Products related to Data:
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Executing Data Quality Projects : Ten Steps to Quality Data and Trusted Information (TM)
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions.Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine.This book includes numerous templates, detailed examples, and practical advice for executing every step.At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face.The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings.The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work.The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean.No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.
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Practical Python Data Wrangling and Data Quality : Getting Started with Reading, Cleaning, and Analyzing Data
The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them.Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations. Complementing foundational concepts with expert advice, author Susan E.McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively.This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sourcesUnderstand and use programming basics in Python to wrangle data at scaleOrganize, document, and structure your code using best practicesCollect data from structured data files, web pages, and APIsPerform basic statistical analyses to make meaning from datasetsVisualize and present data in clear and compelling ways
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Fuzzy Data Matching with SQL : Enhancing Data Quality and Query Performance
If you were handed two different but related sets of data, what tools would you use to find the matches?What if all you had was SQL SELECT access to a database?In this practical book, author Jim Lehmer provides best practices, techniques, and tricks to help you import, clean, match, score, and think about heterogeneous data using SQL. DBAs, programmers, business analysts, and data scientists will learn how to identify and remove duplicates, parse strings, extract data from XML and JSON, generate SQL using SQL, regularize data and prepare datasets, and apply data quality and ETL approaches for finding the similarities and differences between various expressions of the same data. Full of real-world techniques, the examples in the book contain working code.You'll learn how to:Identity and remove duplicates in two different datasets using SQLRegularize data and achieve data quality using SQLExtract data from XML and JSONGenerate SQL using SQL to increase your productivityPrepare datasets for import, merging, and better analysis using SQLReport results using SQLApply data quality and ETL approaches to finding similarities and differences between various expressions of the same data
Price: 47.99 £ | Shipping*: 0.00 £ -
Data Quality Fundamentals : A Practitioner's Guide to Building Trustworthy Data Pipelines
Do your product dashboards look funky? Are your quarterly reports stale? Is the dataset you're using broken or just plain wrong?These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner.If you answered yes to any of the questions above, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem.It doesn't matter how advanced your data infrastructure is if the data you're piping is bad.In this book, Barr Moses, Lior Gavish, and Molly Vorwerck from the data reliability company Monte Carlo explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityProgram your own data quality monitors from scratchDevelop and lead data quality initiatives at your companyGenerate a dashboard to highlight your company's key data assetsAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets
Price: 52.99 £ | Shipping*: 0.00 £
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What is currently trending or popular?
Currently, the trend of sustainability and eco-friendly living is gaining popularity. People are becoming more conscious of their environmental impact and are seeking out sustainable products and practices. Additionally, there is a growing interest in plant-based diets and veganism, as well as a focus on mental health and self-care. In the entertainment world, nostalgic content and throwback fashion from the 90s and early 2000s are also trending.
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How do governments store top-secret data?
Governments store top-secret data through a combination of physical and digital security measures. Physical security includes storing data in highly secure facilities with limited access and surveillance. Digital security involves using encryption, access controls, and secure networks to protect the data from unauthorized access. Additionally, governments may use air-gapped systems, which are completely isolated from the internet, to further safeguard their most sensitive information. Regular security audits and strict protocols for handling and accessing the data are also essential to ensure its protection.
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Which providers offer affordable proxies for high data usage?
There are several providers that offer affordable proxies for high data usage, including Luminati, Smartproxy, and Oxylabs. These providers offer residential proxies that are suitable for high data usage and have pricing plans that cater to different usage needs. It's important to compare the features and pricing of each provider to find the best option for your specific data usage requirements.
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What is the quality of my mobile data connection?
The quality of your mobile data connection can vary depending on factors such as your location, network coverage, and the type of device you are using. You can assess the quality of your mobile data connection by checking the signal strength indicator on your device, running speed tests, and monitoring the consistency of your data connection while browsing or streaming. If you are experiencing slow speeds or frequent disconnections, it may indicate a poor quality data connection. It's also important to consider factors such as network congestion and potential technical issues with your device or network provider.
Similar search terms for Data:
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Data Quality Engineering in Financial Services : Applying Manufacturing Techniques to Data
Data quality will either make you or break you in the financial services industry.Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster.This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines. You'll get invaluable advice on how to: Evaluate data dimensions and how they apply to different data types and use cases Determine data quality tolerances for your data quality specification Choose the points along the data processing pipeline where data quality should be assessed and measured Apply tailored data governance frameworks within a business or technical function or across an organization Precisely align data with applications and data processing pipelines And more
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Deep Learning for Medical Applications with Unique Data
Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used.The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations.The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature.The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions.Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.
Price: 131.00 £ | Shipping*: 0.00 £ -
Managing Data Quality : A practical guide
Data is an increasingly important business asset and enabler for organisational activities.Data quality is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability.This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.
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Deciphering Data Architectures : Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh
Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse.These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion.This practical book provides a guided tour of each architecture to help data professionals understand its pros and cons. In the process, James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features.You'll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality.Best of all, you'll be able to determine the most appropriate data architecture for your needs.By reading this book, you'll:Gain a working understanding of several data architecturesKnow the pros and cons of each approachDistinguish data architecture theory from the realityLearn to pick the best architecture for your use caseUnderstand the differences between data warehouses and data lakesLearn common data architecture concepts to help you build better solutionsAlleviate confusion by clearly defining each data architectureKnow what architectures to use for each cloud provider
Price: 63.99 £ | Shipping*: 0.00 £
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Which low-top sneakers are currently trending?
As of now, some of the low-top sneakers that are currently trending include the Nike Air Force 1, Adidas Stan Smith, and Converse Chuck Taylor All Star. These classic styles have remained popular over the years and continue to be a favorite among sneaker enthusiasts. Additionally, brands like Vans and New Balance have also been gaining popularity for their low-top sneaker offerings.
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How can one top up the prepaid data volume?
To top up prepaid data volume, one can usually do so by purchasing a data top-up package from their mobile service provider. This can be done through the provider's website, mobile app, or by visiting a physical store. The top-up package will typically include a certain amount of data that will be added to the user's existing prepaid plan. Additionally, some providers may offer the option to top up data volume by purchasing a data voucher or by dialing a specific code on their phone.
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How can you top up the prepaid data volume?
You can top up the prepaid data volume by purchasing a data top-up package from your mobile service provider. This can usually be done through their website, mobile app, or by visiting a physical store. Once you have purchased the top-up package, the data volume will be added to your prepaid account, allowing you to continue using data on your mobile device.
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How can you top up additional data volume at netzclub?
To top up additional data volume at netzclub, you can purchase data packages through the netzclub app or website. Simply log in to your netzclub account and navigate to the "Top Up" or "Add-Ons" section where you can choose from a variety of data packages to suit your needs. Once you select the desired data package, you can proceed to make the payment and the additional data volume will be added to your account.
* All prices are inclusive of VAT and, if applicable, plus shipping costs. The offer information is based on the details provided by the respective shop and is updated through automated processes. Real-time updates do not occur, so deviations can occur in individual cases.