Produkt zum Begriff Big Data Analytics:
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Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.
Preis: 22.46 € | Versand*: 0 € -
Network Security with Netflow and IPFIX: Big Data Analytics for Information Security
A comprehensive guide for deploying, configuring, and troubleshooting NetFlow and learning big data analytics technologies for cyber security Today’s world of network security is full of cyber security vulnerabilities, incidents, breaches, and many headaches. Visibility into the network is an indispensable tool for network and security professionals and Cisco NetFlow creates an environment where network administrators and security professionals have the tools to understand who, what, when, where, and how network traffic is flowing. Network Security with NetFlow and IPFIX is a key resource for introducing yourself to and understanding the power behind the Cisco NetFlow solution. Omar Santos, a Cisco Product Security Incident Response Team (PSIRT) technical leader and author of numerous books including the CCNA Security 210-260 Official Cert Guide, details the importance of NetFlow and demonstrates how it can be used by large enterprises and small-to-medium-sized businesses to meet critical network challenges. This book also examines NetFlow’s potential as a powerful network security tool. Network Security with NetFlow and IPFIX explores everything you need to know to fully understand and implement the Cisco Cyber Threat Defense Solution. It also provides detailed configuration and troubleshooting guidance, sample configurations with depth analysis of design scenarios in every chapter, and detailed case studies with real-life scenarios. You can follow Omar on Twitter: @santosomar NetFlow and IPFIX basics Cisco NetFlow versions and features Cisco Flexible NetFlow NetFlow Commercial and Open Source Software Packages Big Data Analytics tools and technologies such as Hadoop, Flume, Kafka, Storm, Hive, HBase, Elasticsearch, Logstash, Kibana (ELK) Additional Telemetry Sources for Big Data Analytics for Cyber Security Understanding big data scalability Big data analytics in the Internet of everything Cisco Cyber Threat Defense and NetFlow Troubleshooting NetFlow Real-world case studies
Preis: 33.16 € | Versand*: 0 € -
Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World
Distill Maximum Value from Your Digital Data! Do It Now!Why hasn’t all that data delivered a whopping competitive advantage? Because you’ve barely begun to use it, that’s why! Good news: neither have your competitors. It’s hard! But digital marketing analytics is 100% doable, it offers colossal opportunities, and all of the data is accessible to you. Chuck Hemann and Ken Burbary will help you chop the problem down to size, solve every piece of the puzzle, and integrate a virtually frictionless system for moving from data to decision, action to results! Scope it out, pick your tools, learn to listen, get the metrics right, and then distill your digital data for maximum value for everything from R&D to customer service to social media marketing!Prioritize—because you can’t measure and analyze everything Use analysis to craft experiences that profoundly reflect each customer’s needs, expectations, and behaviors Measure real digital media ROI: sales, leads, and customer satisfaction Track the performance of all paid, earned, and owned digital channels Leverage digital data way beyond PR and marketing: for strategic planning, product development, and HR Start optimizing digital content in real time Implement advanced tools, processes, and algorithms for accurately measuring influence Make the most of surveys, focus groups, and offline research synergies Focus new marketing investments where they’ll deliver the most value • Identify and understand your most important audiences across the digital ecosystem“Chuck and Ken lead marketers clearly and efficiently through the minefield of digital marketing measurement. And they do so with a lightness of touch and absence of jargon so rare in this overhyped, much-misunderstood ecosystem.” —Sam Knowles, Founder & MD of Insight Agents; author of Narrative by Numbers: How to Tell Powerful & Purposeful Stories with Data
Preis: 29.95 € | Versand*: 0 € -
Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.
Preis: 36.37 € | Versand*: 0 €
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Was ist Big Data einfach erklärt?
Was ist Big Data einfach erklärt?
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Wie werden Big Data-Analysen dazu genutzt, um Entscheidungen in Unternehmen zu verbessern? Welche Auswirkungen hat die zunehmende Nutzung von Big Data auf die Privatsphäre der Verbraucher?
Big Data-Analysen helfen Unternehmen, Daten aus verschiedenen Quellen zu sammeln, zu analysieren und zu interpretieren, um fundierte Entscheidungen zu treffen. Durch die Nutzung von Big Data können Unternehmen Trends und Muster erkennen, Risiken minimieren und Effizienz steigern. Die zunehmende Nutzung von Big Data kann jedoch die Privatsphäre der Verbraucher gefährden, da persönliche Informationen gesammelt, analysiert und möglicherweise ohne Zustimmung weitergegeben werden. Es ist wichtig, dass Unternehmen transparent sind und Datenschutzrichtlinien einhalten, um das Vertrauen der Verbraucher zu wahren und deren Daten zu schützen. Es besteht auch die Notwendigkeit, Datenschutzgesetze und -vorschriften zu stärken, um die Privatsphäre
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Was sind die potenziellen Auswirkungen von Big Data auf die Privatsphäre und den Datenschutz?
Big Data kann dazu führen, dass persönliche Daten ohne Einwilligung gesammelt und analysiert werden. Dies kann zu Datenschutzverletzungen und Identitätsdiebstahl führen. Es besteht auch die Gefahr, dass Unternehmen und Regierungen die Daten missbrauchen, um das Verhalten der Menschen zu überwachen und zu kontrollieren.
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Was sind die potenziellen Auswirkungen von Big Data auf die Unternehmensführung und die globalen Märkte?
