Protocols (Studies in Big Data #38)
by Usha Mujoo Munshi Neeta VermaThe edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.
Data Science of Renewable Energy Integration: The Nexus of Energy, Environment, and Economic Growth (Evolutionary Economics and Social Complexity Science #30)
by Yuichi IkedaThis book covers various data scientific approaches to analyze the issue of grid integration of renewable energy for which the grid flexibility is the key to cope with its intermittency. It provides readers with the scope to view renewable energy integration as establishing a distributed energy network instead of the traditional centralized energy system. Specifically, quantitative valuation system-wise of the levelized cost of energy, which includes both initial cost and various operational costs, enables readers to optimize energy systems in order to minimize economic cost and environmental impact. It is noted, however, that the high cost of integrating renewable energy on a large scale might slow economic growth considerably. Topics addressed in the book also include statistical comparative study of the relationship between energy and economic growth, a graphical model of determinant factors for foreign direct investment in renewable energy, the coupled oscillator model and unit commitment model to capture intermittency of renewable energy, and the network model of evolving micro-grids. The book explains desired innovation to reduce the integration cost significantly using innovative technologies such as energy storage with hydrogen production and vehicle-to-grid technology. Illustrated by careful analysis of selected examples of renewable integration using different types of grid flexibility, this volume is indispensable to readers who make policy recommendations to establish the distributed energy network integrated with large-scale renewable energy by disentangling the nexus of energy, environment, and economic growth.
Data Science on AWS: Implementing End-to-end, Continuous Ai And Machine Learning Pipelines
by Chris Fregly Antje BarthWith this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and moreUse automated machine learning to implement a specific subset of use cases with SageMaker AutopilotDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deploymentTie everything together into a repeatable machine learning operations pipelineExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache KafkaLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Data Science Techniques for Cryptocurrency Blockchains
by Innar LiivThis book brings together two major trends: data science and blockchains. It is one of the first books to systematically cover the analytics aspects of blockchains, with the goal of linking traditional data mining research communities with novel data sources. Data science and big data technologies can be considered cornerstones of the data-driven digital transformation of organizations and society. The concept of blockchain is predicted to enable and spark transformation on par with that associated with the invention of the Internet. Cryptocurrencies are the first successful use case of highly distributed blockchains, like the world wide web was to the Internet. <P><P> The book takes the reader through basic data exploration topics, proceeding systematically, method by method, through supervised and unsupervised learning approaches and information visualization techniques, all the way to understanding the blockchain data from the network science perspective. <P><P> Chapters introduce the cryptocurrency blockchain data model and methods to explore it using structured query language, association rules, clustering, classification, visualization, and network science. Each chapter introduces basic concepts, presents examples with real cryptocurrency blockchain data and offers exercises and questions for further discussion. Such an approach intends to serve as a good starting point for undergraduate and graduate students to learn data science topics using cryptocurrency blockchain examples. It is also aimed at researchers and analysts who already possess good analytical and data skills, but who do not yet have the specific knowledge to tackle analytic questions about blockchain transactions. The readers improve their knowledge about the essential data science techniques in order to turn mere transactional information into social, economic, and business insights.
Data Science Thinking: The Next Scientific, Technological and Economic Revolution (Data Analytics)
by Longbing CaoThis book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book's three parts each detail layers of these different aspects. The book is intended for decision-makers, data managers (e.g., analytics portfolio managers, business analytics managers, chief data analytics officers, chief data scientists, and chief data officers), policy makers, management and decision strategists, research leaders, and educators who are responsible for pursuing new scientific, innovation, and industrial transformation agendas, enterprise strategic planning, a next-generation profession-oriented course development, as well as those who are involved in data science, technology, and economy from an advanced perspective. Research students in data science-related courses and disciplines will find the book useful for positing their innovative scientific journey, planning their unique and promising career, and competing within and being ready for the next generation of science, technology, and economy.
Data Science und Statistik mit R: Anwendungslösungen für die Praxis
by Bernd HeesenData Science trägt wesentlich zu einer schnelleren Nutzbarmachung von Markt-, Kunden- und Nutzerdaten bei, inklusive der Analyse von Daten aus Sozialen Netzwerken. Wo früher klassische Statistik für Berechnungen und Vorhersagen herangezogen wurde, da erlauben heute Open-Source-Werkzeuge wie R Daten in unterschiedlichsten Formaten und aus beliebig vielen Quellen für die Analyse einzulesen, aufzubereiten und mit Hilfe von Methoden der Künstlichen Intelligenz und des Machine Learning zu analysieren. Die Ergebnisse können dann anschließend perfekt visuell dargestellt werden, so dass die Entscheider schnell und effektiv davon profitieren können. Daraus lässt sich ableiten, welche Maßnahmen mit einer vorhersagbaren Wahrscheinlichkeit zur Erreichung der eigenen Ziele geeignet sind, z.B. welcher Preis für ein Angebot die gewünschte Nachfrage erzeugt oder welche Marketingmaßnahme eine gewünschte Zielgruppe erreicht.Dieses Buch vermittelt auf Basis von R, wie Sie Statistik, Data Science, Künstliche Intelligenz und Machine Learning in der Industrie 4.0 nutzen können. Die Anwendungsbeispiele können von Lesern selbst durchgeführt werden, da das Buch die R-Anweisungen beinhaltet. Damit ist das Buch ideal für Studierende und andere Interessierte, die sich Kenntnisse in der Statistiklösung R aneignen wollen.
