Hands-On BootingLearn the Boot Process of Linux, Windows, and Unix
Master the booting procedure of various operating systems with in-depth analysis of bootloaders and firmware. The primary focus is on the Linux booting procedure along with other popular operating systems such as Windows and Unix. Hands-on Booting begins by explaining what a bootloader is, starting with the Linux bootloader followed by bootloaders for Windows and Unix systems. Next, you'll address the BIOS and UEFI firmware by installing multiple operating systems on one machine and booting them through the Linux bootloader. Further, you'll see the kernel's role in the booting procedure of the operating system and the dependency between kernel, initramfs, and dracut. You'll also cover systemd, examining its structure and how it mounts the user root filesystem. In the final section, the book explains troubleshooting methodologies such as debugging shells followed by live images and rescue mode. On completing this book, you will understand the booting processof major operating systems such as Linux, Windows, and Unix. You will also know how to fix the Linux booting issues through various boot modes. What You Will LearnExamine the BIOS and UEFI firmware Understanding the Linux boot loader (GRUB)Work with initramfs, dracut, and systemdFix can't-boot issues on Linux Who This Book Is For Linux users, administrators, and developers.
A Practical Guide to Database Design
This book contains a major update to the previous edition. It covers how to implement and manage the Database Management System (DBMS) itself, how to write scripts to extract and load data from source files, and how to develop user interfaces to view and update data within the database.
Introduction to Computing Applications in Forestry and Natural Resource Management
Due to the complexity of operational forestry problems, computing applications are becoming pervasive in all aspects of forest and natural resource management.
Bioinformatics Database Systems
This book explains the world of database systems to bioinformatics and cheminformatics database users. It begins with a survey of popular databases, including primary genetic and protein sequence, phylogenetic, structure and pathway, and microarray databases. The text discusses information management and modeling issues in these databases, addre
Process Modeling and Management for Healthcare
Process modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Featuring contributions from leading experts, this book provides an in-depth analysis of what process modeling and management techniques can do in healthcare, the major challenges faced, and challenges to come.
High Performance Computing for Big Data
This book presents state-of-the-art research, methodologies, and applications of high performance computing for big data applications. It covers fundamental issues in Big Data research, including emerging architectures for data-intensive applications, novel analytical strategies to boost data processing, and cutting-edge applications.
Social Networks with Rich Edge Semantics
This book introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time.
Data Analytics and Management in Data Intensive Domains
This book constitutes the post-conference proceedings of the 21st International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2019, held in Kazan, Russia, in October 2019.The 11 revised full papers presented together with four invited papers were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: advanced data analysis methods; data infrastructures and integrated information systems; models, ontologies and applications; data analysis in astronomy; information extraction from text; distributed computing; data science for education.
Data Analytics
This book provides a broad picture on the concepts, techniques, applications, and open research directions in Data Analytics. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.
Distributed Tracing in PracticeInstrumenting, Analyzing, and Debugging Microservices
Since most applications today are distributed in some fashion, monitoring their health and performance requires a new approach. Enter distributed tracing, a method of profiling and monitoring distributed applications--particularly those that use microservice architectures. There's just one problem: distributed tracing can be hard. But it doesn't have to be. With this guide, you'll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at LightStep and other organizations walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful operational insights. If you want to implement distributed tracing, this book tells you what you need to know. You'll learn: The pieces of a distributed tracing deployment: instrumentation, data collection, and analysis Best practices for instrumentation: methods for generating trace data from your services How to deal with (or avoid) overhead using sampling and other techniques How to use distributed tracing to improve baseline performance and to mitigate regressions quickly Where distributed tracing is headed in the future
Big Data and Computational Intelligence in Networking
This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization.
Statistical and Machine-Learning Data Mining:
The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.
