Products related to Data:
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Efficient Go : Data-Driven Performance Optimization
With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency.However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success. With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress.Author Bartlomiej Plotka provides the tools and knowledge required to make your systems faster and less resource-hungry.Efficient Go guides you in achieving better day-to-day efficiency using Go.In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles. This book shows you how to: Clarify and negotiate efficiency goalsOptimize efficiency on various levelsUse common resources like CPU and memory effectivelyAssess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and ParcaApply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarksEfficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!
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Multivariate Biomarker Discovery : Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine.This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient.This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine.It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them.This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas.
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Innovative Applications of Big Data in the Railway Industry
Use of big data has proven to be beneficial within many different industries, especially in the field of engineering; however, infiltration of this type of technology into more traditional heavy industries, such as the railways, has been limited. Innovative Applications of Big Data in the Railway Industry is a pivotal reference source for the latest research findings on the utilization of data sets in the railway industry.Featuring extensive coverage on relevant areas such as driver support systems, railway safety management, and obstacle detection, this publication is an ideal resource for transportation planners, engineers, policymakers, and graduate-level engineering students seeking current research on a specific application of big data and its effects on transportation.
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Data Analytics for IT Networks : Developing Innovative Use Cases
Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks.Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance.To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts.Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data.He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers.These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data
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What is more data-efficient regarding Two-Factor Authentication (2FA)?
Using a hardware token for Two-Factor Authentication (2FA) is more data-efficient compared to receiving one-time codes via SMS or email. Hardware tokens generate unique codes without the need for an internet connection, reducing the amount of data transmitted and stored. This can be especially beneficial in areas with limited connectivity or for individuals concerned about privacy and security. Additionally, hardware tokens are not susceptible to phishing attacks or SIM swapping, further enhancing their data efficiency and security.
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How can I recover deleted data from my Sony Xperia Z1 Compact phone?
To recover deleted data from your Sony Xperia Z1 Compact phone, you can use a data recovery software specifically designed for Android devices. Connect your phone to your computer and run the data recovery software to scan for deleted files. Once the scan is complete, you can preview the recovered data and select the files you want to restore. It's important to note that the sooner you attempt to recover the deleted data, the higher the chances of successful recovery.
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Do active mobile data consume data volume?
Yes, active mobile data does consume data volume. When your mobile data is turned on and you are using apps, browsing the internet, streaming videos, or downloading files, data is being consumed from your data plan. It is important to monitor your data usage to avoid exceeding your data limit and potentially incurring extra charges from your mobile service provider.
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Do activated mobile data consume data volume?
Yes, activated mobile data does consume data volume. When mobile data is turned on, it allows your device to connect to the internet using your cellular network, and any data used during this connection will be deducted from your data plan. Activities such as browsing the web, streaming videos, or using apps that require an internet connection will all consume data volume when mobile data is activated. It's important to monitor your data usage to avoid exceeding your plan's limits and incurring additional charges.
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PostgreSQL 15 Cookbook : Efficient data management with 100+ recipes
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RJ11 RJ12 6P6C Data Cable, Male to Male Modular Data Cord Straight Wiring Pinout Telephone Handset
RJ11 RJ12 6P6C Data Cable, Male to Male Modular Data Cord Straight Wiring Pinout Telephone Handset
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RJ11 RJ12 6P6C Data Cable, Male to Male Modular Data Cord Straight Wiring Pinout Telephone Handset
RJ11 RJ12 6P6C Data Cable, Male to Male Modular Data Cord Straight Wiring Pinout Telephone Handset
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Digital Investigative Journalism : Data, Visual Analytics and Innovative Methodologies in International Reporting
In the post-digital era, investigative journalism around the world faces a revolutionary shift in the way information is gathered and interpreted.Reporters in the field are confronted with data sources, new logics of information dissemination, and a flood of disinformation.Investigative journalists are working with programmers, designers and scientists to develop innovative tools and hands-on approaches that assist them in disclosing the misuse of power and uncovering injustice. This volume provides an overview of the most sophisticated techniques of digital investigative journalism: data and computational journalism, which investigates stories hidden in numbers; immersive journalism, which digs into virtual reality; drone journalism, which conquers hitherto inaccessible territories; visual and interactive journalism, which reforms storytelling with images and audience perspectives; and digital forensics and visual analytics, which help to authenticate digital content and identify sources in order to detect manipulation.All these techniques are discussed against the backdrop of international political scenarios and globally networked societies. This edited volume, written by renowned international media practitioners and scholars, is full of illuminating insights into digital investigative journalism and addresses professional journalists, journalism researchers and students.
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What are master data and transactional data?
Master data refers to the core data entities of an organization, such as customer, product, employee, and supplier information. This data is typically static and does not change frequently. Master data is used as a reference point for transactional data. On the other hand, transactional data refers to the detailed records of day-to-day business activities, such as sales orders, purchase orders, invoices, and payments. This data is dynamic and changes frequently as business transactions occur. Transactional data is used to track and record the specific activities and events within an organization.
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Is data volume the same as mobile data?
No, data volume and mobile data are not the same. Data volume refers to the amount of data being used or transferred, which can include various types of data such as text, images, videos, etc. On the other hand, mobile data specifically refers to the internet data that is used on a mobile device, typically through a cellular network. Mobile data is a subset of data volume, as data volume can also include data used on other devices or networks.
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What do Data Science and Data Engineering mean?
Data Science involves extracting insights and knowledge from data using various techniques such as statistics, machine learning, and data visualization. Data Scientists are responsible for analyzing and interpreting complex data sets to inform business decisions and solve problems. Data Engineering, on the other hand, focuses on the design and construction of systems for collecting, storing, and processing data. Data Engineers build and maintain the infrastructure that enables data to be accessed and utilized by Data Scientists and other stakeholders. They are also responsible for ensuring the reliability, scalability, and efficiency of data pipelines and storage systems.
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How do you compare experimental data with theoretical data?
Experimental data is collected through direct observation or measurement, while theoretical data is derived from mathematical models or theoretical predictions. To compare the two, one can analyze the agreement or disagreement between the experimental and theoretical results. This can involve statistical analysis, such as calculating the percent difference or correlation coefficient, as well as visual comparison through graphs or plots. Additionally, one can also consider the sources of error in the experimental data and assess the validity of the theoretical model in explaining the observed results.
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