Data Visualization with Microsoft Power Bi
The first book that delivers data viz best practices precisely for Power BI This practical guide shows how to quickly create visualizations and build savvy dashboards, with 25 chapters that explore different chart types plus 40 visuals from the AppSource gallery, from default to advanced, all over 400 color pages. Key features: Beautiful examples of charts, along with specific use cases Step-by-step instruction on how to set up visuals in the app Data preparation tips and tricks Quizzes to consolidate the learning material Data Visualization with Microsoft Power BI is suitable for both experienced data analysts and nontechnical professionals in finance, sales, and marketing. Here's what's inside: Part 1: Classic visuals. Discover how to choose charts for basic types of analysis and avoid common mistakes, then learn how to set up interactions and put visuals together on a dashboard. Part 2: Trusted advanced visuals. Explore different options and data requirements for charts and diagrams including waterfall, bullet, Gantt, tornado, funnel, Sankey, and more. Part 3: Risky advanced visuals. Consider eye-catching charts that nonetheless may confuse the average user, examine use cases, and understand how to avoid pitfalls or suggest simpler alternatives. You get "two in one" Data viz best practices, based on know-how cultivated over 15 years in the field of business intelligence Technical expertise along with clear guides and shortcuts derived from 300+ dashboards developed
Fun with Data Analysis and BI
DESCRIPTION Fun with Data Analysis and BI teaches you how to turn raw data into actionable insights using business intelligence tools. It equips you with essential skills to make data-driven decisions and effectively communicate findings.This book is designed to guide you through learning SQL from the ground up. Starting with installation and environment setup, it covers everything from building databases and creating tables to mastering SQL queries. Alongside theoretical concepts, you will engage in hands-on projects that demonstrate practical applications, including market analysis using Python to track stock trends and churn analysis to understand customer behavior. Each chapter concludes with MCQs to test your knowledge. The book also introduces you to Tableau, a powerful tool for creating visualizations without writing code, with step-by-step instructions on how to use it for your data projects.By the end of this book, you will be equipped with the skills to extract valuable insights from complex datasets, visualize data in compelling ways, and make data-driven decisions that positively impact your organization. KEY FEATURES ● In-depth coverage of SQL, Python, ML, and Tableau for all skill levels.● Hands-on projects to transform raw information into valuable data insights.● Practical examples and end-to-end solutions for mastering data science concepts.WHAT YOU WILL LEARN● Install and set up SQL environments, create databases, develop tables, and write effective SQL queries.● Use Python to analyze stock market data, create clusters, and support data-driven decisions.● Apply ML to understand consumer behavior, predict churn, and improve retention.● Design striking data visuals with Tableau, enhancing data presentation skills without coding.● Gain hands-on experience by working on complete projects, from data preparation to final output.WHO THIS BOOK IS FORWhether you are a business analyst, data scientist, or aspiring data professional, this book provides the essential knowledge and practical guidance to excel in the field of data analysis.
The Hidden World of Data Blankets
Introducing Data Blankets: From Ancient Origins to Modern Applications, a comprehensive and insightful exploration of the history, science, ethics, and future trends of data blankets, covering topics such as data collection methods, analysis techniques, privacy considerations, and industry applications, all while delving into the challenges and opportunities that data blankets present in our data-driven society. Discover the fascinating world of data blankets and how they shape our lives.