Data Science with Julia
by Peter Tait Paul McNicholas"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles BouveyronINRIA Chair in Data ScienceUniversité Côte d’Azur, Nice, France
Data Science with R for Psychologists and Healthcare Professionals
by Christian RyanThis introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines.
Data Science Without Makeup: A Guidebook for End-Users, Analysts, and Managers
by Mikhail Zhilkin"Having worked with Mikhail it does not surprise me that he has put together a comprehensive and insightful book on Data Science where down-to-earth pragmatism is the recurring theme. This is a must-read for everyone interested in industrial data science, in particular analysts and managers who want to learn from Mikhail‘s great experience and approach." --Stefan Freyr Gudmundsson, Lead Data Scientist at H&M, former AI Research Lead at King and Director of Risk Analytics and Modeling at Islandsbanki. "It tells the unvarnished truth about data science. Chapter 2 ("Data Science is Hard") is worth the price on its own—and then Zhilkin gives us processes to help. A must-read for any practitioner, manager, or executive sponsor of data science." --Ted Lorenzen, Director of Marketing Analytics at Vein Clinics of America "Mikhail is a pioneer in the applied data science space. His ability to provide innovative solutions to practical questions in a dynamic environment is simply superb. Importantly, Mikhail’s ability to remain calm and composed in high-pressure situations is surpassed only by his humility." --Darren Burgess, High Performance Manager at Melbourne FC, former Head of Elite Performance at Arsenal FC Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting.
Data, Security, and Trust in Smart Cities (Signals and Communication Technology)
by Stan McClellanThis book provides a comprehensive perspective on issues related to the trustworthiness of information in the emerging “Smart City.” Interrelated topics associated with the veracity of information are presented and discussed by authors with authoritative perspectives from multiple fields. The focus on security, veracity, and trustworthiness of information, data, societal structure and related topics in connected cities is timely, important, and uniquely presented. The authors cover issues related to the proliferation of disinformation and the mechanics of trust in modern society. Topical issues include trust in technologies, such as the use of machine learning (ML) and artificial intelligence (AI), the importance of encryption and cybersecurity, and the value of protecting of critical infrastructure. Structural issues include legal and governmental institutions, including the basis and importance of these fundamental components of society. Functional issues also include issues of societal trust related to healthcare, medical practitioners, and the dependence on reliability of scientific results. Insightful background on the development of AI is provided, and the use of this compelling technology in applications spanning networks, supply chains, and business practices are discussed by practitioners with direct knowledge and convincing perspective. These thought-provoking opinions from notable industry, academia, medicine, law, and government leaders provide substantial benefit for a variety of stakeholders.
Data Skills for Media Professionals: A Basic Guide
by Ken Blake Jason ReinekeTeaches the basic, yet all-important, data skills required by today’s media professionals The authors of Data Skills for Media Professionals have assembled a book that teaches key aspects of data analysis, interactive data visualization and online map-making through an introduction to Google Drive, Google Sheets, and Google My Maps, all free, highly intuitive, platform-agnostic tools available to any reader with a computer and a web connection. Delegating the math and design work to these apps leaves readers free to do the kinds of thinking that media professionals do most often: considering what questions to ask, how to ask them, and how to evaluate and communicate the answers. Although focused on Google apps, the book draws upon complementary aspects of the free QGIS geographic information system, the free XLMiner Analysis ToolPak Add-on for Google Sheets, and the ubiquitous Microsoft Excel spreadsheet application. Worked examples rely on frequently updated data from the U.S. Bureau of Labor Statistics, the Federal Election Commission, the National Bridge Inventory of structurally deficient bridges, and other federal sources, giving readers the option of immediately applying what they learn to current data they can localize to any area in the United States. The book offers chapters covering: basic data analysis; data visualization; making online maps; Microsoft Excel and pivot tables; matching records with Excel's VLOOKUP function; basic descriptive and inferential statistics; and other functions, tools and techniques. Serves as an excellent supplemental text for easily adding data skills instruction to courses in beginning or advanced writing and reporting Features computer screen captures that illustrate each step of each procedure Offers downloadable datasets from a companion web page to help students implement the techniques themselves Shows realistic examples that illustrate how to perform each technique and how to use it on the job Data Skills of Media Professionals is an excellent book for students taking skills courses in the more than 100 ACEJMC-accredited journalism and mass communication programs across the United States. It would also greatly benefit those enrolled in advanced or specialized reporting courses, including courses dedicated solely to teaching data skills.