Big Data kann Unternehmen dabei helfen, fundierte Entscheidungen zu treffen und ihre Geschäftsstrategien zu optimieren. Durch die Analyse großer Datenmengen können Trends frühzeitig erkannt und Wettbewerbsvorteile erzielt werden. Gleichzeitig können Datenschutz und Sicherheitsrisiken bei der Verwendung von Big Data eine Herausforderung darstellen.
Ähnliche Suchbegriffe für Big Data Analytics:
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Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners
Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-MakingUsing predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen's holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studiesincluding lessons from failed projects. It's all designed to help you gain a practical understanding you can apply for profit.* Leverage knowledge extracted via data mining to make smarter decisions* Use standardized processes and workflows to make more trustworthy predictions* Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting)* Understand predictive algorithms drawn from traditional statistics and advanced machine learning* Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
Preis: 37.44 € | Versand*: 0 € -
Getting Started with Data Science: Making Sense of Data with Analytics
Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy!Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now.Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories.Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing.You’ll master data science by answering fascinating questions, such as:• Are religious individuals more or less likely to have extramarital affairs?• Do attractive professors get better teaching evaluations?• Does the higher price of cigarettes deter smoking?• What determines housing prices more: lot size or the number of bedrooms?• How do teenagers and older people differ in the way they use social media?• Who is more likely to use online dating services?• Why do some purchase iPhones and others Blackberry devices?• Does the presence of children influence a family’s spending on alcohol?For each problem, you’ll walk through defining your question and the answers you’ll need; exploring howothers have approached similar challenges; selecting your data and methods; generating your statistics;organizing your report; and telling your story. Throughout, the focus is squarely on what matters most:transforming data into insights that are clear, accurate, and can be acted upon.
Preis: 18.18 € | Versand*: 0 € -
Business Intelligence, Analytics, Data Science, and AI, Global Edition
Business Intelligence, Analytics, Data Science, and AI is your guide to the business-related impact of artificial intelligence, data science and analytics, designed to prepare you for a managerial role. The text's vignettes and cases feature modern companies and non-profit organizations and illustrate capabilities, costs and justifications of BI across various business units. With coverage of many data science/AI applications, you'll explore tools, then learn from various organizations' experiences employing such applications. Ample hands-on practice is provided, can be completed with a range of software, and will help you use analytics as a future manager. The 5th Edition integrates the fully updated content of Analytics, Data Science, and Artificial Intelligence, 11/e and Business Intelligence, Analytics, and Data Science, 4/e into one textbook, strengthened by 4 new chapters that will equip you for today's analytics and AI tech, such as ChatGPT. Examples explore analytics in sports, gaming, agriculture and data for good.
Preis: 81.32 € | Versand*: 0 € -
Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.
Preis: 29.95 € | Versand*: 0 €
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Was bedeuten Data Science und Data Engineering?
Data Science bezieht sich auf die Analyse und Interpretation von Daten, um Erkenntnisse und Muster zu gewinnen, die bei der Lösung von Problemen und der Unterstützung von Entscheidungsprozessen helfen. Data Engineering hingegen bezieht sich auf die Entwicklung und Verwaltung von Dateninfrastrukturen, um sicherzustellen, dass Daten effizient erfasst, gespeichert, verarbeitet und analysiert werden können. Data Engineering legt den Fokus auf die technische Seite der Datenverarbeitung, während Data Science sich auf die Analyse und Interpretation der Daten konzentriert.
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Was bedeutet Data Snack?
Was bedeutet Data Snack? Data Snack ist ein Begriff, der sich auf kleine, leicht verdauliche Datenmengen bezieht, die schnell konsumiert werden können. Ähnlich wie ein Snack in der Nahrungswelt sind Data Snacks dazu gedacht, schnell verfügbar und einfach zu konsumieren zu sein. Sie können beispielsweise in Form von kurzen Grafiken, Diagrammen oder Zusammenfassungen präsentiert werden, um wichtige Informationen auf einen Blick zu vermitteln. Data Snacks sind besonders nützlich, um komplexe Daten verständlich und ansprechend darzustellen und so die Entscheidungsfindung zu erleichtern.
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Was bedeutet Data Luv?
Was bedeutet Data Luv? Data Luv ist ein Begriff, der sich auf die Liebe und Leidenschaft für Daten und deren Analyse bezieht. Es beschreibt die tiefe Verbundenheit und das Interesse an der Verarbeitung und Interpretation von Daten, sei es in der Wissenschaft, Wirtschaft oder Technologie. Data Luv steht für die Wertschätzung und den Respekt gegenüber der Bedeutung von Daten für Entscheidungsprozesse und Innovationen. Es symbolisiert auch die Faszination für die Möglichkeiten, die sich durch die Nutzung von Daten ergeben, um Erkenntnisse zu gewinnen und Probleme zu lösen. Letztendlich steht Data Luv für die Begeisterung und Hingabe, die Menschen für die Welt der Daten und deren Potenzial empfinden.
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Wie beeinflusst die Verwendung von Big Data die Entscheidungsfindung in Unternehmen? Wird die Analyse großer Datenmengen zu einem Wettbewerbsvorteil für Unternehmen?
Die Verwendung von Big Data ermöglicht Unternehmen, fundierte Entscheidungen auf Basis von umfangreichen Datenanalysen zu treffen. Durch die Analyse großer Datenmengen können Unternehmen Trends und Muster identifizieren, um ihre Strategien zu optimieren und Wettbewerbsvorteile zu erlangen. Somit kann die Nutzung von Big Data die Entscheidungsfindung in Unternehmen verbessern und zu einem Wettbewerbsvorteil führen.
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