Blockchain
Demystify the blockchain--and learn how to use it--with this practical guide. Start from the ground up: What is Ethereum? What is Solidity? And how are they used to create smart contracts? Then see how to implement your own blockchain, including configuring a peer-to-peer network, managing miner accounts, and more. Follow step-by-step instructions and detailed code examples to develop smart contracts and dApps. Work with cutting-edge technologies such as Bitcoin, DeFi, NFTs, and more. Welcome to the world of blockchain!In this book, you'll learn about: a. Blockchain Basics You've heard the hype around Bitcoin, NFTs, and crypto mining. But how does the underlying blockchain technology work? Understand the fundamentals of the distributed ledger, and learn how to create and manage your own blockchain. b. Application DevelopmentMaster smart contracts, from programming with Solidity to testing, debugging, deployment, and beyond. Develop decentralized applications (dApps) and expand them into a decentralized autonomous organization (DAO) by implementing a frontend with ether.js. c. Tips from the ExpertsFollow guidance from experienced blockchain programmers. Use commented code examples as templates for your projects to get started building your own blockchain and smart contracts in the real world. Highlights include: 1) Blockchain basics and creation2) Smart contracts and dApps3) Development with Solidity 4) Testing, debugging, and security5) Web APIs6) Peer-to-peer frameworks7) Accounts and balances8) Transaction and block verification9) Gas optimization10) Decentralized finance (DeFi)11) Non-fungible tokens (NFTs)12) Yul and Huff contracts
Introduction to Real-Time Audio Programming Using Sonic Pi and ChucK
This text offers a comprehensive set of lecture notes introducing live audio programming through the use of Sonic Pi and ChucK, two powerful and versatile tools for creating music with code. These notes are intended to act as a course companion, not necessarily a standalone text for self-study unless the reader already has some familiarity with studying programming languages. Students will first develop a solid foundation in Ruby programming before diving into Sonic Pi, where they will explore the basics of sound synthesis, rhythm, melody, harmony, sample manipulation, and effects. Throughout the course, students will learn advanced techniques in live coding, sound design, and generative music, along with integrating external tools like MIDI and OSC for collaborative performances. The latter end of the course will focus on ChucK, where students will further refine their skills in sound synthesis, real-time audio processing, and advanced programming concepts. The course culminates in a final project where students will combine their knowledge of Sonic Pi and ChucK to create and perform a live audio programming piece. Likewise, this text includes a set of appendices containing applied course assignments and supplemental notes on music theory (excluding classical voice leading) - hence, students should be expected to have prerequisite knowledge of introductory music theory and computer programming (e.g., in Python or Ruby, ideally).
Amazon DynamoDB - The Definitive Guide
Harness the potential and scalability of DynamoDB to effortlessly construct resilient, low-latency databasesKey Features: - Explore how DynamoDB works behind the scenes to make the most of its features- Learn how to keep latency and costs minimal even when scaling up- Integrate DynamoDB with other AWS services to create a full data analytics system- Purchase of the print or Kindle book includes a free PDF eBookBook Description: This book is your comprehensive resource to mastering Amazon DynamoDB, the fully managed, serverless NoSQL database service designed for high performance at any scale. Authored by Aman Dhingra, Senior DynamoDB Specialist Solutions Architect at AWS, and Mike Mackay, former Senior NoSQL Specialist Solutions Architect at AWS, this guide draws on their deep expertise to equip you with the knowledge and skills to harness DynamoDB's full potential.This book not only introduces you to DynamoDB's core features and real-world applications but also provides in-depth guidance on transitioning from traditional relational databases to the NoSQL world. You'll learn essential data modeling techniques, such as vertical partitioning, and explore the nuances of DynamoDB's indexing capabilities, capacity modes, and consistency models. The guide also dives into advanced topics like enhanced analytical patterns, implementing caching with DynamoDB Accelerator (DAX), and integrating DynamoDB with other AWS services to optimize your data strategies.Whether you're migrating from a traditional relational database world or seeking to deepen your understanding of NoSQL, this book will help you design, build, and deliver low-latency, high-throughput DynamoDB solutions, driving new levels of efficiency and performance for your applications.What You Will Learn: - Master key-value data modeling in DynamoDB for efficiency- Transition from RDBMS to NoSQL with optimized strategies- Implement read consistency and ACID transactions effectively- Explore vertical partitioning for specific data access patterns- Optimize data retrieval using secondary indexes in DynamoDB- Manage capacity modes, backup strategies, and core components- Enhance DynamoDB with caching, analytics, and global tables- Evaluate and Design your migration strategy to DynamoDBWho this book is for: This book is for database developers looking to expand their knowledge and use of DynamoDB to fully leverage its power and features. A basic understanding of NoSQL databases and familiarity with either Python or Node.js is expected. While hands-on experience with DynamoDB is beneficial, it is not required to follow along with the concepts covered in the book.Table of Contents- DynamoDB in Action- The AWS Management Console and SDKs- NoSQL Workbench for DynamoDB- Simple Key-Value- Moving from a Relational Mindset- Read Consistency, Operations, and Transactions- Vertical Partitioning- Secondary Indexes- Capacity Modes and Table Classes- Request Routers, Storage Nodes, and Other Core Components- Backup, Restore, and More- Streams and TTL- Global Tables- DynamoDB Accelerator (DAX) and Caching with DynamoDB- Enhanced Analytical Patterns- Migrations
The Hidden World of Data Dinosaurs
Book summary: Discover the world of data science, from its evolution to the future of the field, as you explore data foundations, mining and machine learning, advanced analytics, visualization and communication, ethical considerations, and the role of data dinosaurs, all in one comprehensive guide.
