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Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.
Advance your career as an SQL Server developer and DBA Key Features - Cutting-edge coverage from community experts to learn T-SQL programming. - Detailed explanation of concepts and techniques for easy understanding. - Numerous practical demonstrations of T-SQL querying and programming applications. Description This book will teach you the fundamentals of SQL, SQL Server, databases, and how to write queries and programs using T-SQL. After reading this book, you will be able to create, modify, and delete databases, tables, and indexes. You can practice querying the data and running complex analytics on it. You will also be able to add, delete, and modify procedures, user-defined functions, triggers, and views. The journey of learning T-SQL with this book begins with an understanding of SQL and database fundamentals. You'll explore the SQL Server Management Studio (SSMS) used for developing and managing SQL Server databases. You'll then learn how to use DDL statements to create, modify and delete tables and indexes. Gradually, you'll be able to query in T-SQL using DML statements, joins, and various built-in functions. Successively, you'll learn XML and JSON data processing, and by the time you'll reach the end of this book, you will learn to program in SQL Server and various strategies to deploy your databases and programs. Throughout the book, you'll learn through simple examples and straightforward explanations, diagrams, and numerous real-world use-cases. What you will learn - Concise understanding of relational databases and the SQL Server. - Learn how to create database tables and indexes using T-SQL. - Learn to add, modify, and delete records. - Practice how to slice and dice data by running smart T-SQL queries. - Perform advanced analytical analysis using various functions. - Discover Error Handling and Transaction Management. - Administer XML and JSON handling with T-SQL. - Practice different deployment modes for T-SQL objects. Who this book is for If you want to know how to design, develop, and maintain SQL Server databases and run sophisticated T-SQL queries without much hassle, this book is for you. Readers with a basic understanding of programming would have an advantage. Table of Contents 1. Getting started 2. Tables 3. Index 4. DML 5. Built-In Functions - Part 1 6. Join, Apply, and Subquery 7. Built-In Functions - Part 2 8. Dealing with XML and JSON 9. Variables and Control Flow Statements 10. Temporary Tables, CTE, and MERGE Statement 11. Error Handling and Transaction Management 12. Data Conversion, Cross Database, and Cross-Server Data Access 13. Programmability 14. Deployment
Troubleshoot query performance issues, identify anti-patterns in code, and write efficient T-SQL queries Key Features Discover T-SQL functionalities and services that help you interact with relational databases Understand the roles, tasks and responsibilities of a T-SQL developer Explore solutions for carrying out database querying tasks, database administration, and troubleshooting Book Description Transact-SQL (T-SQL) is Microsoft's proprietary extension to the SQL language that is used with Microsoft SQL Server and Azure SQL Database. This book will be a useful guide to learning the art of writing efficient T-SQL code in modern SQL Server versions, as well as the Azure SQL Database. The book will get you started with query processing fundamentals to help you write powerful, performant T-SQL queries. You will then focus on query execution plans and learn how to leverage them for troubleshooting. In the later chapters, you will learn how to identify various T-SQL patterns and anti-patterns. This will help you analyze execution plans to gain insights into current performance, and determine whether or not a query is scalable. You will also learn to build diagnostic queries using dynamic management views (DMVs) and dynamic management functions (DMFs) to address various challenges in T-SQL execution. Next, you will study how to leverage the built-in tools of SQL Server to shorten the time taken to address query performance and scalability issues. In the concluding chapters, the book will guide you through implementing various features, such as Extended Events, Query Store, and Query Tuning Assistant using hands-on examples. By the end of this book, you will have the skills to determine query performance bottlenecks, avoid pitfalls, and discover the anti-patterns in use. Foreword by Conor Cunningham, Partner Architect – SQL Server and Azure SQL – Microsoft What you will learn Use Query Store to understand and easily change query performance Recognize and eliminate bottlenecks that lead to slow performance Deploy quick fixes and long-term solutions to improve query performance Implement best practices to minimize performance risk using T-SQL Achieve optimal performance by ensuring careful query and index design Use the latest performance optimization features in SQL Server 2017 and SQL Server 2019 Protect query performance during upgrades to newer versions of SQL Server Who this book is for This book is for database administrators, database developers, data analysts, data scientists, and T-SQL practitioners who want to get started with writing T-SQL code and troubleshooting query performance issues, through the help of practical examples. Previous knowledge of T-SQL querying is not required to get started on this book.
A beginner's guide to storing, managing, and analyzing data with the updated features of Elastic 7.0 Key Features Gain access to new features and updates introduced in Elastic Stack 7.0 Grasp the fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana Explore useful tips for using Elastic Cloud and deploying Elastic Stack in production environments Book Description The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and help you use it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools, and exploring their basic configurations. You'll then get up to speed with using Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You'll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you'll be well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems. What you will learn Install and configure an Elasticsearch architecture Solve the full-text search problem with Elasticsearch Discover powerful analytics capabilities through aggregations using Elasticsearch Build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis Create interactive dashboards for effective storytelling with your data using Kibana Learn how to secure, monitor and use Elastic Stack's alerting and reporting capabilities Take applications to an on-premise or cloud-based production environment with Elastic Stack Who this book is for This book is for entry-level data professionals, software engineers, e-commerce developers, and full-stack developers who want to learn about Elastic Stack and how the real-time processing and search engine works for business analytics and enterprise search applications. Previous experience with Elastic Stack is not required, however knowledge of data warehousing and database concepts will be helpful.
Leverage the Azure analytics platform's key analytics services to deliver unmatched intelligence for your dataKey FeaturesLearn to ingest, prepare, manage, and serve data for immediate business requirementsBring enterprise data warehousing and big data analytics together to gain insights from your dataDevelop end-to-end analytics solutions using Azure SynapseBook DescriptionAzure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform. The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features. By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks.What you will learnExplore the necessary considerations for data ingestion and orchestration while building analytical pipelinesUnderstand pipelines and activities in Synapse pipelines and use them to construct end-to-end data-driven workflowsQuery data using various coding languages on Azure SynapseFocus on Synapse SQL and Synapse SparkManage and monitor resource utilization and query activity in Azure SynapseConnect Power BI workspaces with Azure Synapse and create or modify reports directly from Synapse StudioCreate and manage IP firewall rules in Azure SynapseWho this book is forThis book is for data architects, data scientists, data engineers, and business analysts who are looking to get up and running with the Azure Synapse Analytics platform. Basic knowledge of data warehousing will be beneficial to help you understand the concepts covered in this book more effectively.
Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn Discover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks — the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
Leverage Elastic Stack's machine learning features to gain valuable insight from your data Key Features Combine machine learning with the analytic capabilities of Elastic Stack Analyze large volumes of search data and gain actionable insight from them Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn Install the Elastic Stack to use machine learning features Understand how Elastic machine learning is used to detect a variety of anomaly types Apply effective anomaly detection to IT operations and security analytics Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting Combine your created jobs to correlate anomalies of different layers of infrastructure Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.
Gain proficiency in monitoring infrastructure along with focusing on cloud backup and recoveryKey FeaturesExplore the 3-2-1 rule of backups in Veeam to keep your data safeGain in-depth knowledge of NAS backups and Scale-Out Repositories to use in your virtual environmentDiscover Veeam's monitoring and reporting utility - Veeam ONE - along with Linux and Window's proxyBook DescriptionVeeam is one of the leading modern data protection solutions, and mastering this technology can help you to protect your virtual environments effectively. This book guides you through implementing modern data protection solutions for your cloud and virtual infrastructure with Veeam. You will even gain in-depth knowledge of advanced concepts such as DataLabs, cloud backup and recovery, Instant VM Recovery, and Veeam ONE. This book starts by taking you through Veeam essentials, including installation, best practices, and optimizations for Veeam Backup & Replication. You'll get to grips with the 3-2-1 rule to safeguard data along with understanding how to set up a backup server, proxies, repositories, and more. Later chapters go on to cover a powerful feature of Veeam 10 – NAS backup. As you progress, you'll learn about scale-out Repositories and best practices for creating them. In the concluding chapters, you'll explore the new proxy option available in both Linux and Windows. Finally, you'll discover advanced topics such as DataLabs, cloud backup and recovery, Instant VM Recovery, and Veeam ONE. By the end of this book, you will be equipped with the skills you need to implement Veeam Backup & Replication for your environment and disaster recovery.What you will learnDiscover the advanced concepts of Veeam Backup & Replication 10Master application optimizations based on Veeam best practicesUnderstand how to configure NAS backups and work with repositories and proxiesExplore different ways to protect your backups, including object immutability and cloud backup and recoveryDiscover how DataLabs worksUnderstand how Instant VM Recovery allows you to restore virtual machinesBecome well versed in Veeam ONE for monitoring and reporting on your environmentWho this book is forThis Veeam backup book is for IT professionals who have intermediate to advanced-level knowledge of virtualization as well as backups and backup applications. Anyone who needs a reference guide for learning the advanced features of Veeam Backup & Replication and how they are used, including best practices and optimizations, will also find this book useful.
A comprehensive innovative product handbook for managers designing and deploying enterprise business solutions. Key Features - Covers proven technical approaches in migrating your enterprise systems to Oracle Cloud Computing. - A handbook for decision-makers on using Oracle Product Suite for digital transformation. - Understand the Oracle product benefits and leveraging capital investment to avail great measurable ROI and TCO. Description The Oracle Enterprise Architecture Framework emerges from the on-site legacy to current cloud native and is called Modern Oracle Enterprise Architecture. It aims to clear the path for critical business application workloads in the field of database and the application architecture to hybrid and cloud applications. This is a very handy book for chief decision-makers and professional cloud solution engineers. As the current cloud computing services are agile and pay-as-you-go (PAYG) based subscription including multi-year cost model thus a more agile approach is covered throughout the book. This book will help readers to achieve their database and application system solution architecture career objectives more quickly without spending years. The readers can prevent committing errors, recovering from them, and learning things the hard way. What you will learn - 360-degree view of Oracle database and application products. - Transition to hybrid cloud identity services via Oracle Identity Cloud platform. - Understand and implement Oracle accessibility and architecture observability. Who this book is for This book is for decision-makers, business architects, system development teams, technological professionals and product teams who want to use the Oracle stack's hidden capabilities to develop, manage and keep enhancing enterprise systems. Table of Contents 01. Artificial Intelligence for Cloud Computing 02. Business Benefits of Migrating and Operating on Oracle Cloud 03. Move and Optimize the Cost for Oracle E-Business Suite on Cloud Compute 04. Contemplating IaaS, PaaS, and SaaS Migration for On-Premise Legacy Systems 05. Oracle Autonomous Dedicated for Oracle E-Business Suite Customers 06. Benefits of Oracle PeopleSoft with Autonomous Database Dedicated and Shared 07. Oracle Autonomous Dedicated for Oracle E-Business Suite Customers 08. Oracle Agile Maximum-Security Architecture (AMSA) 09. Agile Accessibility and Observability Architecture Agile AOA (AAOA) 10. Fleet Management for On-Premises and Cloud (DBaaS and IaaS) Database Stack 11. Identity transition from Identity Manager (IDM) to Universal Directory (OUD) and Identity Cloud Suite 12. Decision Analysis Resolution (DAR) for Oracle E-Business Suite on Cloud Compute 13. Hidden Jewel on Oracle Crown. Oracle Enterprise Manager Site Guard Use Cases: 14. Case Study One Oracle E-Business Suite Migration to OCI with Business Continuity Site 15. Case Study Two. Oracle E-Business Suite Migration to OCI with Business Continuity Site 16. Case Study Three. Oracle Universal Directory Installation and Configuration About the Authors Javid Ur Rahman is a distinguished database product manager and enterprise solution architect who has been actively involved in productizing and promoting cross-ecosystem collaboration in the Cloud Infrastructure, Edge computing and Analytics Platform space for over the last two decades. He has been focused on research and development of blockchain-based database algorithm designs and cloud-native-run engine development. Blog links: http://jrahaman.ai/ LinkedIn Profile: https://www.linkedin.com/in/jrahaman7/