Data-Driven Process Discovery and Analysis
This book constitutes revised selected papers from the 8th and 9th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018, held in Seville, Spain, on December 13-14, 2018, and SIMPDA 2019, held in Bled, Slovenia, on September 8, 2019. From 16 submissions received for SIMPDA 2018 and 9 submissions received for SIMPDA 2019, 3 papers each were carefully reviewed and selected for presentation in this volume. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
Graph-Based Social Media Analysis
This book provides a comprehensive introduction to the use of graph analysis in the study of social media and digital media. It covers the following topics: graphs in social media, graph theory, algebraic analysis of graphs, graph clustering, diffusion in social media, label propagation in graphs, graphs in pattern recognition and machine learni
Handbook of Parallel Computing and Statistics
This unique reference weaves together the principles and theoretical models of parallel computing with the design, analysis, and application of algorithms for solving statistical problems. After a brief introduction to parallel computing, the book explores the architecture, programming, and computational aspects of parallel processing. Focus then t
Multi-Disciplinary Trends in Artificial Intelligence
This book constitutes the refereed conference proceedings of the 10th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2016, held in Chiang Mai, Thailand, in December 2016. The 22 revised full papers presented together with 5 short papers and 2 abstracts of invited talks were carefully reviewed and selected from 50 submissions. The workshop solicits papers from all areas of AI including cognitive science; computational intelligence; computational philosophy; game theory; machine learning; multi-agent systems; natural language; representation and reasoning; speech; vision and the web; as well as applications of AI in big data; bioinformatics; biometrics; decision support; e-commerce; image processing; analysis and retrieval; industrial applications; knowledge management; privacy; recommender systems; security; software engineering; spam filtering; surveillance; telecommunications; and web services.
Ein Physiologiemodell F羹r Tactical Combat Casualty Care Training in Mobilen Serious Games
Julia Hofmann konzipiert in diesem Open Access Buch ein Physiologiemodell f羹r die pr瓣zise Simulation bestimmter Verletzungsfolgen und deren Behandlung in einer Computerspielumgebung. Ihre Ergebnisse leisten einen wichtigen Beitrag, um die Ausbildung von Einsatzkr瓣ften in der taktischen Verwundetenversorgung mit neuen Medien zu verbessern. Prim瓣re Zielgruppe sind dabei die sogenannten Erst-Helfer-Bravo der Bundeswehr. Die medizinische Grundlage der Arbeit bildet der internationale Erstversorgungsalgorithmus Tactical Combat Casualty Care, der die ?berlebenschancen lebensbedrohlich verwundeter Personen erwiesenerma?en deutlich erh繹ht. Das entworfene Physiologiemodell wurde mithilfe praktizierender Notfallmediziner und Ausbilder der Bundeswehr validiert.
Modern Data Mining Algorithms in C++ and Cuda C
Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You'll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selectionLinking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.
End-to-End Data Science with SAS
Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step. Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user's guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model's effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.
Natural Language Processing and Information Systems
This book constitutes the refereed proceedings of the 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, held in Saarbr羹cken, Germany, in June 2020.* The 15 full papers and 10 short papers were carefully reviewed and selected from 68 submissions. The papers are organized in the following topical sections: semantic analysis; question answering and answer generation; classification; sentiment analysis; personality, affect and emotion; retrieval, conversational agents and multimodal analysis. *The conference was held virtually due to the COVID-19 pandemic.
