Understand the basics of data - how to collect, analyse, present and apply it - and give your career a valuable, cutting-edge boost with this engaging and easy-to-read beginner's guide.

Create a world-class MongoDB cluster that is scalable, reliable, and secure. Comply with mission-critical regulatory regimes such as the European Union’s General Data Protection Regulation (GDPR). Whether you are thinking of migrating to MongoDB or need to meet legal requirements for an existing self-managed cluster, this book has you covered. It begins with the basics of replication and sharding, and quickly scales up to cover everything you need to know to control your data and keep it safe from unexpected data loss or downtime. This book covers best practices for stable MongoDB deployments. For example, a well-designed MongoDB cluster should have no single point of failure. The book covers common use cases when only one or two data centers are available. It goes into detail about creating geopolitical sharding configurations to cover the most stringent data protection regulation compliance. The book also covers different tools and approaches for automating and monitoring a cluster with Kubernetes, Docker, and popular cloud provider containers. What You Will Learn Get started with the basics of MongoDB clusters Protect and monitor a MongoDB deployment Deepen your expertise around replication and sharding Keep effective backups and plan ahead for disaster recovery Recognize and avoid problems that can occur in distributed databases Build optimal MongoDB deployments within hardware and data center limitations Who This Book Is For Solutions architects, DevOps architects and engineers, automation and cloud engineers, and database administrators who are new to MongoDB and distributed databases or who need to scale up simple deployments. This book is a complete guide to planning a deployment for optimal resilience, performance, and scaling, and covers all the details required to meet the new set of data protection regulations such as the GDPR. This book is particularly relevant for large global organizations such as financial and medical institutions, as well as government departments that need to control data in the whole stack and are prohibited from using managed cloud services.

The fun and friendly guide to mastering IBM’s Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis. Covering the latest release and updates to SPSS 27.0, and including more than 150 pages of basic statistical theory, it helps you understand the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and more. You’ll even dabble in programming as you expand SPSS functionality to suit your specific needs. Master the fundamental mechanics of SPSS Learn how to get data into and out of the program Graph and analyze your data more accurately and efficiently Program SPSS with Command Syntax Get ready to start handling data like a pro—with step-by-step instruction and expert advice!

"Learning Tableau 2020" is a comprehensive resource designed to strengthen your understanding of Tableau. It takes you from mastering the fundamentals to achieving proficiency in advanced visualization and data handling techniques. Through this book, you will gain the ability to create impactful data visualizations and interactive dashboards, effectively leveraging the capabilities of Tableau 2020. What this Book will help me do Effectively utilize Tableau 2020 features to develop data visualizations and dashboards. Apply advanced Tableau techniques, such as LOD and table calculations, to solve complex data analysis problems. Clean and structure data using Tableau Prep, enhancing data quality and reliability. Incorporate mapping and geospatial visualization for geographic data insights. Master storytelling with data by constructing engaging and interactive dashboards. Author(s) Joshua N. Milligan, the author of "Learning Tableau 2020," is an experienced Tableau training consultant and professional. With extensive years in the data visualization and analytics field, Joshua brings a practical perspective to the book. He excels at breaking down complex topics into accessible learning paths, making advanced Tableau concepts approachable for learners of all levels. Who is it for? This book is perfect for aspiring data analysts, IT professionals, and data enthusiasts who aim to understand and create compelling business intelligence reports. Beginners in Tableau will find the learning process straightforward due to its structured and incremental lessons. Advanced users can refine their skills with the wide range of complex examples covered. A basic familiarity with working with data is beneficial, though not required.

The Data Science Workshop provides a comprehensive introduction to building real-world data science projects. Through a hands-on approach, you will learn how to analyze data, build machine learning models, and deploy them effectively in various scenarios. This book is designed to equip you with the skills to confidently tackle data science challenges. What this Book will help me do Understand the differences between supervised and unsupervised learning to select the appropriate technique. Master data manipulation and analysis using popular Python libraries like pandas and scikit-learn. Develop skills in regression, classification, and clustering to solve diverse data science problems. Learn advanced methods to improve model accuracy, including hyperparameter tuning and feature engineering. Implement and deploy machine learning models efficiently in production workflows. Author(s) The authors of The Data Science Workshop are experienced professionals and educators in the field of data science and machine learning. They have extensive expertise in using practical methods to solve data challenges and have a passion for teaching others through engaging and clear instructional material. Who is it for? This book is ideal for aspiring data analysts, data scientists, and business analysts who wish to build foundational skills in data science. It caters to those new to the field and professionals transitioning to a data-centric role, providing practical knowledge without requiring an advanced mathematical background. Familiarity with Python is recommended.

