Data capture and analysis

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.

Your hands-on reference guide to developing, training, and optimizing your machine learning modelsKey FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook DescriptionMachine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques.

Explore powerful R packages to create predictive models using ensemble methodsKey FeaturesImplement machine learning algorithms to build ensemble-efficient modelsExplore powerful R packages to create predictive models using ensemble methodsLearn to build ensemble models on large datasets using a practical approachBook DescriptionEnsemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model.

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.

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.

Build efficient forecasting models using traditional time series models and machine learning algorithms.

Implement blockchain principles in your choice of domain using Ethereum Key FeaturesBuild permissioned enterprise-grade blockchain applications from scratchImplement Blockchain-as-a-Service to enterprises in terms of deployment and securityAchieve privacy in blockchains using proxy re-encryption algorithmsBook DescriptionThe increasing growth in blockchain use is enormous, and it is changing the way business is done.

This fully revised and updated edition of the bestselling Chief Data Officer's Playbook offers new insights into the role of the CDO and the data environment.

This fully revised and updated edition of the bestselling Chief Data Officer's Playbook offers new insights into the role of the CDO and the data environment.

Harness the power of today's financial technologies -- whether you make 2 trades per year or 2000Tools for the Direct Access Trader shows you how to outfit yourself for success and acquire the speed, functionality, and information access that puts you on the same level as institutional traders - but with maneuverability and flexibility that larger, bulkier traders can't touch.

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.

Doing data science is difficult.

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.

Former Wall Street Journal technology reporter Yukari Iwatani Kane delves deep inside Apple in the two years since Steve Jobss death, revealing the tensions and challenges CEO Tim Cook and his team face as they try to sustain Jobss vision and keep the company moving forward.

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

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies.

In this explosive memoir, a political consultant and technology whistleblower reveals the disturbing truth about the multi-billion-dollar data industry, revealing to the public how companies are getting richer using our personal information and exposing how Cambridge Analytica exploited weaknesses in privacy laws to help elect Donald Trump.

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.

Data is powerful.

In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms.

This book provides a guide to the analysis of complex systems through the lens of data science.

In one short decade, the internet has become a critical part of our everyday lives.

Episodes in the history of data, from early modern math problems to today''s inescapable “dataveillance,” that demonstrate the dependence of data on culture.

Making interactive web maps has never been easier.

Is your business looking out?

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting.

Environmental science (ecology, conservation, and resource management) is an increasingly quantitative field.

The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced.

Experiments, surveys, measurements, and observations all generate data.

The social sciences are becoming datafied.

The social sciences are becoming datafied.

Demography is everywhere in our lives: from birth to death.

Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students.

Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your dataKey FeaturesIntegrate machine learning with distributed search and analyticsPreprocess and analyze large volumes of search data effortlesslyOperationalize machine learning in a scalable, production-worthy wayBook DescriptionElastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively.

Speed up the design and implementation of deep learning solutions using Apache SparkKey FeaturesExplore the world of distributed deep learning with Apache SparkTrain neural networks with deep learning libraries such as BigDL and TensorFlowDevelop Spark deep learning applications to intelligently handle large and complex datasetsBook DescriptionDeep learning is a subset of machine learning where datasets with several layers of complexity can be processed.

Analytics is the application of mathematical and statistical concepts to large data sets so as to distil insights that offer the owner some options for action and competitive advantage or value.

Analytics is the application of mathematical and statistical concepts to large data sets so as to distil insights that offer the owner some options for action and competitive advantage or value.

The second edition of this internationally acclaimed title is the ideal handbook for those involved in conducting epidemiological research.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting.

Become an expert at using Python for advanced statistical analysis of data using real-world examplesAbout This BookClean, format, and explore data using graphical and numerical summariesLeverage the IPython environment to efficiently analyze data with PythonPacked with easy-to-follow examples to develop advanced computational skills for the analysis of complex dataWho This Book Is ForIf you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you.

Learn how to bring your data to life with this hands-on guide to visual analytics with TableauKey FeaturesMaster the fundamentals of Tableau Desktop and Tableau PrepLearn how to explore, analyze, and present data to provide business insightsBuild your experience and confidence with hands-on exercises and activitiesBook DescriptionLearning Tableau has never been easier, thanks to this practical introduction to storytelling with data.

Learn how to make the right decisions for your business with the help of Python recipes and the expertise of data leadersKey FeaturesLearn and practice various clustering techniques to gather market insights Explore real-life use cases from the business world to contextualize your learningWork your way through practical recipes that will reinforce what you have learnedBook DescriptionOne of the most valuable contributions of data science is toward helping businesses make the right decisions.

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.