PostgreSQL 16 Cookbook, Second Edition
Offering a detailed practical look at PostgreSQL 16's new features, "PostgreSQL 16 Cookbook, Second Edition" equips database administrators and developers to take advantage of the most recent developments. This edition provides in-depth coverage of enhanced logical replication, which now includes the ability to replicate from standby servers. We provide detailed instructions for setting up these advanced replication configurations, allowing you to better distribute workloads and improve data availability. The optimization of concurrent bulk loading capabilities for faster data ingestion is another noteworthy addition. Another standout feature of PostgreSQL 16 is the expanded SQL/JSON syntax, which gives developers more control over JSON data management.Additionally, the book teaches new monitoring capabilities introduced with the pg_stat_io view, which provide insights into I/O operations to help optimize performance. The book goes on to implement performance enhancements such as SIMD acceleration for processing ASCII and JSON strings, as well as the new load balancing feature, load_balance_hosts, which distributes traffic efficiently among multiple servers. The goal of this book is to provide you with the knowledge you need to successfully manage, optimize, and troubleshoot database environments by providing a deep-dive understanding of how to implement and benefit from PostgreSQL 16's latest features.Key LearningsBoost data availability and workload distribution using advanced logical replication techniques.Apply the SIMD acceleration to expedite the processing of ASCII and JSON strings.Make use of improved SQL/JSON syntax to manage complicated JSON data operations.Utilize pg_stat_io for troubleshooting and monitoring I/O operations.Utilize Rust libraries like pgx and rust-postgres for easy integration with PostgreSQL.Distribute workload among numerous PostgreSQL instances by configuring load_balance_hosts.Simplify user role configurations and security with refined privilege management.Utilize pgBackRest and Barman to implement strong backup strategiesTable of ContentPreparing PostgreSQL 16Performing Basic PostgreSQL OperationsPostgreSQL Cloud ProvisioningDatabase Migration to Cloud and PostgreSQLWAL, AutoVacuum & ArchiveLogPartitioning and Sharding StrategiesTroubleshooting Replication, Scalability & High AvailabilityBlob, JSON Query, CAST Operator & ConnectionsAuthentication, Audit & EncryptionImplementing Database Backup StrategiesPerform Database Recovery & Restoration
Bayesian Analysis with Python - Third Edition
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features: - Conduct Bayesian data analysis with step-by-step guidance- Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling- Enhance your learning with best practices through sample problems and practice exercises- Purchase of the print or Kindle book includes a free PDF eBook.Book Description: The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What You Will Learn: - Build probabilistic models using PyMC and Bambi- Analyze and interpret probabilistic models with ArviZ- Acquire the skills to sanity-check models and modify them if necessary- Build better models with prior and posterior predictive checks- Learn the advantages and caveats of hierarchical models- Compare models and choose between alternative ones- Interpret results and apply your knowledge to real-world problems- Explore common models from a unified probabilistic perspective- Apply the Bayesian framework's flexibility for probabilistic thinkingWho this book is for: If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.Table of Contents- Introduction to Deep Learning for Mobile - Mobile Vision: Face Detection using on-device models - Chatbot using Actions on Google - Recognizing Plant Species - Live Captions Generation of Camera Feed - Building Artificial Intelligence Authentication System - Speech/Multimedia Processing: Generating music using AI - Reinforced Neural Network based Chess Engine - Building Image Super-Resolution Application - Road Ahead - Appendix
Hands-On Salesforce Data Cloud
Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data. Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks. This book will help you: Develop a plan to execute a CDP project effectively and efficiently Connect Data Cloud to external data sources and build out a Customer 360 Data Model Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure Use Salesforce Data Cloud capabilities for identity resolution and segmentation Create calculated, streaming, visualization, and predictive insights Use Data Graphs to power Salesforce Einstein capabilities Learn Data Cloud best practices for all phases of the development lifecycle
The Quality of Live Subtitling
In an era where accessibility is a key concern, this book provides a critical examination of live subtitling, which is essential for the Deaf and Hard of Hearing to access audiovisual media, including television.
Experimental IR Meets Multilinguality, Multimodality, and Interaction
The two volume set LNCS 14958 + 14959 constitutes the proceedings of the 15th International Conference of the CLEF Association, CLEF 2024, held in Grenoble, France, during September 9-12, 2024. The proceedings contain 11 conference papers; 6 best of CLEF 2023 Labs' papers, and 14 Lab overview papers accepted from 45 submissions. In addition an overview paper on the CLEF activities in the last 25 years is included. The CLEF conference and labs of the evaluation forum deal with topics in information access from different perspectives, in any modality and language, focusing on experimental information retrieval (IR).
Impact of Digital Engineering on Defense Acquisition and the Supply Chain
This report summarizes the results of an industry survey designed to assess the progress of digital engineering (DE) implementation and to identify (1) implementation challenges and opportunities and (2) possible metrics for tracking DE implementation. Although the benefits of DE might not be immediately apparent in terms of cost savings or schedule reduction, it has the potential to provide significant long-term benefits to defense acquisition.
Practical Lakehouse Architecture
This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
From Data To Decisions
This comprehensive guide demystifies data analytics, providing clear, practical insights into transforming raw data into actionable strategies that drive business success. Whether you're a professional, business leader, or aspiring data scientist, this book equips you with the knowledge and tools to make informed, data-backed decisions. With real-world case studies from finance, healthcare, retail, and manufacturing, Yusuf bridges the gap between traditional decision-making processes and modern data-driven techniques.As the data analytics landscape evolves, this book serves as both a roadmap for current methodologies and a springboard into future developments. Learn how to integrate advanced analytics into your business strategy, navigate ethical considerations, and stay ahead in a data-centric world. "From Data to Decisions" is your essential guide to making data work for you and achieving significant performance improvements through data-driven decision-making.As the data analytics landscape evolves, "From Data to Decisions" serves as both a roadmap for current methodologies and a springboard into future developments. Learn how to integrate advanced analytics into your business strategy, navigate ethical considerations, and stay ahead of the curve in a data-centric world.Unlock the hidden potential of your data and fuel your journey to success with "From Data to Decisions: Driving Performance in the Age of Analytics." Your comprehensive guide to making data work for you is just a click away.
Cluster Analysis: A Primer Using R
Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis.At its core, the book delves deeply into various clustering algorithms, covering partitioning methods, hierarchical methods, and advanced techniques such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with clear, concise explanations, supported by illustrative examples and hands-on implementations in the R programming language -- a popular and powerful tool for data analysis and visualization.Recognizing the importance of cluster validation and evaluation, the book devotes a dedicated chapter to exploring a wide range of internal and external quality criteria, equipping readers with the necessary tools to assess the performance of clustering algorithms. For those eager to stay at the forefront of the field, the book also presents deep learning-based clustering methods, showcasing the remarkable capabilities of neural networks in uncovering hidden structures within complex, high-dimensional data.Whether you are a student seeking to expand your knowledge, a data analyst looking to enhance your toolbox, or a researcher exploring the frontiers of data analysis, this book will provide you with a solid foundation in cluster analysis and empower you to tackle a wide range of data-driven problems.
Grokking Data Structures
Don't be perplexed by data structures! This fun, friendly, and fully illustrated guide makes it easy to learn useful data structures you'll put to work every day. Grokking Data Structures makes it a breeze to learn the most useful day-to-day data structures. You'll follow a steady learning path from absolute basics to advanced concepts, all illustrated with fun examples, engaging industry stories, and hundreds of graphics and cartoons. In Grokking Data Structures you'll learn how to: - Understand the most important and widely used data structures - Identify use cases where data structures make the biggest difference - Pick the best data structure solution for a coding challenge - Understand the tradeoffs of data structures and avoid catastrophes - Implement basic data collections like arrays, linked lists, stacks, and priority queues - Use trees and binary search trees (BSTs) to organize data - Use graphs to model relationships and learn about complex data - Efficiently search by key using hash tables and hashing functions - Reason about time and memory requirements of operations on data structures Grokking Data Structures carefully guides you from the most basic data structures like arrays or linked lists all the way to powerful structures like graphs. It's perfect for beginners, and you won't need anything more than high school math to get started. Each data structure you encounter comes with its own complete Python implementation so you can start experimenting with what you learn right away. Foreword by Daniel Zingaro. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Data structures are vital for shaping and handling your data organization. They're also an important part of most IT job interviews! Whether you're new to data structures or just dusting off what you learned in school, this book will get you up to speed fast with no advanced math, abstract theory, or complicated proofs. About the book Grokking Data Structures introduces common and useful data structures that every developer needs to know. Real-world examples show you how data structures are used in practice, from making your searches faster to handling triage in an emergency room. You'll love the fun cartoons, insightful stories, and useful Python code samples that make data structures come alive. And unlike jargon-laden academic texts, this book is easy-to-read and practical. What's inside - Fast searches using hash tables - Trees and binary search trees (BSTs) to organize data - Use graphs to model complex data - The best data structures for a coding challenge About the reader For readers who know the basics of Python. A perfect companion to Grokking Algorithms! About the author Marcello La Rocca is a research scientist and a full-stack engineer. He has contributed to large-scale web applications and machine learning infrastructure at Twitter, Microsoft, and Apple. The technical editor on this book was Beau Carnes. Table of Contents 1 Introducing data structures: Why you should learn about data structures 2 Static arrays: Building your first data structure 3 Sorted arrays: Searching faster, at a price 4 Big-O notation: A framework for measuring algorithm efficiency 5 Dynamic arrays: Handling dynamically sized datasets 6 Linked lists: A flexible dynamic collection 7 Abstract data types: Designing the simplest container--the bag 8 Stacks: Piling up data before processing it 9 Queues: Keeping information in the same order as it arrives 10 Priority queues and heaps: Handling data according to its priority 11 Binary search trees: A balanced container 12 Dictionaries and hash tables: How to build and use associative arrays 13 Graphs: Learning how to model complex relationships in data
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed conference proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Volume I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Volume II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Volume III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Volume IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Volume V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Privacy in Statistical Databases
​This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2024, held in Antibes Juan-les-Pins, France, during September 25-27, 2024. The 28 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections as follows: Privacy models and concepts; Microdata protection; Statistical table protection; Synthetic data generation methods; Synthetic data generation software; Disclosure risk assessment; Spatial and georeferenced data; Machine learning and privacy; and Case studies.
Document Analysis Systems
This book constitutes the refereed proceedings of the 16th IAPR International Workshop on Document Analysis Systems, DAS 2024, held in Athens, Greece, during August 30-31, 2024. The 27 full papers presented were carefully reviewed and selected from 43 submissions addressing topics like: document analysis and understanding; retrieval and VQA; layout analysis; document classification; OCR correction and NLP; recognition systems; and historical documents.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Podcasting for Dummies
Step up to the mic and unleash your inner host with Podcasting For Dummies Ever wonder what it takes to get your very own podcast up and running? How to get the gear you need, pick a great topic, secure fascinating guests, and assemble it all into a refined and irresistible product? Well wonder no more! Because Podcasting For Dummies has the essential guidance you need to get your brand-new podcast up and running. From selecting the right recording equipment to identifying an audience and pro-level production tips, you'll find all the killer info to help you get started on your next big idea. You'll also get: Software and hardware tips to create and produce a crystal-clear podcast Interview advice, whether you'll be seeing your guests in-person or over Zoom Strategies for choosing the perfect platform, finding sponsors, and advertising and marketing your new creation Pointers for setting up a streaming account and doing live podcasting like an expert With everyone from A-list brands to world-famous celebrities getting in on the podcast craze, it's time you took your turn on the mic. Grab Podcasting For Dummies today and turn up the volume on the practice that's transformed countless amateurs into household names!
Simulation for a Sustainable Future
The two volume set CCIS 2032 and 2033 constitutes the proceedings of the 11th Congress on Simulation for a Sustainable Future, EUROSIM 2023, which was held in Amsterdam, The Netherlands, during July 3-5, 2023. The 47 full papers included in the proceedings were carefully reviewed and selected from 99 submissions. The papers are divided in the following topical sections: ​environmental sustainability; healthcare; production systems; business and industries; logistics and transportation systems; monitor, control, and theoretical systems.
MCQ for Data Science Users
This book intends to provide a collection of various MCQs on data scienceDESCRIPTION This book is a comprehensive manual created to assess and improve your comprehension of many concepts and methodologies in data science. The course encompasses a broad spectrum of subjects, such as data preprocessing, Machine Learning techniques, data visualization, statistical analysis, and additional topics. Every chapter is organized with a series of multiple-choice questions that test your understanding and allow you to evaluate your expertise in the subject. The book's objective is to offer a pragmatic and captivating approach for readers to enhance their proficiency in data science through practical exercises. The book provides an extensive examination of several subjects in data science, encompassing data preprocessing, statistical analysis, Machine Learning techniques, data visualization, and additional areas. This extensive knowledge helps readers acquire a full and all-encompassing comprehension of the subject matter. The chapters in this book adhere to a structured framework, which includes multiple-choice questions that enable readers to assess their understanding and grasp of the content. KEY FEATURES ● Comprehensive coverage of data science concepts and features.● Multiple-choice questions to test and assess knowledge effectively.● Over 5000 multiple-choice questions for practice.WHAT YOU WILL LEARN● Mastering data science concepts through multiple-choice questions.● Strengthening problem-solving skills by practicing diverse scenarios.● Interpreting the results of data analyses and Machine Learning models effectively.● Evaluating the performance of different Machine Learning models using metrics.● Developing critical thinking skills to assess the suitability of various data science approaches.● Preparing for exams, interviews, and quizzes, etc.WHO THIS BOOK IS FORThis data science MCQ book is perfect for anyone looking to test and improve their knowledge of data through multiple-choice questions.
Autodesk Vault 2025 for Inventor and AutoCAD Users
Autodesk(R) Vault Professional 2025 for Inventor(R) and AutoCAD(R) Users introduces the Autodesk Vault 2025 software (Basic and Professional) to users. The guide is intended for Autodesk CAD users who need to access their design files from the Autodesk Vault software. It provides an introduction to the Autodesk Vault software and focuses specifically on features available to end-users for working with and managing Inventor and AutoCAD designs.You can use the Autodesk Vault 2025 software and the desired Autodesk 2025 CAD software (such as Inventor or AutoCAD) to complete the practices in this guide. Note that this guide does not cover administrative functionality. Hands-on practices are included to reinforce how to manage the design workflow process using the Autodesk Vault software. Included with this guide is a training Vault that can be used alongside a production Vault, to ensure that both Vaults can be accessed from the Autodesk Vault software. Topics CoveredIntroduction to Autodesk Vault featuresUsing Autodesk Vault ClientWorking with non-CAD filesWorking with Inventor filesWorking with AutoCAD filesSearching the VaultData management and reusing design dataItems and bill of materials managementChange managementCustomizing the user interface PrerequisitesAccess to the 2025.0 version of the software, to ensure compatibility with this guide. Future software updates that are released by Autodesk may include changes that are not reflected in this guide. The practices and files included with this guide might not be compatible with prior versions (e.g., 2024).Basic working knowledge of the Autodesk CAD software such as Inventor and AutoCAD.
Cognitive Science, Computational Intelligence, and Data Analytics
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems. The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.
Machine Learning-Based Prediction of Missing Parts for Assembly
Manufacturing companies face challenges in managing increasing process complexity while meeting demands for on-time delivery, particularly evident during critical processes like assembly. The early identification of potential missing parts at the beginning assembly emerges as a crucial strategy to uphold delivery commitments. This book embarks on developing machine learning-based prediction models to tackle this challenge. Through a systemic literature review, deficiencies in current predictive methodologies are highlighted, notably the underutilization of material data and a late prediction capability within the procurement process. Through case studies within the machine industry a significant influence of material data on the quality of models predicting missing parts from in-house production was verified. Further, a model for predicting delivery delays in the purchasing process was implemented, which makes it possible to predict potential missing parts from suppliers at the time of ordering. These advancements serve as indispensable tools for production planners and procurement professionals, empowering them to proactively address material availability challenges for assembly operations.
System Programming Essentials with Go
Go beyond the web, learn system programming with Go, and build efficient, secure applicationsKey Features: - Get to grips with system programming concepts in Go with application examples- Gain expert guidance on essential topics like file operations, process management, and network programming- Learn how to develop modern, functional applications from scratch- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Alex Rios, a seasoned Go developer and active community builder, shares his 15 years of expertise in designing large-scale systems through this book. It masterfully cuts through complexity, enabling you to build efficient and secure applications with Go's streamlined syntax and powerful concurrency features.In this book, you'll learn how Go, unlike traditional system programming languages (C/C++), lets you focus on the problem by prioritizing readability and elevating developer experience with features like automatic garbage collection and built-in concurrency primitives, which remove the burden of low-level memory management and intricate synchronization.Through hands-on projects, you'll master core concepts like file I/O, process management, and inter-process communication to automate tasks and interact with your system efficiently. You'll delve into network programming in Go, equipping yourself with the skills to build robust, distributed applications. This book goes beyond the basics by exploring modern practices like logging and tracing for comprehensive application monitoring, and advance to distributed system design using Go to prepare you to tackle complex architectures.By the end of this book, you'll emerge as a confident Go system programmer, ready to craft high-performance, secure applications for the modern world.What You Will Learn: - Understand the fundamentals of system programming using Go- Grasp the concepts of goroutines, channels, data races, and managing concurrency in Go- Manage file operations and inter-process communication (IPC)- Handle USB drives and Bluetooth devices and monitor peripheral events for hardware automation- Familiarize yourself with the basics of network programming and its application in Go- Implement logging, tracing, and other telemetry practices- Construct distributed cache and approach distributed systems using GoWho this book is for: This book is for software engineers looking to expand their understanding of system programming concepts. Professionals with a coding foundation seeking profound knowledge of system-level operations will also greatly benefit. Additionally, individuals interested in advancing their system programming skills, whether experienced developers or those transitioning to the field, will find this book indispensable.Table of Contents- Why Go?- Refreshing Concurrency and Parallelism- Understanding System Calls- File and Directory Operations- Working with System Events- Understanding Pipes in Inter-Process Communication- Hardware Automation- Memory Management- Analysing Performance- Networking- Telemetry- Distributing Your Apps- Capstone Project - Distributed Cache- Effective Coding Practices- Stay Sharp with System Programming
