Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers

This Solution Redpaper™ publication explains how the features of IBM Cloud® Object Storage System reduces the effect of incidents on business data when combined with log analysis, deep inspection, and detection of threats that IBM QRadar SIEM provides. This paper also demonstrates how to integrate IBM Cloud Object Storage's access logs with IBM QRadar SIEM. An administrator can monitor, inspect, detect, and derive insights for identifying potential threats to the data that is stored on IBM Cloud Object Storage. Also, IBM QRadar SIEM can proactively trigger cyber resiliency workflow in IBM Cloud Object Storage remotely to protect the data based on threat detection. This publication is intended for chief technology officers, solution and security architects, and systems administrators.

Many organizations must protect their mission-critical applications in production, but security threats can also surface during the development and pre-production phases. Also, during deployment and production, insiders who manage the infrastructure that hosts critical applications can pose a threat given their super-user credentials and level of access to secrets or encryption keys. Organizations must incorporate secure design practices in their development operations and embrace DevSecOps to protect their applications from the vulnerabilities and threat vectors that can compromise their data and potentially threaten their business. IBM® Cloud Hyper Protect Services provide built-in data-at-rest and data-in-flight protection to help developers easily build secure cloud applications by using a portfolio of cloud services that are powered by IBM LinuxONE. The LinuxONE platform ensures that client data is always encrypted, whether at rest or in transit. This feature gives customers complete authority over sensitive data and associated workloads (which restricts access, even for cloud admins) and helps them meet regulatory compliance requirements. LinuxONE also allows customers to build mission-critical applications that require quick time to market and dependable rapid expansion. The purpose of this IBM Redbooks® publication is to: Introduce the IBM Hyper Protect Services that are running on IBM LinuxONE on the IBM Cloud™ and on-premises Provide high-level design architectures Describe deployment best practices Provide guides to getting started and examples of the use of the Hyper Protect Services The target audience for this book is IBM Hyper Protect Virtual Services technical specialists, IT architects, and system administrators.

Gain competitive advantage by adopting the best practices of established companies such as FedEx and CarMax, who successfully transformed their practices around people, processes, technology, internal partnerships and external networks.

Extending Microsoft Power Apps with Power Apps Component Framework is your ultimate guide to mastering the creation and deployment of advanced code components within the Microsoft Power Apps environment. You'll explore the framework's fundamentals and advanced techniques through hands-on examples, enabling you to leverage its full capabilities. What this Book will help me do Understand the Power Apps Component Framework and its development lifecycle. Develop custom controls using modern web development technologies. Debug and troubleshoot components effectively with the help of tools like Fiddler. Implement advanced concepts like authentication profiles and data caching. Deploy and configure components across both model-driven and canvas apps. Author(s) None Naglekar is a seasoned software developer specializing in Microsoft Power Platform and has been crafting enterprise solutions using Power Apps for several years. With a strong background in both platform extensibility and modern web development practices, None brings extensive practical knowledge to this book. Their teaching approach focuses on clarity and real-world applications, making complex topics approachable and actionable. Who is it for? This book is written for developers with some experience in Power Apps and web development who are looking to enhance their skills in extending Power Apps functionality. Ideal for professionals aiming to build advanced custom components using the Power Apps Component Framework, this book helps you turn ideas into deployable solutions. It's suited for programmers who want to deepen their understanding of extending Microsoft's low-code platform.

The "PostgreSQL 13 Cookbook" is your step-by-step resource for mastering PostgreSQL 13. Explore over 120 recipes, solving both common and advanced database management challenges, with a focus on high performance, fault tolerance, and cutting-edge features. What this Book will help me do Master the implementation of backup and recovery strategies tailored for PostgreSQL 13. Set up robust high availability clusters ensuring seamless failover with PostgreSQL replication features. Improve performance using optimization techniques specific to PostgreSQL 13 databases. Secure your databases with advanced authentication, encryption, and auditing measures. Analyze and monitor PostgreSQL servers to identify performance bottlenecks and maintain uptime efficiently. Author(s) Vallarapu Naga Avinash Kumar is an experienced PostgreSQL architect and developer who brings years of expertise in designing and managing enterprise-level databases. He has authored resources that simplify complex technical concepts for readers. His meticulous and straightforward writing approach empowers readers to skillfully apply PostgreSQL concepts in real-world scenarios. Who is it for? This book is perfect for database administrators, architects, and developers aiming to master PostgreSQL 13 capabilities. If you have prior experience with PostgreSQL and SQL, this cookbook will be a reliable reference to solve challenges and optimize your database solutions. If you're designing or managing databases, you'll find practical insights and actionable recipes tailored to your needs.

The "Snowflake Cookbook" is your guide to mastering Snowflake's unique cloud-centric architecture. This book provides detailed recipes for building modern data pipelines, configuring efficient virtual warehouses, ensuring robust data protection, and optimizing cost-performance-all while leveraging Snowflake's distinctive features such as data sharing and time travel. What this Book will help me do Set up and configure Snowflake's architecture for optimized performance and cost efficiency. Design and implement robust data pipelines using SQL and Snowflake's specialized features. Secure, manage, and share data efficiently with built-in Snowflake capabilities. Apply performance tuning techniques to enhance your Snowflake implementations. Extend Snowflake's functionality with tools like Spark Connector for advanced workflows. Author(s) Hamid Mahmood Qureshi and Hammad Sharif are both seasoned experts in data warehousing and cloud computing technologies. With extensive experience implementing analytics solutions, they bring a hands-on approach to teaching Snowflake. They are ardent proponents of empowering readers towards creating effective and scalable data solutions. Who is it for? This book is perfect for data warehouse developers, data analysts, cloud architects, and anyone managing cloud data solutions. If you're familiar with basic database concepts or just stepping into Snowflake, you'll find practical guidance here to deepen your understanding and functional expertise in cloud data warehousing.

Get up and running with SAS using Ron Cody’s easy-to-follow, step-by-step guide. Aimed at beginners, Getting Started with SAS Programming: Using SAS Studio in the Cloud uses short examples to teach SAS programming from the basics to more advanced topics in the point-and-click interactive environment of SAS Studio. To begin, you will learn how to register for SAS OnDemand for Academics, an online delivery platform for teaching and learning statistical analysis that provides free access to SAS software via the cloud. The first part of the book shows you how to use SAS Studio built-in tasks to produce a report, summarize data, and create charts and graphs. It also describes how you can perform basic statistical tests using the interactive point-and-click environment. The second part of the book uses easy-to-follow examples to show you how to write your own SAS programs and how to use SAS procedures to perform a variety of tasks. This part of the book also explains how to read data from a variety of sources: text files, Excel workbooks, and CSV files. In order to get familiar with the SAS Studio environment, this book also shows you how to access dozens of interesting data sets that are included with the SAS OnDemand for Academics platform.

This IBM® Redbooks® publication describes the IBM VM Recovery Manager for Power Systems, and addresses topics to help answer customers' complex high availability (HA) and disaster recovery (DR) requirements for IBM AIX® and Linux on IBM Power Systems servers to help maximize systems' availability and resources, and provide technical documentation to transfer the how-to skills to users and support teams. The IBM VM Recovery Manager for Power Systems product is an easy to use and economical HA and DR solution. Automation software, installation services, and remote-based software support help you streamline the process of recovery, which raises availability and recovery testing, and maintains a state-of-the-art HA and DR solution. Built-in functions and IBM Support can decrease the need for expert-level skills and shorten your recovery time objective (RTO), improve your recovery point objective (RPO), optimize backups, and better manage growing data volumes. This book examines the IBM VM Recovery Manager solution, tools, documentation, and other resources that are available to help technical teams develop, implement, and support business resilience solutions in IBM VM Recovery Manager for IBM Power Systems environments. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing HA and DR solutions and support for IBM Power Systems.

Discover how easy it is to create multi-user, cross-platform custom solutions with FileMaker Pro, the relational database platform published by Apple subsidiary Claris International, Inc. Meticulously rewritten with clearer lessons, more real-world examples and updated to include feature changes introduced in recent versions, this book makes it easier to get started planning, building and deploying a custom database solution. The material is presented in an easy to follow manner with each chapter building on the last. After an initial review of the user environment and application basics, it begins a deep exploration of the integrated development environment that seamlessly combines the full stack of data table schema, business logic and interface layers into one visual programming experience. This book includes everything needed to get started building custom databases and contains advanced material that seasoned professionals will appreciate. Written bya professional developer with decades of real-world experience, Learn FileMaker Pro 19 is your comprehensive learning and reference guide. Join millions of users and developers worldwide in achieving a new level of workflow efficiency with FileMaker Pro. What You’ll Learn Discover interface and feature changes in FileMaker 17-19 Create and maintain healthy files Plan and create custom tables, fields, relationships Write calculations using built-in and custom functions Build recursive and repeating formulas Discover advanced features using cURL, JSON, SQL, ODBC and FM URL Manipulate data files in the computer directory with scripts Deploy solutions to a server and share with desktop, iOS and web clients Who This Book Is For Casual programmers, full time consultants, and IT professionals

Protect business value, stay compliant with global regulations, and meet stakeholder demands with this privacy how-to Privacy, Regulations, and Cybersecurity: The Essential Business Guide is your guide to understanding what “privacy” really means in a corporate environment: how privacy is different from cybersecurity, why privacy is essential for your business, and how to build privacy protections into your overall cybersecurity plan. First, author Chris Moschovitis walks you through our evolving definitions of privacy, from the ancient world all the way to the General Law on Data Protection (GDPR). He then explains—in friendly, accessible language—how to orient your preexisting cybersecurity program toward privacy, and how to make sure your systems are compliant with current regulations. This book—a sequel to Moschovitis’ well-received Cybersecurity Program Development for Business—explains which regulations apply in which regions, how they relate to the end goal of privacy, and how to build privacy into both new and existing cybersecurity programs. Keeping up with swiftly changing technology and business landscapes is no easy task. Moschovitis provides down-to-earth, actionable advice on how to avoid dangerous privacy leaks and protect your valuable data assets. Learn how to design your cybersecurity program with privacy in mind Apply lessons from the GDPR and other landmark laws Remain compliant and even get ahead of the curve, as privacy grows from a buzzword to a business must Learn how to protect what’s of value to your company and your stakeholders, regardless of business size or industry Understand privacy regulations from a business standpoint, including which regulations apply and what they require Think through what privacy protections will mean in the post-COVID environment Whether you’re new to cybersecurity or already have the fundamentals, this book will help you design and build a privacy-centric, regulation-compliant cybersecurity program.

Build custom SQL Server Integration Services (SSIS) tasks using Visual Studio Community Edition and C#. Bring all the power of Microsoft .NET to bear on your data integration and ETL processes, and for no added cost over what you’ve already spent on licensing SQL Server. New in this edition is a demonstration deploying a custom SSIS task to the Azure Data Factory (ADF) Azure-SSIS Integration Runtime (IR). All examples in this new edition are implemented in C#. Custom task developers are shown how to implement custom tasks using the widely accepted and default language for .NET development. Why are custom components necessary? Because even though the SSIS catalog of built-in tasks and components is a marvel of engineering, gaps remain in the available functionality. One such gap is a constraint of the built-in SSIS Execute Package Task, which does not allow SSIS developers to select SSIS packages from other projects in the SSIS Catalog. Examples in this bookshow how to create a custom Execute Catalog Package task that allows SSIS developers to execute tasks from other projects in the SSIS Catalog. Building on the examples and patterns in this book, SSIS developers may create any task to which they aspire, custom tailored to their specific data integration and ETL needs. What You Will Learn Configure and execute Visual Studio in the way that best supports SSIS task development Create a class library as the basis for an SSIS task, and reference the needed SSIS assemblies Properly sign assemblies that you create in order to invoke them from your task Implement source code control via Azure DevOps, or your own favorite tool set Troubleshoot and execute custom tasks as part of your own projects Create deployment projects (MSIs) for distributing code-complete tasks Deploy custom tasks to Azure Data Factory Azure-SSIS IRs in the cloud Create advanced editors for custom task parameters Who This Book Is For For database administrators and developers who are involved in ETL projects built around SQL Server Integration Services (SSIS). Readers do not need a background in software development with C#. Most important is a desire to optimize ETL efforts by creating custom-tailored tasks for execution in SSIS packages, on-premises or in ADF Azure-SSIS IRs.

High-speed I/O workloads are moving away from the SAN to Ethernet and IBM® Spectrum Scale is pushing the network limits. The IBM Spectrum® Scale team discovered that many infrastructure Ethernet networks that were used for years to support various applications are not designed to provide a high-performance data path concurrently to many clients from many servers. IBM Spectrum Scale is not the first product to use Ethernet for storage access. Technologies, such as Fibre Channel over Ethernet (FCoE), scale out NAS, and IP connected storage (iSCSI and others) use Ethernet though IBM Spectrum Scale as the leader in parallel I/O performance, which provides the best performance and value when used on a high-performance network. This IBM Redpaper publication is based on lessons that were learned in the field by deploying IBM Spectrum Scale on Ethernet and InfiniBand networks. This IBM Redpaper® publication answers several questions, such as, "How can I prepare my network for high performance storage?", "How do I know when I am ready?", and "How can I tell what is wrong?" when deploying IBM Spectrum Scale and IBM Elastic Storage® Server (ESS). This document can help IT architects get the design correct from the beginning of the process. It also can help the IBM Spectrum Scale administrator work effectively with the networking team to quickly resolve issues.

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting

Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.

Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time. Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing. Learn the basics of Kafka and the pub/sub communication pattern Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB Perform advanced stateful operations, including windowed joins and aggregations Understand how stateful processing works under the hood Learn about ksqlDB's data integration features, powered by Kafka Connect Work with different types of collections in ksqlDB and perform push and pull queries Deploy your Kafka Streams and ksqlDB applications to production

Learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. With this book you will review contemporary MATLAB coding including the latest MATLAB language features and use MATLAB as a software development environment including code organization, GUI development, and algorithm design and testing. Features now covered include the new graph and digraph classes for charts and networks; interactive documents that combine text, code, and output; a new development environment for building apps; locally defined functions in scripts; automatic expansion of dimensions; tall arrays for big data; the new string type; new functions to encode/decode JSON; handling non-English languages; the new class architecture; the Mocking framework; an engine API for Java; the cloud-based MATLAB desktop; the memoize function; and heatmap charts. MATLAB Recipes: A Problem-Solution Approach, Second Edition provides practical, hands-on code snippets and guidance for using MATLAB to build a body of code you can turn to time and again for solving technical problems in your work. Develop algorithms, test them, visualize the results, and pass the code along to others to create a functional code base for your firm. What You Will Learn Get up to date with the latest MATLAB up to and including MATLAB 2020b Code in MATLAB Write applications in MATLAB Build your own toolbox of MATLAB code to increase your efficiency and effectiveness Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples using MATLAB.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.

This IBM® Redbooks® publication is a detailed technical guide to the IBM System Storage™ SAN Volume Controller, which is powered by IBM Spectrum® Virtualize V8.3.1. IBM SAN Volume Controller is a virtualization appliance solution that maps virtualized volumes that are visible to hosts and applications to physical volumes on storage devices. Each server within the storage area network (SAN) has its own set of virtual storage addresses that are mapped to physical addresses. If the physical addresses change, the server continues running by using the same virtual addresses that it had before. Therefore, volumes or storage can be added or moved while the server is still running. The IBM virtualization technology improves the management of information at the block level in a network, which enables applications and servers to share storage devices on a network.