Information architecture

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organizationKey FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook DescriptionData science and machine learning can transform any organization and unlock new opportunities.

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners.

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobsKey FeaturesWork with large amounts of agile data using distributed datasets and in-memory cachingSource data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3Employ the easy-to-use PySpark API to deploy big data Analytics for productionBook DescriptionApache Spark is an open source parallel-processing framework that has been around for quite some time now.

Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlowKey FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook DescriptionWith information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning.

Explore the world of neural networks by building powerful deep learning models using the R ecosystemKey FeaturesGet to grips with the fundamentals of deep learning and neural networksUse R 3.

Learn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.

Get hands-on with deploying and managing your database services to provide scalable and high-speed data access on CockroachDBKey FeaturesGain insights into CockroachDB and build highly reliable cloud-native applicationsExplore the power of a scalable and highly available cloud-native SQL database to distribute data and workloads automaticallyBuild high-speed database services using CockroachDB and troubleshoot performance issuesBook DescriptionGetting Started with CockroachDB will introduce you to the inner workings of CockroachDB and help you to understand how it provides faster access to distributed data through a SQL interface.

Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricksKey FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook DescriptionGetting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results.

Explore Kinesis managed services such as Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video Streams with the help of practical use casesKey FeaturesGet well versed with the capabilities of Amazon KinesisExplore the monitoring, scaling, security, and deployment patterns of various Amazon Kinesis servicesLearn how other Amazon Web Services and third-party applications such as Splunk can be used as destinations for Kinesis dataBook DescriptionAmazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services.

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming languageKey FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time.

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data FactoryKey FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory's visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook DescriptionAzure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure.

Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operationsKey FeaturesBuild and scale modern data solutions using the all-in-one Snowflake platformPerform advanced cloud analytics for implementing big data and data science solutionsMake quicker and better-informed business decisions by uncovering key insights from your dataBook DescriptionSnowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud.

Get up to speed with the new features added to Microsoft SQL Server 2019 Analysis Services and create models to support your businessKey FeaturesExplore tips and tricks to design, develop, and optimize end-to-end data analytics solutions using Microsoft's technologiesLearn tabular modeling and multi-dimensional cube design development using real-world examplesImplement Analysis Services to help you make productive business decisionsBook DescriptionSQL Server Analysis Services (SSAS) continues to be a leading enterprise-scale toolset, enabling customers to deliver data and analytics across large datasets with great performance.

Explore the latest Azure ETL techniques both on-premises and in the cloud using Azure services such as SQL Server Integration Services (SSIS), Azure Data Factory, and Azure DatabricksKey FeaturesUnderstand the key components of an ETL solution using Azure Integration ServicesDiscover the common and not-so-common challenges faced while creating modern and scalable ETL solutionsProgram and extend your packages to develop efficient data integration and data transformation solutionsBook DescriptionETL is one of the most common and tedious procedures for moving and processing data from one database to another.

Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing frameworkKey FeaturesMaster the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystemsKey FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook DescriptionData scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models.

Tackle the complex challenges faced while building end-to-end deep learning models using modern R librariesKey FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook DescriptionDeep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning.

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and moreKey FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.

Create and unleash the power of neural networks by implementing C# and .

Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power.

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.

Design, administer, and deploy high-volume and fault-tolerant database applications using MongoDB 4.

A comprehensive guide to developing neural network-based solutions using TensorFlow 2.

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.

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.

Learn how to architect, implement, and administer a complex Splunk Enterprise environment and extract valuable insights from business data.

An expert guide to implementing fast, secure, and scalable decentralized applications that work with thousands of users in real timeKey FeaturesImplement advanced features of the Ethereum network to build powerful decentralized applicationsBuild smart contracts on different domains using the programming techniques of Solidity and VyperExplore the architecture of Ethereum network to understand advanced use cases of blockchain development Book DescriptionEthereum is one of the commonly used platforms for building blockchain applications.

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.

Learn to work with XRP and build applications on Ripple's blockchainKey FeaturesLearn to use Ripple's decentralized system for transfering digital assets globallyA simpilfied and shortened learning curve to understand the Ripple innovation and BlockchainTakes a hands-on approach to work with XRP - Ripple's native currencyBook DescriptionThis book starts by giving you an understanding of the basics of blockchain and the Ripple protocol.

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandasKey FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook DescriptionExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset.

Enhance your data analysis and predictive modeling skills using popular Python toolsKey FeaturesCover all fundamental libraries for operation and manipulation of Python for data analysisImplement real-world datasets to perform predictive analytics with PythonAccess modern data analysis techniques and detailed code with scikit-learn and SciPyBook DescriptionPython is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.

Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasksKey FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key FeaturesA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZA modern, practical and computational approach to Bayesian statistical modelingA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.

Optimize your marketing strategies through analytics and machine learningKey FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook DescriptionRegardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry.

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithmsKey FeaturesExplore statistics and complex mathematics for data-intensive applicationsDiscover new developments in EM algorithm, PCA, and bayesian regressionStudy patterns and make predictions across various datasetsBook DescriptionMachine learning has gained tremendous popularity for its powerful and fast predictions with large datasets.

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.

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

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.

A hands-on guide for professionals to perform various data science tasks in RKey FeaturesExplore the popular R packages for data scienceUse R for efficient data mining, text analytics and feature engineeringBecome a thorough data science professional with the help of hands-on examples and use-cases in RBook DescriptionR is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world.

Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook DescriptionPredictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions.

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework.

Integrate open source data analytics and build business intelligence on SQL databases with Apache Superset.

Perform Interactive Data Analysis with Smarter Visualizations and Support your Enterprise-wide Analytical Needs Key Features Get a practical demonstration of discovering data for sales, human resources, and more using Qlik Sense Create dynamic dashboards for business intelligence and predictive analytics Create and collaborate comprehensive analytical solutions using Rattle and Qlik Sense Book Description Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business.

Create dynamic dashboards to perform interactive analytics for business intelligence operationsKey FeaturesExplore newly added features in Qlik SenseDiscover best practices to work with data using Qlik SenseLearn to implement advanced functions for better data insightBook DescriptionQlik Sense allows you to explore simple and complex data to reveal hidden insight and data relationships that help you make quality decisions for overall productivity.

Find, explore, and extract big data to transform into actionable insightsKey FeaturesPerform end-to-end data analysis-from exploration to visualizationReal-world examples, tasks, and interview queries to be a proficient data scientistUnderstand how SQL is used for big data processing using HiveQL and SparkSQLBook DescriptionSQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features.

Leverage the power of Hyperledger Fabric to develop Blockchain-based distributed ledgers with ease Key FeaturesWrite your own chaincode/smart contracts using Golang on hyperledger networkBuild and deploy decentralized applications (DApps)Dive into real world blockchain challenges such as integration and scalabilityBook DescriptionBBlockchain and Hyperledger technologiesare hot topics today.

Understand the Blockchain revolution and get to grips with Ethereum, Hyperledger Fabric, and Corda.

Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Key Features A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Book Description Social Media and the Internet of Things have resulted in an avalanche of data.

Process large volumes of data in real-time while building high performance and robust data stream processing pipeline using the latest Apache Kafka 2.

A comprehensive guide to mastering the most advanced Hadoop 3 conceptsKey FeaturesGet to grips with the newly introduced features and capabilities of Hadoop 3Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystemSharpen your Hadoop skills with real-world case studies and codeBook DescriptionApache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data.