Data Sleuth: Using Data in Forensic Accounting Engagements and Fraud Investigations (Wiley Corporate F&A)
by Leah WietholterStraightforward, practical guidance for working fraud examiners and forensic accountants In Data Sleuth: Using Data in Forensic Accounting Engagements and Fraud Investigations, certified fraud examiner, former FBI support employee, private investigator, and certified public accountant Leah Wietholter delivers a step-by-step guide to financial investigation that can be applied to almost any forensic accounting use-case. The book emphasizes the use of best evidence as you work through problem-solving data analysis techniques that address the common challenge of imperfect and incomplete information. The accomplished author bridges the gap between modern fraud investigation theory and practical applications and processes necessary for working practitioners. She also provides: Access to a complimentary website with supplementary resources, including a Fraud Detection Worksheet and case planning template Strategies for systematically applying the Data Sleuth® framework to streamline and grow your practice Methods and techniques to improve the quality of your work productData Sleuth is an indispensable, hands-on resource for practicing and aspiring fraud examiners and investigators, accountants, and auditors. It’s a one-of-a-kind book that puts a practical blueprint to effective financial investigation in the palm of your hand.
Data Smart
by John W. ForemanData Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data. Each chapter will cover a different technique in a spreadsheet so you can follow along:Mathematical optimization, including non-linear programming and genetic algorithmsClustering via k-means, spherical k-means, and graph modularityData mining in graphs, such as outlier detectionSupervised AI through logistic regression, ensemble models, and bag-of-words modelsForecasting, seasonal adjustments, and prediction intervals through monte carlo simulationMoving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
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 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 Google Looker Studio: A hands-on guide to using Looker Studio for building compelling and effective dashboards
by Sireesha Pulipati Nicholas KellyApply 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 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 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 versus Democracy: How Big Data Algorithms Shape Opinions and Alter the Course of History
by Kris ShafferHuman attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they’ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see. Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results. In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, and other pivotal concepts are analyzed and then expanded upon via fascinating and timely case studies: the 2016 US presidential election, Ferguson, GamerGate, international political movements, and more events that come to affect every one of us. What are the implications of how we engage with information in the digital age? Data versus Democracy explores this topic and an abundance of related crucial questions. We live in a culture vastly different from any that has come before. In a society where engagement is currency, we are the product. Understanding the value of our attention, how organizations operate based on this concept, and how engagement can be used against our best interests is essential in responsibly equipping ourselves against the perils of disinformation.Who This Book Is ForIndividuals who are curious about how social media algorithms work and how they can be manipulated to influence culture. Social media managers, data scientists, data administrators, and educators will find this book particularly relevant to their work.
Data Visualization in Excel: A Guide for Beginners, Intermediates, and Wonks (AK Peters Visualization Series)
by Jonathan SchwabishThis book closes the gap between what people think Excel can do and what they can achieve in the tool. Over the past few years, recognition of the importance of effectively visualizing data has led to an explosion of data analysis and visualization software tools. But for many people, Microsoft Excel continues to be the workhorse for their data visualization needs, not to mention the only tool that many data workers have access to. Although Excel is not a specialist data visualization platform, it does have strong capabilities. The default chart types do not need to be the limit of the tool’s data visualization capabilities, and users can extend its features by understanding some key elements and strategies. Data Visualization in Excel provides a step-by-step guide to creating more advanced and often more effective data visualizations in Excel and is the perfect guide for anyone who wants to create better, more effective, and more engaging data visualizations.
Data Visualization Made Simple: Insights into Becoming Visual
by Kristen SosulskiData Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems.
Data Visualization with Microsoft Power BI: How to Design Savvy Dashboards
by Alex Kolokolov Maxim ZelenskyThe sheer volume of business data has reached an all-time high. Using visualizations to transform this data into useful and understandable information can facilitate better decision-making. This practical book shows data analysts as well as professionals in finance, sales, and marketing how to quickly create visualizations and build savvy dashboards.Alex Kolokolov from Data2Speak and Maxim Zelensky from Intelligent Business explain in simple and clear language how to create brilliant charts with Microsoft Power BI and follow best practices for corporate reporting. No technical background is required. Step-by-step guides help you set up any chart in a few clicks and avoid common mistakes. Also, experienced data analysts will find tips and tricks on how to enrich their reports with advanced visuals.This book helps you understand:The basic rules for classic charts that are used in 90% of business reportsExceptions to general rules based on real business casesBest practices for dashboard designHow to properly set up interactionsHow to prepare data for advanced visualsHow to avoid pitfalls with eye-catching charts
Data Warehouse Requirements Engineering: A Decision Based Approach
by Naveen Prakash Deepika PrakashAs the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue. The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery.