Data capture and analysis

A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial IntelligenceKey FeaturesLearn to build and run a big data application with sample codeExplore examples to implement activities that a big data architect performsUse Machine Learning and AI for structured and unstructured dataBook DescriptionThe big data architects are the "e;masters"e; of data, and hold high value in today's market.

Offers researchers and teachers an introduction to clustering, with R code and real data to facilitate interaction with the concepts.

Microsoft Excel is a powerful tool that can transform the way you use data.

Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools.

A solution-based guide to put your deep learning models into production with the power of Apache SparkKey FeaturesDiscover practical recipes for distributed deep learning with Apache SparkLearn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache SparkBook DescriptionWith deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries.

Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data.

A pathbreaking history of art that uses digital research and economic tools to reveal enduring inequities in the formation of the art historical canonPainting by Numbers presents a groundbreaking blend of art historical and social scientific methods to chart, for the first time, the sheer scale of nineteenth-century artistic production.

This new book from bestselling author Jordan Morrow empowers data professionals to work and operate more effectively in an organizational setting, equipping them with key business knowledge and skills.

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

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.

Machine learning is an intimidating subject until you know the fundamentals.

Solving complex geospatial problems and delivering solutions has never been easier.

Graph data closes the gap between the way humans and computers view the world.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.

This book is the culmination of three years of research effort on a multidisciplinary project in which physicists, mathematicians, computer scientists and social scientists worked together to arrive at a unifying picture of complex networks.

An insider's guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data.

Make sense of your data and predict the unpredictableAbout This BookA unique book that centers around develop six key practical skills needed to develop and implement predictive analyticsApply the principles and techniques of predictive analytics to effectively interpret big dataSolve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domainsWho This Book Is ForThis book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues.

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.

Implement decentralized blockchain applications to build scalable DappsKey FeaturesUnderstand the blockchain ecosystem and its terminologiesImplement smart contracts, wallets, and consensus protocolsDesign and develop decentralized applications using Bitcoin, Ethereum, and HyperledgerBook DescriptionThe Blockchain is a revolution promising a new world without middlemen.

Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries.

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.

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

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.

Learn to view, edit and analyse geospatial data using QGIS and Python 3Key FeaturesLeverage the power of QGIS to add professionalism to your mapsExplore and work with the newly released features like Python 3, GeoPackage, 3D views, Print layouts in QGIS 3.

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

In today's world, organizations face a multitude of problems that require an unprecedented need for tools to share information and work better together.

Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming languageKey FeaturesUnderstand the basics of R and QGIS to work with GIS and remote sensing dataLearn to manage, manipulate, and analyze spatial data using R and QGISApply machine learning algorithms to geospatial data using R and QGISBook DescriptionManaging spatial data has always been challenging and it's getting more complex as the size of data increases.

If you know how to program, you're ready to tackle Bayesian statistics.

The main goal of the new field of data mining is the analysis of large and complex datasets.

How can you get your data from frontend servers to Hadoop in near real time?

Why a book about logs?

Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMakerKey FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook DescriptionMachine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications.

Understand and build beautiful and advanced plots with Matplotlib and PythonKey FeaturesPractical guide with hands-on examples to design interactive plotsAdvanced techniques to constructing complex plotsExplore 3D plotting and visualization using Jupyter NotebookBook DescriptionIn this book, you'll get hands-on with customizing your data plots with the help of Matplotlib.

Freakonomics lived on the New York Times bestseller list for an astonishing two years.

Make sense of your data and predict the unpredictableAbout This BookA unique book that centers around develop six key practical skills needed to develop and implement predictive analyticsApply the principles and techniques of predictive analytics to effectively interpret big dataSolve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domainsWho This Book Is ForThis book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues.

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.

Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualizationKey FeaturesAnalyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensimImplement common and not-so-common linguistic processing tasks using Python librariesOvercome the common challenges faced while implementing NLP pipelinesBook DescriptionPython is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data.

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework.

Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased.

In the digital age, data has become the cornerstone of effective marketing strategies.

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.

Our newly digital world is generating an almost unimaginable amount of data about all of us.

The book addresses the impact of ambient intelligence, particularly its user-centric context-awareness requirement on data management strategies and solutions.

Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with JupyterKey FeaturesLearn how to use Jupyter 5.

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy.

Bring data to life with this essential guide to storytelling with maps.