Can smartphone apps help track the spread of the novel coronavirus, privately and securely? In this report, Rob Pegoraro weighs the issue of whether mobile apps can help trace and then slow the spread of COVID-19 or will end up as just another episode of botched government procurement and application of technology. Apple and Google have recently devised a system to track COVID-19 infections anonymously using Bluetooth with iOS and Android smartphones. This development points a spotlight on a needed debate about balancing privacy and collecting useful data. Do privacy-optimizing techniques, such as federated learning and differential privacy, offer useful alternatives to building centralized databases that may later invite abuse? This report takes a close look at this subject and then provides recommendations for software developers, public health authorities, and elected officials who want to build on the Apple-Google API. Understand the scope of the problem, including how contact tracing can help slow and stop outbreaks Take a closer look at Apple and Google’s proposed remedy Learn how other countries including Singapore, India, France, and Australia have traced the spread of COVID-19 Examine the risk factors for adopting and using a decentralized system like the Apple-Google app

Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands-On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: · Explains basics to advanced concepts of time series · How to design, develop, train, and validate time-series methodologies · What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results · Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. · Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers

In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg

This book is your gateway into the world of graph analytics with Neo4j, empowering you to reveal insights hidden in connected data. By diving into real-world examples, you'll learn how to implement algorithms to uncover relationships and patterns critical for applications such as fraud detection, recommendation systems, and more. What this Book will help me do Understand fundamental concepts of the Neo4j graph database, including nodes, relationships, and Cypher querying. Effectively explore and visualize data relationships, enhancing applications like search engines and recommendations. Gain proficiency in graph algorithms such as pathfinding and spatial search to solve key business challenges. Leverage Neo4j's Graph Data Science library for machine learning and predictive analysis tasks. Implement web applications that utilize Neo4j for scalable, production-ready graph data management. Author(s) None Scifo is an experienced author in graph technologies, extensively working with Neo4j. He brings practical knowledge and a hands-on approach to the forefront, making complex topics accessible to learners of all levels. Through his work, he continues to inspire readers to harness the power of connected data effectively. Who is it for? This book is perfect for professionals like data analysts, business analysts, graph analysts, and database developers aiming to delve into graph data. It caters to those seeking to solve problems through graph analytics, whether in fraud detection, recommendation systems, or other fields. Some prior experience with Neo4j is recommended for maximal benefit.

Artificial Intelligence Business: How you can profit from AI is your essential guide to understanding how AI shapes the modern business landscape. This book guides you through the power of machine learning and artificial intelligence, revealing how these technologies can elevate businesses and impact society. What this Book will help me do Gain insight into how artificial intelligence can foster innovative cultures in enterprises. Learn key strategies for utilizing AI to accelerate start-up success. Understand the application of AI in fields such as manufacturing, logistics, and content generation. Discover how text and image generation technologies are transforming modern industries. Explore the societal and political implications of artificial intelligence. Author(s) Noelle Silver Russel and Przemek Chojecki bring extensive expertise in artificial intelligence and business strategy. Noelle is a renowned AI thought leader, with substantial experience in applying machine learning in corporate settings. Przemek is a successful entrepreneur and technologist passionate about innovation in AI. Together, they provide approachable yet informed insights tailored for practical learning. Who is it for? This book is perfect for professionals or enthusiasts with an interest in artificial intelligence and its applications in business. It's suitable for individuals in business roles seeking to enhance their understanding of AI's potential to transform enterprises. The content is designed for learners ranging from AI beginners to those with moderate knowledge looking to explore AI's practical uses. If you're keen on leveraging AI for strategic advantage, this book is for you.

What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges

Build enterprise-grade microservices in the SAP HANA Advanced Model (XSA). This book explains building scalable APIs in XSA and the benefits of building microservices with SAP HANA XSA. This book covers the cloud foundry (CF) architecture and how SAP HANA XSA follows the model. It begins with the details of the different architectural layers of applications hosted in XSA (specifically, microservices). Everything you need to know is presented, including analyzing requests, modularization, database ingestion, building JSON responses, and scaling your microservices. You will learn to use developmental tools such as the SAP WEB IDE, POSTMAN, and the SAP HANA Cockpit for XSA, including debugging examples on SAP HANA XSA with code snippets showing how microservices can be developed, debugged, scaled, and deployed on SAP HANA XSA. Microservices are divided into security and authentication, request handling, modularization of Node.js, and interaction with the SAP HANA database containers and response formatting. An end-to-end scenario is presented of a Node.js REST API that uses HTTP methods, concluding with deploying an SAP HANA XSA project to a production environment. This book is simple enough to help you implement a Node.js module in order to understand the development of microservices, and complex enough for architects to design their next business-ready solution integrating UAA security, application modularization, and an end-to-end REST API on SAP HANA XSA. What You Will Learn Know the definition and architecture of cloud foundry and its application on SAP HANA XSA Understand REST principles and different HTTP methods Explore microservices (Node.js) development Database interaction from Node (executing SQL statements and stored procedures) Who This Book Is For Architects designing business-ready solutions that integrate UAA security, application modularization, and an end-to-end REST API on SAP HANA XSA

Whether you are an executive, departmental decision maker, or analyst, the need to leverage data and analytical techniques in order make critical business decisions is now crucial to every part of an organization. Gain Powerful Insights with SAS Viya! Smart Data Discovery with SAS Viya: Powerful Techniques for Deeper Insights provides you with the necessary knowledge and skills to conduct a smart discovery process and empower you to ask more complex questions using your data. The book highlights key components of a smart data discovery process utilizing advanced machine learning techniques, powerful capabilities from SAS Viya, and finally brings it all together using real examples and applications. With its step-by-step approach and integrated examples, the book provides a relevant and practical guide to insight discovery that goes beyond traditional charts and graphs. By showcasing the powerful visual modeling capabilities of SAS Viya, it also opens up the world of advanced analytics and machine learning techniques to a much broader set of audiences.

How the data revolution is transforming biotech and health care, especially in the wake of COVID-19—and why you can’t afford to let it pass you by We are living through a time when the digitization of health and medicine is becoming a reality, with new abilities to improve outcomes for patients as well as the efficiency and success of the organizations that serve them. In The Patient Equation, Glen de Vries presents the history and current state of life sciences and health care as well as crucial insights and strategies to help scientists, physicians, executives, and patients survive and thrive, with an eye toward how COVID-19 has accelerated the need for change. One of the biggest challenges facing biotech, pharma, and medical device companies today is how to integrate new knowledge, new data, and new technologies to get the right treatments to the right patients at precisely the right times—made even more profound in the midst of a pandemic and in the years to come. Drawing on the fascinating stories of businesses and individuals that are already making inroads—from a fertility-tracking bracelet changing the game for couples looking to get pregnant, to an entrepreneur reinventing the treatment of diabetes, to Medidata's own work bringing clinical trials into the 21st century—de Vries shares the breakthroughs, approaches, and practical business techniques that will allow companies to stay ahead of the curve and deliver solutions faster, cheaper, and more successfully—while still upholding the principles of traditional therapeutic medicine and reflecting the current environment. How new approaches to cancer and rare diseases are leading the way toward precision medicine What data and digital technologies enable in the building of robust, effective disease management platforms Why value-based reimbursement is changing the business of life sciences How the right alignment of incentives will improve outcomes at every stage of the patient journey Whether you're a scientist, physician, or executive, you can't afford to let the moment pass: understand the landscape with this must-read roadmap for success—and see how you can change health care for the better.

