Data capture and analysis

Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges.

Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality.

Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guideKey FeaturesLearn how to acquire and process textual data and visualize the key findingsObtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffsImplement models for solving real-world problems and evaluate their performanceBook DescriptionWith the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field.

E-health applications such as tele-medicine, tele-radiology, tele-ophthalmology, and tele-diagnosis are very promising and have immense potential to improve global healthcare.

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data.

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.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data.

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform.

Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.

Leverage the power of Python and statistical modeling techniques for building accurate predictive modelsKey FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today.

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and KubernetesKey FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book DescriptionThe increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models.

Build a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook DescriptionMachine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data.

Explore distributed ledger technology, decentralization, and smart contracts and develop real-time decentralized applications with Ethereum and SolidityKey FeaturesGet to grips with the underlying technical principles and implementations of blockchainBuild powerful applications using Ethereum to secure transactions and create smart contractsGain advanced insights into cryptography and cryptocurrenciesBook DescriptionBlockchain technology is a distributed ledger with applications in industries such as finance, government, and media.

Solve real-world statistical problems using the most popular R packages and techniquesKey FeaturesLearn how to apply statistical methods to your everyday research with handy recipesFoster your analytical skills and interpret research across industries and business verticalsPerform t-tests, chi-squared tests, and regression analysis using modern statistical techniquesBook DescriptionR is a popular programming language for developing statistical software.

Progressively explore UI development with Shiny via practical examplesKey FeaturesWrite a Shiny interface in pure HTMLExplore powerful layout functions to make attractive dashboards and other intuitive interfacesGet to grips with Bootstrap and leverage it in your Shiny applicationsBook DescriptionAlthough vanilla Shiny applications look attractive with some layout flexibility, you may still want to have more control over how the interface is laid out to produce a dashboard.

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applicationsKey FeaturesBuild independent machine learning (ML) systems leveraging the best features of R 3.

Get unique insights from your data by combining the power of SQL Server, R and PythonKey FeaturesUse the features of SQL Server 2017 to implement the data science project life cycleLeverage the power of R and Python to design and develop efficient data modelsfind unique insights from your data with powerful techniques for data preprocessing and analysisBook DescriptionSQL Server only started to fully support data science with its two most recent editions.

Implement real-world decentralized applications using Python, Vyper, Populus, and EthereumKey FeaturesStay up-to-date with everything you need to know about the blockchain ecosystemImplement smart contracts, wallets, and decentralized applications(DApps) using Python librariesGet deeper insights into storing content in a distributed storage platformBook DescriptionBlockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions.

Advances in Imaging and Electron Physics, Volume 203, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy.

Big Data and Smart Service Systems presents the theories and applications regarding Big Data and smart service systems, data acquisition, smart cities, business decision-making support, and smart service design.

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications.

OCEB 2 Certification Guide, Second Edition has been updated to cover the new version 2 of the BPMN standard and delivers expert insight into BPM from one of the developers of the OCEB Fundamental exam, offering full coverage of the fundamental exam material for both the business and technical tracks to further certification.

Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability.

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data.

Data mapping in a data warehouse is the process of creating a link between two distinct data models' (source and target) tables/attributes.

Cyber Security Awareness for Corporate Directors and Board Members is a concise overview of the cyber threat to you, your business, your livelihood, and what you need to do--especially as board members and directors of boards-- to lower the risk, reduce or eliminate liability, and protect reputation.

Cyber Security for CEOs and Managment is a concise overview of the security threats posed to organizations and networks by the ubiquity of USB Flash Drives used as storage devices.

The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable.

Application Performance Management (APM) in the Digital Enterprise enables IT professionals to be more successful in managing their company's applications.

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form.

Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain.

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud.

The mobile threat landscape is evolving bringing about new forms of data loss.

Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data.

Doing data science is difficult.

Big Data Analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise.

Implement real-world machine learning in a microservices architecture as well as design, build, and deploy intelligent microservices systems using examples and case studiesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesDesign, build, and run microservices systems that utilize the full potential of machine learningDiscover the latest models and techniques for combining microservices and machine learning to create scalable systemsImplement machine learning in microservices architecture using open source applications with pros and consBook DescriptionWith the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems.

Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasetsKey FeaturesMaster each concept through practical exercises and activitiesDiscover various statistical techniques to analyze your dataImplement everything you've learned on a real-world case study to uncover valuable insightsBook DescriptionEvery day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace.

Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization.

Writing Effective Business Rules moves beyond the fundamental dilemma of system design: defining business rules either in natural language, intelligible but often ambiguous, or program code (or rule engine instructions), unambiguous but unintelligible to stakeholders.

Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights.

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and KerasKey FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML).

A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark.

Build smarter systems by combining artificial intelligence and the Internet of Things-two of the most talked about topics todayKey FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook DescriptionThere are many applications that use data science and analytics to gain insights from terabytes of data.

Distributed Systems: Concurrency and Consistency explores the gray area of distributed systems and draws a map of weak consistency criteria, identifying several families and demonstrating how these may be implemented into a programming language.

Learn Azure's cloud capabilities with the help of this introductory guide to employing Azure for your cloud infrastructure needs.

Learn everything you need to know to build efficient SQL queries using this easy-to-follow beginner's guideKey FeaturesExplore all SQL statements in depth using a variety of examplesGet to grips with database querying, data aggregate, manipulation, and much moreUnderstand how to explore and process data of varying complexity to tell a storyBook DescriptionSQL is a powerful querying language that's used to store, manipulate, and retrieve data, and it is one of the most popular languages used by developers to query and analyze data efficiently.

Transform machine-generated data into valuable business insights using the powers of SplunkKey FeaturesExplore the all-new machine learning toolkit in Splunk 7.

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously.

This new textbook in signals and systems provides a pedagogically rich approach to what can commonly be a mathematically dry subject.