SQL Server Big Data Clusters
Use this guide to one of SQL Server 2019's most impactful features--Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL--taking advantage of skills you have honed for years--and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environmentsAnalyze large volumes of data directly from SQL Server and/or Apache SparkManage data stored in HDFS from SQL Server as if it wererelational dataImplement advanced analytics solutions through machine learning and AIExpose different data sources as a single logical source using data virtualizationWho This Book Is ForData engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments
Learn Grafana 7.0
A comprehensive introduction to help you get up and running with creating interactive dashboards to visualize and monitor time-series data in no timeKey Features Install, set up, and configure Grafana for real-time data analysis and visualization Visualize and monitor data using data sources such as InfluxDB, Prometheus, and Elasticsearch Explore Grafana's multi-cloud support with Microsoft Azure, Amazon CloudWatch, and Google Stackdriver Book Description Grafana is an open-source analytical platform used to analyze and monitoring time-series data. This beginner's guide will help you get to grips with Grafana's new features for querying, visualizing, and exploring metrics and logs no matter where they are stored. The book begins by showing you how to install and set up the Grafana server. You'll explore the working mechanism of various components of the Grafana interface along with its security features, and learn how to visualize and monitor data using, InfluxDB, Prometheus, Logstash, and Elasticsearch. This Grafana book covers the advanced features of the Graph panel and shows you how Stat, Table, Bar Gauge, and Text are used. You'll build dynamic dashboards to perform end-to-end analytics and label and organize dashboards into folders to make them easier to find. As you progress, the book delves into the administrative aspects of Grafana by creating alerts, setting permissions for teams, and implementing user authentication. Along with exploring Grafana's multi-cloud monitoring support, you'll also learn about Grafana Loki, which is a backend logger for users running Prometheus and Kubernetes. By the end of this book, you'll have gained all the knowledge you need to start building interactive dashboards. What you will learn Find out how to visualize data using Grafana Understand how to work with the major components of the Graph panel Explore mixed data sources, query inspector, and time interval settings Discover advanced dashboard features such as annotations, templating with variables, dashboard linking, and dashboard sharing techniques Connect user authentication to Google, GitHub, and a variety of external services Find out how Grafana can provide monitoring support for cloud service infrastructures Who this book is for This book is for business intelligence developers, business analysts, data analysts, and anyone interested in performing time-series data analysis and monitoring using Grafana. Those looking to create and share interactive dashboards or looking to get up to speed with the latest features of Grafana will also find this book useful. Although no prior knowledge of Grafana is required, basic knowledge of data visualization and some experience in Python programming will help you understand the concepts covered in the book.
Building a Data Integration Team
Find the right people with the right skills. This book clarifies best practices for creating high-functioning data integration teams, enabling you to understand the skills and requirements, documents, and solutions for planning, designing, and monitoring both one-time migration and daily integration systems.The growth of data is exploding. With multiple sources of information constantly arriving across enterprise systems, combining these systems into a single, cohesive, and documentable unit has become more important than ever. But the approach toward integration is much different than in other software disciplines, requiring the ability to code, collaborate, and disentangle complex business rules into a scalable model. Data migrations and integrations can be complicated. In many cases, project teams save the actual migration for the last weekend of the project, and any issues can lead to missed deadlines or, at worst, corrupted data that needs to be reconciled post-deployment. This book details how to plan strategically to avoid these last-minute risks as well as how to build the right solutions for future integration projects. What You Will Learn Understand the "language" of integrations and how they relate in terms of priority and ownershipCreate valuable documents that lead your team from discovery to deploymentResearch the most important integration tools in the market todayMonitor your error logs and see how the output increases the cycle of continuous improvementMarket across the enterprise to provide valuable integration solutions Who This Book Is For The executive and integration team leaders who are building the corresponding practice. It is also for integration architects, developers, and business analysts who need additional familiarity with ETL tools, integration processes, and associated project deliverables.
Computational Advances in Bio and Medical Sciences
This book constitutes revised selected papers from the 9th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2019, held in Miami, Florida, USA in November 2019.The 15 papers presented in this volume were carefully reviewed and selected from 30 submissions. They deal with topics such as computational biology; biomedical image analysis; biological networks; cancer genomics; gene enrichment analysis; functional genomics; interaction networks; protein structure prediction; dynamic programming; and microbiome analysis.
Azure Strategy and Implementation Guide - Third EditionUp-to-date information for organiza
Education and Technology in SciencesFirst International Congress, Cisetc 2019, Arequipa, P
Hands-On Python Natural Language Processing
Get well-versed with traditional as well as modern natural language processing concepts and techniquesKey Features Perform various NLP tasks to build linguistic applications using Python libraries Understand, analyze, and generate text to provide accurate results Interpret human language using various NLP concepts, methodologies, and tools Book Description Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you'll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you'll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP. What you will learn Understand how NLP powers modern applications Explore key NLP techniques to build your natural language vocabulary Transform text data into mathematical data structures and learn how to improve text mining models Discover how various neural network architectures work with natural language data Get the hang of building sophisticated text processing models using machine learning and deep learning Check out state-of-the-art architectures that have revolutionized research in the NLP domain Who this book is for This NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.