Discover how to power your distributed and scalable applications using RabbitMQ in "RabbitMQ Essentials". This book provides a detailed journey into understanding and implementing message queuing architectures, guiding you from the basics through advanced techniques. Through a realistic case study, you'll gain the skills necessary to succeed with RabbitMQ. What this Book will help me do Understand the core concepts and architecture of RabbitMQ and message queuing. Learn how to configure and use RabbitMQ, including installation and plugin management. Master the use of channels, routing strategies, and exchange types for optimized message delivery. Apply strategies for ensuring message queue scalability and robust fault-tolerance. Gain insights and best practices directly from RabbitMQ experts for production-level deployment. Author(s) None Johansson and David Dossot bring a wealth of experience managing and deploying systems based on RabbitMQ. As part of CloudAMQP, they oversee the largest RabbitMQ installations globally. This book reflects their dedication to helping developers succeed with message queuing technology. Who is it for? This book is perfectly suited for developers and software engineers interested in designing scalable and distributed applications. Whether you're new to RabbitMQ or already familiar with microservices and message queuing, "RabbitMQ Essentials" provides clear guidance and real-world insights. Beginners will appreciate its accessible approach, while advanced developers will value its comprehensive coverage and best practices.

This IBM® Redbooks® publication helps you install, configure, and maintain the IBM z15™ (machine types 8561 and 8562) systems. The z15 systems offers new functions that require a comprehensive understanding of the available configuration options. This book presents configuration setup scenarios, and describes implementation examples in detail. This publication is intended for systems engineers, hardware planners, and anyone who needs to understand IBM Z® configuration and implementation. Readers should be familiar with IBM Z technology and terminology. For more information about the functions of the z15 systems, see IBM z15 Technical Introduction, SG24-8850, IBM z15 (8561) Technical Guide, SG24-8851 and IBM z15 (8562) Technical Guide, SG24-8852.

IBM® Spectrum Archive Enterprise Edition for the IBM TS4500, IBM TS3500, IBM TS4300, and IBM TS3310 tape libraries provides seamless integration of IBM Linear Tape File System (LTFS) with IBM Spectrum® Scale by creating an LTFS tape tier. You can run any application that is designed for disk files on tape by using IBM Spectrum Archive. IBM Spectrum Archive can play an important role in reducing the cost of storage for data that does not need the access performance of primary disk. The IBM Spectrum Archive Virtual Appliance can be deployed in minutes and key features can be tried along with this user guide. The virtual machine (VM) has a pre-configured IBM Spectrum Scale and a virtual tape library that allows to quickly test the IBM Spectrum Archive features without connecting to a physical tape library. The virtual appliance is provided as a VirtualBox .ova file.

The purpose of this document is to show how to install RedHat OpenShift Container Platform (OCP) on Amazon web services (AWS) public cloud with OpenShift installer, a method that is known as Installer-provisioned infrastructure (IPI). We also describe how to validate the installation of IBM container storage interface (CSI) driver on OCP 4.2 that is installed on AWS. This document also describes the installation of OCP 4.x on AWS with customization and OCP 4.x installation on IBM cloud. This document discusses how to provision internet small computer system interface (iSCSI) storage that is made available by IBM Spectrum® Virtualize for Public Cloud (SVPC) that is deployed on AWS. Finally, the document discusses the use of Red Hat OpenShift command line interface (CLI), OCP web console graphical user interface (GUI), and AWS console.

Explore the intersection of machine learning and algorithmic trading with "Machine Learning for Algorithmic Trading" by Stefan Jansen. This comprehensive guide walks you through applying predictive modeling and data analysis to uncover financial signals and build systematic trading strategies. By the end, you'll be equipped to design and implement machine learning-driven trading systems. What this Book will help me do Develop data-driven trading strategies using supervised, unsupervised, and reinforcement learning methods. Master techniques for extracting predictive features from market and alternative datasets. Gain expertise in backtesting and validating ML-based trading strategies in Python. Apply text analysis techniques like NLP to news articles and transcripts for financial insights. Optimize portfolio risk and returns using advanced Python libraries. Author(s) Stefan Jansen is a quantitative researcher and data scientist with extensive experience in developing algorithmic trading solutions. He specializes in leveraging machine learning to extract financial insights and optimize investment strategies. His practical approach to applying ML in trading is reflected in this comprehensive guide, helping readers navigate complex trading challenges. Who is it for? This book is crafted for Python developers, data scientists, and finance professionals looking to integrate machine learning into algorithmic trading. Ideal for those with a basic understanding of Python and ML principles, it guides readers in crafting data-driven trading strategies. It's especially useful for analysts aiming to harness diverse data types for financial applications.

As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata