The sheer volume of business data has reached an all-time high. Using visualizations to transform this data into useful and understandable information can facilitate better decision-making. This practical book shows data analysts as well as professionals in finance, sales, and marketing how to quickly create visualizations and build savvy dashboards. Alex Kolokolov from Data2Speak and Maxim Zelensky from Intelligent Business explain in simple and clear language how to create brilliant charts with Microsoft Power BI and follow best practices for corporate reporting. No technical background is required. Step-by-step guides help you set up any chart in a few clicks and avoid common mistakes. Also, experienced data analysts will find tips and tricks on how to enrich their reports with advanced visuals. This book helps you understand: The basic rules for classic charts that are used in 90% of business reports Exceptions to general rules based on real business cases Best practices for dashboard design How to properly set up interactions How to prepare data for advanced visuals How to avoid pitfalls with eye-catching charts

The new edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods.

As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively. Data mesh is one solution that provides a new approach to managing data in complex organizations. This practical guide offers step-by-step guidance on how to implement data mesh in your organization. In this book, Jean-Georges Perrin and Eric Broda focus on the key components of data mesh and provide practical advice supported by code. Data engineers, architects, and analysts will explore a simple and intuitive process for identifying key data mesh components and data products. You'll learn a consistent set of interfaces and access methods that make data products easy to consume. This approach ensures that your data products are easily accessible and the data mesh ecosystem is easy to navigate. This book helps you: Identify, define, and build data products that interoperate within an enterprise data mesh Build a data mesh fabric that binds data products together Build and deploy data products in a data mesh Establish the organizational structure to operate data products, data platforms, and data fabric Learn an innovative architecture that brings data products and data fabric together into the data mesh About the authors: Jean-Georges "JG" Perrin is a technology leader focusing on building innovative and modern data platforms. Eric Broda is a technology executive, practitioner, and founder of a boutique consulting firm that helps global enterprises realize value from data.

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

This revised and updated 8th edition retains its focus on processes that require understanding, have variation, must be properly controlled, have a capability, and need improvement – as reflected in the five sections of the book.

Master Amazon DynamoDB, the serverless NoSQL database designed for lightning-fast performance and scalability, with this definitive guide. You'll delve into its features, learn advanced concepts, and acquire practical skills to harness DynamoDB for modern application development. What this Book will help me do Understand AWS DynamoDB fundamentals for real-world applications. Model and optimize NoSQL databases with advanced techniques. Integrate DynamoDB into scalable, high-performance architectures. Utilize DynamoDB indexing, caching, and analytical features effectively. Plan and execute RDBMS to NoSQL data migrations successfully. Author(s) None Dhingra, an AWS DynamoDB solutions expert, and None Mackay, a seasoned NoSQL architect, bring their combined expertise straight from Amazon Web Services to guide you step-by-step in mastering DynamoDB. Combining comprehensive technical knowledge with approachable explanations, they empower readers to implement practical and efficient data strategies. Who is it for? This book is ideal for software developers and architects seeking to deepen their knowledge about AWS solutions like DynamoDB, engineering managers aiming to incorporate scalable NoSQL solutions into their projects, and data professionals transitioning from RDBMS towards a serverless data approach. Individuals with basic knowledge in cloud computing or database systems and those ready to advance in DynamoDB will find this book particularly beneficial.

This comprehensive guide will teach you how to build an income statement semantic model, also known as the profit and loss (P&L) statement. Author Chris Barber— a business intelligence (BI) consultant, Microsoft MVP, and chartered accountant (ACMA, CGMA)—helps you master everything from designing conceptual models to building semantic models based on these designs. You will learn how to build a re-usable solution based on the trial balance and how to expand upon this to build enterprise-grade solutions. If you want to leverage the Microsoft BI platform to understand profit within your organization, this is the resource you need. What You Will Learn Modeling and the income statement: Learn what modelling the income statement entails, why it is important, and how income statements are constructed Calculating account balances: Learn how to optimally calculate account balances using a Star Schema Producing external income statement semantic models: Learn how to produce external income statement semantic models as they enable income statements to be analyzed from a range of perspectives and can be explored to reveal the underlying accounts and journal entries Producing internal income statement semantic models: Learn how to create multiple income statement layouts and further contextualize financial information by including percentages and non-financial information, and learn about the various security and self-service considerations Who This Book Is For Technical users (solution architects, Microsoft Fabric developers, Power BI developers) who require a comprehensive methodology for income statement semantic models because of the modeling complexities and knowledge needed of the accounting process; and finance (management accountants) who have hit the limits of Excel and have started using Power BI, but are unsure how income statement semantic models are built

Full Stack FastAPI, React, and MongoDB guides you step-by-step through creating web applications using the FARM stack. This hands-on resource teaches you how to integrate FastAPI, a modern Python framework, React for front-end development, and MongoDB for data storage to build and deploy powerful, scalable web applications. What this Book will help me do Master the essentials of MongoDB, including creating and managing document-based databases. Gain proficiency in building APIs using FastAPI and Python for robust backend systems. Develop dynamic frontends using React, integrating seamlessly with a FastAPI backend. Securely authenticate and authorize users using JSON Web Tokens in your applications. Explore advanced features like integrating AI models and building with Next.js for production-ready development. Author(s) Marko Aleksendrić, Shrey Batra, Rachelle Palmer, and Shubham Ranjan combine their expertise in web development and software engineering in this book. Together, they bring years of professional experience and a passion for teaching developers to create modern web applications effectively using cutting-edge tools. Who is it for? Intermediate web developers who possess foundational JavaScript and Python skills are the ideal audience for this book. If you want to advance your skills by mastering modern web application development with the FARM stack, this book will guide you comprehensively. With practical, real-world examples, it is designed for developers aiming to build production-grade applications.

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley, former Google healthcare professionals, guide you through the transformative potential of large language models (LLMs) and generative AI in healthcare. From personalized patient care and clinical decision support to drug discovery and public health applications, this comprehensive exploration covers real-world uses and future possibilities of LLMs and generative AI in healthcare. With this book, you will: Understand the promise and challenges of LLMs in healthcare Learn the inner workings of LLMs and generative AI Explore automation of healthcare use cases for improved operations and patient care using LLMs Dive into patient experiences and clinical decision-making using generative AI Review future applications in pharmaceutical R&D, public health, and genomics Understand ethical considerations and responsible development of LLMs in healthcare "The authors illustrate generative's impact on drug development, presenting real-world examples of its ability to accelerate processes and improve outcomes across the pharmaceutical industry." --Harsh Pandey, VP, Data Analytics & Business Insights, Medidata-Dassault Kerrie Holley is a retired Google tech executive, IBM Fellow, and VP/CTO at Cisco. Holley's extensive experience includes serving as the first Technology Fellow at United Health Group (UHG), Optum, where he focused on advancing and applying AI, deep learning, and natural language processing in healthcare. Manish Mathur brings over two decades of expertise at the crossroads of healthcare and technology. A former executive at Google and Johnson & Johnson, he now serves as an independent consultant and advisor. He guides payers, providers, and life sciences companies in crafting cutting-edge healthcare solutions.

Microsoft Power BI Performance Best Practices is your comprehensive guide to designing, optimizing, and scaling Power BI solutions. By understanding data modeling, DAX formulation, and report design, you will be able to enhance the efficiency and performance of your Power BI systems, ensuring that they meet the demands of modern data-driven decision-making. What this Book will help me do Understand and apply techniques for high-efficient data modeling to enhance Power BI performance and manage large datasets. Identify and resolve performance bottlenecks in Power BI reports and dashboards using tools like DAX Studio and VertiPaq Analyzer. Implement governance and monitoring strategies for Power BI performance to ensure robust and scalable systems. Gain expertise in leveraging Power BI Premium and Azure for handling larger scale data and integrations. Adopt best practices for designing, implementing row-level security, and optimizing queries for efficient operations. Author(s) Thomas LeBlanc and Bhavik Merchant are experienced professionals in the field of Business Intelligence and Power BI. Thomas brings over 30 years of IT expertise as a Business Intelligence Architect, ensuring practical and effective solutions for BI challenges. Bhavik is a recognized expert in enterprise-grade Power BI implementation. Together, they share actionable insights and strategies to make Power BI solutions advanced and highly performant. Who is it for? This book is ideal for data analysts, BI developers, and data professionals seeking to elevate their Power BI implementations. If you are proficient with the essentials of Power BI and aim to excel in optimizing its performance and scalability, this book will guide you to achieve those goals efficiently and effectively.

Dive into the world of data analysis with the Polars Cookbook. This book, ideal for data professionals, covers practical recipes to manipulate, transform, and analyze data using the Python Polars library. You'll learn both the fundamentals and advanced techniques to build efficient and scalable data workflows. What this Book will help me do Master the basics of Python Polars including installation and setup. Perform complex data manipulation like pivoting, grouping, and joining. Handle large-scale time series data for accurate analysis. Understand data integration with libraries like pandas and numpy. Optimize workflows for both on-premise and cloud environments. Author(s) Yuki Kakegawa is an experienced data analytics consultant who has collaborated with companies such as Microsoft and Stanford Health Care. His passion for data led him to create this detailed guide on Polars. His expertise ensures you gain real-world, actionable insights from every chapter. Who is it for? This book is perfect for data analysts, engineers, and scientists eager to enhance their efficiency with Python Polars. If you are familiar with Python and tools like pandas but are new to Polars, this book will upskill you. Whether handling big data or optimizing code for performance, the Polars Cookbook has the guidance you need to succeed.

Dive into DuckDB and start processing gigabytes of data with ease—all with no data warehouse. DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you’ll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you’ll learn everything you need to get the most out of DuckDB—all through hands-on examples. Open up DuckDB in Action and learn how to: Read and process data from CSV, JSON and Parquet sources both locally and remote Write analytical SQL queries, including aggregations, common table expressions, window functions, special types of joins, and pivot tables Use DuckDB from Python, both with SQL and its "Relational"-API, interacting with databases but also data frames Prepare, ingest and query large datasets Build cloud data pipelines Extend DuckDB with custom functionality Pragmatic and comprehensive, DuckDB in Action introduces the DuckDB database and shows you how to use it to solve common data workflow problems. You won’t need to read through pages of documentation—you’ll learn as you work. Get to grips with DuckDB's unique SQL dialect, learning to seamlessly load, prepare, and analyze data using SQL queries. Extend DuckDB with both Python and built-in tools such as MotherDuck, and gain practical insights into building robust and automated data pipelines. About the Technology DuckDB makes data analytics fast and fun! You don’t need to set up a Spark or run a cloud data warehouse just to process a few hundred gigabytes of data. DuckDB is easily embeddable in any data analytics application, runs on a laptop, and processes data from almost any source, including JSON, CSV, Parquet, SQLite and Postgres. About the Book DuckDB in Action guides you example-by-example from setup, through your first SQL query, to advanced topics like building data pipelines and embedding DuckDB as a local data store for a Streamlit web app. You’ll explore DuckDB’s handy SQL extensions, get to grips with aggregation, analysis, and data without persistence, and use Python to customize DuckDB. A hands-on project accompanies each new topic, so you can see DuckDB in action. What's Inside Prepare, ingest and query large datasets Build cloud data pipelines Extend DuckDB with custom functionality Fast-paced SQL recap: From simple queries to advanced analytics About the Reader For data pros comfortable with Python and CLI tools. About the Authors Mark Needham is a blogger and video creator at @‌LearnDataWithMark. Michael Hunger leads product innovation for the Neo4j graph database. Michael Simons is a Java Champion, author, and Engineer at Neo4j. Quotes I use DuckDB every day, and I still learned a lot about how DuckDB makes things that are hard in most databases easy! - Jordan Tigani, Founder, MotherDuck An excellent resource! Unlocks possibilities for storing, processing, analyzing, and summarizing data at the edge using DuckDB. - Pramod Sadalage, Director, Thoughtworks Clear and accessible. A comprehensive resource for harnessing the power of DuckDB for both novices and experienced professionals. - Qiusheng Wu, Associate Professor, University of Tennessee Excellent! The book all we ducklings have been waiting for! - Gunnar Morling, Decodable

Real-time applications are becoming the norm today. But building a model that works properly requires real-time data from the source, in-flight stream processing, and low latency serving of its analytics. With this practical book, data engineers, data architects, and data analysts will learn how to use streaming databases to build real-time solutions. Authors Hubert Dulay and Ralph M. Debusmann take you through streaming database fundamentals, including how these databases reduce infrastructure for real-time solutions. You'll learn the difference between streaming databases, stream processing, and real-time online analytical processing (OLAP) databases. And you'll discover when to use push queries versus pull queries, and how to serve synchronous and asynchronous data emanating from streaming databases. This guide helps you: Explore stream processing and streaming databases Learn how to build a real-time solution with a streaming database Understand how to construct materialized views from any number of streams Learn how to serve synchronous and asynchronous data Get started building low-complexity streaming solutions with minimal setup

Graph Based Multimedia Analysis applies concepts from graph theory to the problems of analyzing overabundant video data. Video data can be quite diverse: exocentric (captured by a standard camera) or egocentric (captured by a wearable device like Google Glass); of various durations (ranging from a few seconds to several hours); and could be from a single source or multiple sources. Efficient extraction of important information from such a large class of diverse video data can be overwhelming. The book, with its rich repertoire of theoretically elegant solutions, from graph theory in conjunction with deep learning, constrained optimization, and game theory, empowers the audience to achieve tasks like obtaining concise yet useful summaries and precisely recognizing single as well as multiple actions in a computationally efficient manner. The book provides a unique treatise on topics like egocentric video analysis and scalable video processing. Addresses a number of challenging state-of-the-art problems in multimedia analysis like summarization, co-summarization, and action recognition Handles a wide class of video with different genres, durations, and numbers Applies a class of theoretically rich algorithms from the discipline of graph theory, in conjunction with deep learning, constrained optimization and game theory Includes thorough complexity analyses of the proposed solutions, and an appendix containing implementable source codes

The book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.

Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.

Analyzing Baseball Data with R Third Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps.

This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. There are seven new chapters, and each of them focuses on a separate real-world problem to which deep learning is applied.

Discover how to harness the full potential of Microsoft Power BI in "Microsoft Power BI Cookbook". Through its recipe-based structure, this book offers step-by-step guidance on mastering data integration, crafting impactful visualizations, and utilizing Power BI's latest features like Hybrid tables and enhanced scorecards. This edition equips you with the skills to transform raw data into actionable insights for your organization. What this Book will help me do Turn business data into actionable insights by utilizing Microsoft Data Fabric effectively. Create engaging and clear visualizations through Hybrid tables and advanced reporting techniques. Gain competence in managing real-time data accuracy and implementing dynamic analytics in Power BI. Ensure robust data compliance and governance integrated seamlessly into business reporting workflows. Leverage cutting-edge Power BI features to prepare for emerging trends in data intelligence. Author(s) Greg Deckler and None Powell, both esteemed professionals in the Power BI and data analytics domain, co-author this comprehensive guide. With decades of experience, they bring vast knowledge and practical skills to this work, presenting it in a structured and approachable manner. Both are dedicated to empowering learners of all levels to excel with Power BI. Who is it for? This book is ideal for professionals like data analysts, business intelligence developers, and IT specialists focused on reporting. It suits readers with a basic familiarity with Power BI, looking to deepen their understanding. If you aim to stay current with Power BI's most modern practices and features, this book will help you achieve that. Additionally, it supports those aiming to enhance business decision-making through better visualizations and advanced analysis.

The "MuleSoft Platform Architect's Guide" is your essential resource for mastering API-driven solutions using MuleSoft Anypoint Platform. This book enables you to design, deploy, and operate scalable, secure, and high-performance API architectures in enterprise settings while preparing for MuleSoft Platform Architect certification. What this Book will help me do Design robust API integration solutions using MuleSoft Anypoint Platform. Successfully deploy applications to CloudHub and Runtime Fabric environments. Monitor and operate APIs with advanced management tools. Implement scalable solutions aligned with business outcomes. Prepare confidently for the MuleSoft Platform Architect certification. Author(s) Jitendra Bafna is a Senior Solution Architect with years of experience optimizing MuleSoft implementations. Jim Andrews, a MuleSoft Evangelist, has dedicated his career to guiding others in achieving enterprise-ready API solutions. Together, they share practical knowledge, step-by-step guidance, and expertise in API and integration mastery. Who is it for? This book is perfect for IT architects and senior developers experienced in API development, especially those familiar with MuleSoft. It's tailored for professionals aiming to master Anypoint Platform or pursue MuleSoft Platform Architect certification. Readers should have basic experience with integration platforms and a willingness to explore advanced API design.