Data capture and analysis

Take a new approach to digital marketing based on transparency, relevance, and delivering real value to your audience.

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

The complexity of large systems in public administration progresses in terms of both quality and quantity year after year.

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

Value-Driven Data explains how data and business leaders can co-create and deploy data-driven solutions for their organizations.

Over insightful 90 recipes to get lightning-fast analytics with Apache SparkAbout This BookUse Apache Spark for data processing with these hands-on recipesImplement end-to-end, large-scale data analysis better than ever beforeWork with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your dataWho This Book Is ForThis book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework.

Analysis within the Systems Development Life-Cycle: Book 2, Data Analysis-The Methods describes the methods for carrying out data analysis within the systems development life-cycle and demonstrates how the results of fact gathering can be used to produce and verify the analysis deliverables.

Your life online is their product.

This book focuses on the modern nuclear models and computer codes used in nuclear model calculations of nuclear data required for nuclear technology and nuclear safety applications.

Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programmingKey FeaturesWork your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisionsUnderstand how various data science algorithms functionBuild a solid foundation in statistics for data science and machine learning using Python-based examplesBook DescriptionStatistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines.

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.

Don t be afraid of the GDPR wolf!

Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations.

Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems.

Discover the new features and widely used packages in Julia to solve complex computational problems in your statistical applications.

Learn how to quickly generate business intelligence, insights and create interactive dashboards for digital storytelling through various data sources with RedashKey FeaturesLearn the best use of visualizations to build powerful interactive dashboardsCreate and share visualizations and data in your organizationWork with different complexities of data from different data sourcesBook DescriptionData exploration and visualization is vital to Business Intelligence, the backbone of almost every enterprise or organization.

Leverage Elastic Stack's machine learning features to gain valuable insight from your dataKey FeaturesCombine machine learning with the analytic capabilities of Elastic StackAnalyze large volumes of search data and gain actionable insight from themUse external analytical tools with your Elastic Stack to improve its performanceBook DescriptionMachine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting.

Get to grips with pandas-a versatile and high-performance Python library for data manipulation, analysis, and discoveryKey FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook DescriptionData analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value.

Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics.

A complete guide to understanding, developing, and testing popular security-token smart contractsKey FeaturesUnderstand key Blockchain and Ethereum platforms conceptsStep-by-step guide to developing STO smart contracts on EthereumMonetize digital tokens under various U.

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task.

If youre like most R users, you have deep knowledge and love for statistics.

A diverse selection of data science topics explored through a mathematical lens.

Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.

Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured dataKey FeaturesGet to grips with word embeddings, semantics, labeling, and high-level word representations using practical examplesLearn modern approaches to NLP and explore state-of-the-art NLP models using PyTorchImprove your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNsBook DescriptionIn the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill.

Data is constantly increasing and data analysts are in higher demand than ever.

Jump-start your career as a data scientist learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most.

Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models.

Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data.

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.

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

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.

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

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.

What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization?

Build efficient, high-performance & scalable systems to process large volumes of data with Apache IgniteKey FeaturesUnderstand Apache Ignite's in-memory technologyCreate High-Performance app components with IgniteBuild a real-time data streaming and complex event processing systemBook DescriptionApache Ignite is a distributed in-memory platform designed to scale and process large volume of data.

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction.

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.

There is an easier way to build Hadoop applications.

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.

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

Exploit the power of data in your business by building advanced predictive modeling applications with PythonAbout This BookMaster open source Python tools to build sophisticated predictive modelsLearn to identify the right machine learning algorithm for your problem with this forward-thinking guideGrasp the major methods of predictive modeling and move beyond the basics to a deeper level of understandingWho This Book Is ForThis book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python.

Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few.

Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models.

Providing clear, practical explanations of research methods in business studies, this guide is indispensable for students writing reports.

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.