Products related to Data:
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Direct, Digital & Data-Driven Marketing
In this latest edition of her classic text, Lisa Spiller takes an insightful, in-depth look at contemporary marketing concepts, tactics, and techniques and the dynamic innovations that continue to drive and shape this multi-faceted, multi-dimensional field. Direct, Digital, and Data-Driven Marketing recognizes the growth of the various digital formats as the newest interactive channels for conducting modern marketing.But it does not overlook the traditional principles of direct marketing still relevant today.This book examines the field both as it once was and as it is evolving. With plenty of learning features online resources, the Fifth Edition provides an engaging journey, which will leave any marketing student with a thorough knowledge of how all kinds of businesses manage regular communication with their customer base and target demographic.
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Big Data Analytics : Digital Marketing and Decision-Making
Big Data Analytics: Digital Marketing and Decision-Making covers the advances related to marketing and business analytics.Investment marketing analytics can create value through proper allocation of resources and resource orchestration processes.The use of data analytics tools can be used to improve and speed decision-making processes. Chapters examining analytics for decision-making cover such topics as: Big data analytics for gathering business intelligenceData analytics and consumer behaviorThe role of big data analytics in organizational decision-makingThis book also looks at digital marketing and focuses on such areas as: The prediction of marketing by consumer analyticsWeb analytics for digital marketingSmart retailingLeveraging web analytics for optimizing digital marketing strategiesBig Data Analytics: Digital Marketing and Decision-Making aims to help organizations increase their profits by making better decisions on time through the use of data analytics.It is written for students, practitioners, industry professionals, researchers, and faculty working in the field of commerce and marketing, big data analytics, and organizational decision-making.
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Data Analyst : Careers in data analysis
Data is constantly increasing and data analysts are in higher demand than ever.This book is an essential guide to the role of data analyst.Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to.Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.
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The Digital Journey of Banking and Insurance, Volume III : Data Storage, Data Processing and Data Analysis
This book, the third one of three volumes, focuses on data and the actions around data, like storage and processing.The angle shifts over the volumes from a business-driven approach in “Disruption and DNA” to a strong technical focus in “Data Storage, Processing and Analysis”, leaving “Digitalization and Machine Learning Applications” with the business and technical aspects in-between.In the last volume of the series, “Data Storage, Processing and Analysis”, the shifts in the way we deal with data are addressed.
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How can one study data analysis and data management?
One can study data analysis and data management through a variety of methods. This can include enrolling in formal education programs such as a degree in data science, statistics, or computer science. Additionally, there are many online courses and tutorials available that cover data analysis and data management. Practical experience can also be gained through internships or working on real-world projects. It's important to continuously practice and apply the concepts learned through these methods to gain a deeper understanding of data analysis and data management.
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How to calculate the amount of data in digital video data?
To calculate the amount of data in digital video data, you need to consider the resolution, frame rate, and bit depth of the video. First, calculate the total number of pixels in each frame by multiplying the width by the height of the video resolution. Then, multiply this by the number of frames per second to get the total number of pixels per second. Finally, multiply this by the bit depth (usually 8 bits per color channel) to get the total amount of data per second in bits.
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How do you calculate the amount of data in digital video data?
The amount of data in digital video data can be calculated by multiplying the video resolution (width x height in pixels), the bit depth (number of bits used to represent each color channel), the frame rate (number of frames per second), and the duration of the video in seconds. This will give you the total amount of data in bits. To convert this to a more commonly used unit, such as megabytes or gigabytes, you can divide the total amount of data in bits by 8 to get the amount in bytes, and then divide by 1024 multiple times to convert to larger units.
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How can one intercept network data?
One can intercept network data through various methods such as packet sniffing, man-in-the-middle attacks, and network tapping. Packet sniffing involves capturing data packets as they travel across a network, allowing the interceptor to view the contents of the packets. Man-in-the-middle attacks involve intercepting communication between two parties by positioning oneself between them and relaying the data back and forth. Network tapping involves physically accessing the network cables or devices to intercept the data passing through them. These methods can be used for legitimate purposes such as network troubleshooting or security monitoring, but they can also be exploited for malicious activities.
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Paleontological Data Analysis
PALEONTOLOGICAL DATA ANALYSIS An up-to-date edition of the indispensable guide to analysing paleontological data Paleontology has developed in recent decades into an increasingly data-driven discipline, which brings to bear a huge variety of statistical tools.Applying statistical methods to paleontological data requires a discipline-specific understanding of which methods and parameters are the most appropriate ones, and how to account for statistical bias inherent in the fossil record.By guiding the reader to these and other fundamental questions in the statistical analysis of fossilized specimens, Paleontological Data Analysis has become the standard text for anyone with an interest in quantitative analysis of the fossil record.Now fully updated to reflect the latest statistical methods and disciplinary advances, it is an essential tool for practitioners and students alike. Readers of the second edition of Paleontological Data Analysis readers will also find: New sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more New use cases and examples using PAST, R, and Python software packages Full color illustrations throughout Paleontological Data Analysis is ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields.
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Multivariate Data Analysis
For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis.The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to multivariate analysis for the non-statistician.By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.
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Longitudinal Data Analysis
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature.Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications.It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data.Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters.Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature.They seamlessly blend theory with applications and include examples and case studies from various disciplines.Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future.Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.
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Categorical Data Analysis
Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries.Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses.Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data.This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects modelsTwo new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysisNew sections introducing the Bayesian approach for methods in that chapterMore than 100 analyses of data sets and over 600 exercisesNotes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sourcesA supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
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Is digital data protection important to you?
Yes, digital data protection is very important to me. As a consumer, I want to ensure that my personal information is secure and not vulnerable to cyber attacks or data breaches. As a professional, I understand the importance of protecting sensitive business data and maintaining the trust of clients and customers. In today's digital age, data protection is essential for both personal and professional security.
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How can one eavesdrop on network data?
One can eavesdrop on network data by using various techniques such as packet sniffing, man-in-the-middle attacks, and network tapping. Packet sniffing involves capturing data packets as they travel across a network, allowing the eavesdropper to view the contents of the communication. Man-in-the-middle attacks involve intercepting and relaying communication between two parties without their knowledge. Network tapping involves physically accessing the network infrastructure to monitor the data passing through. These techniques can be used to intercept sensitive information such as passwords, financial data, and personal messages.
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How can data be transferred over a network?
Data can be transferred over a network using various methods such as packet switching, circuit switching, and message switching. In packet switching, data is broken down into small packets, each with its own destination address, and then transmitted over the network. In circuit switching, a dedicated communication path is established between the sender and receiver for the duration of the communication. In message switching, the entire message is sent as a whole from one node to another. These methods allow data to be transferred efficiently and reliably over a network.
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How can data be transmitted over a network?
Data can be transmitted over a network through various methods such as wired connections like Ethernet cables or fiber optics, or wirelessly through technologies like Wi-Fi or cellular networks. When data is sent over a network, it is broken down into smaller packets that are then transmitted from the sender to the receiver. These packets travel through the network, following the most efficient path, and are reassembled at the receiving end to recreate the original data. The process of transmitting data over a network is facilitated by networking protocols and technologies that ensure the reliable and secure delivery of information.
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