Data and Information in Online Environments
This book constitutes the refereed post-conference proceedings of the First International Conference on Data and Information in Online Environments, DIONE 2020, which took place in Florian籀polis, Brazil, in March 2020. DIONE 2020 handles the growing interaction between the information sciences, communication sciences and computer sciences. The 18 revised full papers were carefully reviewed and selected from 37 submissions and focus on the production, dissemination and evaluation of contents in online environments. The goal is to improve cooperation between data science, natural language processing, data engineering, big data, research evaluation, network science, sociology of science and communication communities.
Next-Generation Machine Learning with Spark
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications.The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will LearnBe introduced to machine learning, Spark, and Spark MLlib 2.4.xAchieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM librariesDetect anomalies with the Isolation Forest algorithm for SparkUse the Spark NLP and Stanford CoreNLP libraries that support multiple languagesOptimize your ML workload with the Alluxio in-memory data accelerator for SparkUse GraphX and GraphFrames for Graph AnalysisPerform image recognition using convolutional neural networksUtilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is For Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.
Building Analytics Teams
Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AIKey Features Learn to create an operationally effective advanced analytics team in a corporate environment Select and undertake projects that have a high probability of success and deliver the improved top and bottom-line results Understand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your team Book Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learn Avoid organizational and technological pitfalls of moving from a defined project to a production environment Enable team members to focus on higher-value work and tasks Build Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organization Outsource certain projects to competent and capable third parties Support the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analytics Analyze the operational area, the processes, the data, and the organizational resistance Who this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful.
Practical Data Analysis using Jupyter NotebookLearn how to speak the language of data by e
End-to-End Data Science with SAS
Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step. Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user's guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model's effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.
Model Risk Management with SAS
Cut through the complexity of model risk management with a guide to solutions from SAS! There is an increasing demand for more model governance and model risk awareness. At the same time, high-performing models are expected to be deployed faster than ever. SAS Model Risk Management is a user-friendly, web-based application that facilitates the capture and life cycle management of statistical model-related information. It enables all stakeholders in the model life cycle - developers, validators, internal audit, and management - to get overview reports as well as detailed information in one central place. Model Risk Management with SAS introduces you to the features and capabilities of this software, including the entry, collection, transfer, storage, tracking, and reporting of models that are drawn from multiple lines of business across an organization. This book teaches key concepts, terminology, and base functionality that are integral to SAS Model Risk Management through hands-on examples and demonstrations. With this guide to SAS Model Risk Management, your organization can be confident it is making fact-based decisions and mitigating model risk.
Data Science Tools
In the world of data science there are myriad tools available to analyze data. This book describes some of the popular software application tools along with the processes for downloading and using them in the most optimum fashion. The content includes data analysis using Microsoft Excel, KNIME, R, and OpenOffice (Spreadsheet). Each of these tools will be used to apply statistical concepts including confidence intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysis using real data from Federal Government sources. Features: Analyzes data using popular applications such as Excel, R, KNIME, and OpenOfficeCovers statistical concepts including confidence intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysisCapstone exercises analyze data using the different software packages
Practical Statistics for Data Scientists
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data
Managing Data Quality
Data is an increasingly important business asset and enabler for organisational activities. Data quality is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability. This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.
Group Decision and Negotiation: A Multidisciplinary Perspective
This book constitutes the refereed proceedings of the 20th International Conference on Group Decision and Negotiation, GDN 2020, which was planned to be held in Toronto, ON, Canada, during June 7-11, 2020. The conference was cancelled due to the Coronavirus pandemic. Nevertheless, it was decided to publish the proceedings, because the review process had already been completed at the time the cancellation was decided. The field of Group Decision and Negotiation focuses on decision processes with at least two participants and a common goal but conflicting individual goals. Research areas of Group Decision and Negotiation include electronic negotiations, experiments, the role of emotions in group decision and negotiations, preference elicitation and decision support for group decisions and negotiations, and conflict resolution principles. The 14 full papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized intopical sections named: Conflict Resolution, Preference Modeling for Group Decision and Negotiation, Intelligent Group Decision Making and Consensus Process, Collaborative Decision Making Processes.
Hands-On Kubernetes on Azure - Second Edition
Kick-start your DevOps career by learning how to effectively deploy Kubernetes on Azure in an easy, comprehensive, and fun way with hands-on coding tasksKey Features Understand the fundamentals of Docker and Kubernetes Learn to implement microservices architecture using the Kubernetes platform Discover how you can scale your application workloads in Azure Kubernetes Service (AKS) Book Description From managing versioning efficiently to improving security and portability, technologies such as Kubernetes and Docker have greatly helped cloud deployments and application development. Starting with an introduction to Docker, Kubernetes, and Azure Kubernetes Service (AKS), this book will guide you through deploying an AKS cluster in different ways. You'll then explore the Azure portal by deploying a sample guestbook application on AKS and installing complex Kubernetes apps using Helm. With the help of real-world examples, you'll also get to grips with scaling your application and cluster. As you advance, you'll understand how to overcome common challenges in AKS and secure your application with HTTPS and Azure AD (Active Directory). Finally, you'll explore serverless functions such as HTTP triggered Azure functions and queue triggered functions. By the end of this Kubernetes book, you'll be well-versed with the fundamentals of Azure Kubernetes Service and be able to deploy containerized workloads on Microsoft Azure with minimal management overhead. What you will learn Plan, configure, and run containerized applications in production Use Docker to build apps in containers and deploy them on Kubernetes Improve the configuration and deployment of apps on the Azure Cloud Store your container images securely with Azure Container Registry Install complex Kubernetes applications using Helm Integrate Kubernetes with multiple Azure PaaS services, such as databases, Event Hubs and Functions. Who this book is for This book is for aspiring DevOps professionals, system administrators, developers, and site reliability engineers looking to understand test and deployment processes and improve their efficiency. If you're new to working with containers and orchestration, you'll find this book useful.
Data, Engineering and Applications
This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.
Cisco HyperFlex im Einsatz
Hyperkonvergente Infrastrukturen (HCI) kombinieren Speichersysteme und Rechenleistung in regul瓣ren Servergeh瓣usen. Damit geh繹ren komplexe Speicherlandschaften und chaotische Serverschr瓣nke der Vergangenheit an. Der Trick dabei: Die Hardware wird virtualisiert und in Software abgebildet. Diesen Software-defined Storage platziert Cisco auf seinen UCS-Servern und tauft die Idee "HyperFlex". Cisco HyperFlex ist eine Plattform f羹r Rechenleistung, Speicher und Netzwerk. Obendrauf laufen virtuelle Maschinen auf VMware ESXi oder Microsoft Hyper-V. Durch die variable Anzahl von Servern und Festplatten skaliert sie vom gro?en Rechenzentrum bis zum kleinen Au?enstandort. Das Buch f羹hrt den Leser durch die Grundlagen von HCI und HyperFlex, gefolgt von der Planung und Installation auf "All Flash"-Servern. Die weiteren Kapitel testen HyperFlex auf Systemausf瓣lle und migrieren virtuelle Maschinen in die neue Umgebung. Dieses Buch ist der ideale Begleiter zum schnellen Verstehen von HyperFlex. Es richtet sich an Systemintegratoren, Admins und Entscheider, die HyperFlex jenseits der Marketingbrosch羹ren besichtigen wollen.
Data Science Uncovering the Reality
Data Science has become a popular field of work today. However a good resource to understand applied Data Science is still missing. In Data Science Uncovering the Reality, a group of IITians unravel how Data Science is done in the industry. They have interviewed Data Science and technology leaders at top companies in India and presented their learnings here.This book will give you honest answers to questions such as: How to build a career in Data Science?How A.I. is used in the world's most successful companies.How Data Science leaders actually work and the challenges they face
Analytics Best Practices
Deliver enterprise data analytics success by following Prashanth's prescriptive and practical techniques.Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale.So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are: Practicality. This book offers prescriptive, superior, and practical guidance.Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization.Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies.Dr. Southekal proves why he is one of the leading thinkers on data and analytics today. 'Analytics Best Practices' is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data.Douglas B. LaneyPrincipal, Data & Analytics Strategy, Caserta, and author of "Infonomics"
Blockchain
Do you want to find out what Blockchain is, how it works, and what it can do for you?This book could be the answer you are looking for...This book is the ultimate beginner's guide to understanding blockchain technology, cryptocurrencies, bitcoin and the future of money.In this guide, we shall be discussing everything there is to know about cryptocurrencies, their impact on the future of money and trade, and most importantly, how you can prepare yourself for the disruptive technology that is the blockchain.Here is just some of the information covered in this book: The History of Money What is the Blockchain Technology? History of the Blockchain A Chronological Development of Blockchain Related Technologies Benefits of the Blockchain Technology Disadvantages of Using Blockchain Technology Understanding Ethereum How Ethereum Developed How Ethereum Works How Smart Contracts Work The Application Possibilities for Smart Contracts How Mining Works How to Get Started with Blockchain and Implementing Blockchain into Business Operations Blockchain-Based Applications You Can Integrate Into Your Business How to Get Started with Smart Contract and Ethereum Web Development Understanding Cryptocurrencies and their Emergence How Cryptocurrencies Work Bitcoin Lifecycle: How Cryptocurrency Transactions Work How to Invest In Blockchain and Cryptocurrencies And Much More! Scroll to the top of the page and select the Add to Cart button to learn more about Blockchain!
How To Recover Deleted Files
If you want to discover how to solve your problems regarding loss data in your computer then, check this"How To Recover Deleted Files" guide. In this step-by-step guide, you will reap the following benefits: - Resolve data loss problems. - Learn how to prevent data loss. - Learn how to recover data due to system boot issue. - Learn how to recover data due to hard disk issue. - Discover how to recover Office data files. - Impress your friends to recover their computer from viruses, corrupted registry and files. - Discover how to make use of the computer data recovery tools. - Recover operating system from an unstable program. - Learn how to back-up your files and folders. - And much more.HowExpert publushes quick 'how to' guides on all topics from A to Z by everyday experts.
Computer ArchitectureDigital Circuits to Microprocessors
An introductory text to computer architecture, this comprehensive volume covers the concepts from logic gates to advanced computer architecture. It comes with a full spectrum of exercises and web-downloadable support materials, including assembler and simulator, which can be used in the context of different courses. The authors also make available a hardware description, which can be used in labs and assignments, for hands-on experimentation with an actual, simple processor.This unique compendium is a useful reference for undergraduates, graduates and professionals majoring in computer engineering, circuits and systems, software engineering, biomedical engineering and aerospace engineering.
Software Ecosystems, Sustainability and Human Values in the Social Web
This book constitutes extended revised selected papers presented during the 8th Workshop of Human-Computer Interaction Aspects to the Social Web, WAIHCWS 2017, held in Joinville, Brazil, in October 2017, and during the 9th Workshop of Human-Computer Interaction Aspects to the Social Web, WAIHCWS 2018, held in Bel矇m, Brazil, in October 2018. The 5 full papers presented were thoroughly reviewed and selected from 14 submissions for WAIHCWS 2017 and 3 full papers were selected for publication from 20 submissions for WAIHCWS 2018. The authors were given the opportunity to extend and revise the papers after the conference. The topics included in this volume cover the following fields connected to the social web: user experience, emotion analysis, interoperability, systems-of-information systems, knowledge-intensive processes, ontology, transportation domain, mobile systems, privacy policies, digital legacy, social networks, recommendation models, scientific events, accessible web, software ecosystems, and sustainability.