Elastic Stack 8.x Cookbook
Unlock the full potential of Elastic Stack for search, analytics, security, and observability and manage substantial data workloads in both on-premise and cloud environmentsKey Features: - Explore the diverse capabilities of the Elastic Stack through a comprehensive set of recipes- Build search applications, analyze your data, and observe cloud-native applications- Harness powerful machine learning and AI features to create data science and search applications- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Learn how to make the most of the Elastic Stack (ELK Stack) products-including Elasticsearch, Kibana, Elastic Agent, and Logstash-to take data reliably and securely from any source, in any format, and then search, analyze, and visualize it in real-time. This cookbook takes a practical approach to unlocking the full potential of Elastic Stack through detailed recipes step by step.Starting with installing and ingesting data using Elastic Agent and Beats, this book guides you through data transformation and enrichment with various Elastic components and explores the latest advancements in search applications, including semantic search and Generative AI. You'll then visualize and explore your data and create dashboards using Kibana. As you progress, you'll advance your skills with machine learning for data science, get to grips with natural language processing, and discover the power of vector search. The book covers Elastic Observability use cases for log, infrastructure, and synthetics monitoring, along with essential strategies for securing the Elastic Stack. Finally, you'll gain expertise in Elastic Stack operations to effectively monitor and manage your system.What You Will Learn: - Discover techniques for collecting data from diverse sources- Visualize data and create dashboards using Kibana to extract business insights- Explore machine learning, vector search, and AI capabilities of Elastic Stack- Handle data transformation and data formatting- Build search solutions from the ingested data- Leverage data science tools for in-depth data exploration- Monitor and manage your system with Elastic StackWho this book is for: This book is for Elastic Stack users, developers, observability practitioners, and data professionals ranging from beginner to expert level. If you're a developer, you'll benefit from the easy-to-follow recipes for using APIs and features to build powerful applications, and if you're an observability practitioner, this book will help you with use cases covering APM, Kubernetes, and cloud monitoring. For data engineers and AI enthusiasts, the book covers dedicated recipes on vector search and machine learning. No prior knowledge of the Elastic Stack is required.Table of Content- Getting Started - Installing the Elastic Stack- Ingesting General Content Data- Building Search Applications- Timestamped Data Ingestion- Transform Data- Visualize and Explore Data- Alerting and Anomaly Detection- Advanced Data Analysis and Processing- Vector Search and Generative AI Integration- Elastic Observability Solution- Managing Access Control- Elastic Stack Operation- Elastic Stack Monitoring
Large Language Models: A Deep Dive
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs--their intricate architecture, underlying algorithms, and ethical considerations--require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. Key Features: Over 100 techniques and state-of-the-art methods, including pre-training, prompt-based tuning, instruction tuning, parameter-efficient and compute-efficient fine-tuning, end-user prompt engineering, and building and optimizing Retrieval-Augmented Generation systems, along with strategies for aligning LLMs with human values using reinforcement learning Over 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applications Over 50 strategies to address key ethical issues such as hallucination, toxicity, bias, fairness, and privacy. Gain comprehensive methods for measuring, evaluating, and mitigating these challenges to ensure responsible LLM deployment Over 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycle Nine detailed tutorials that guide readers through pre-training, fine- tuning, alignment tuning, bias mitigation, multimodal training, and deploying large language models using tools and libraries compatible with Google Colab, ensuring practical application of theoretical concepts Over 100 practical tips for data scientists and practitioners, offering implementation details, tricks, and tools to successfully navigate the LLM life- cycle and accomplish tasks efficiently
Enterprise Intelligence
Harness the Power of BI and AI-Utilize highly curated BI data, an enterprise knowledge graph, and advanced AI to build a resilient and intelligent enterprise capable of making innovative decisions.In the unprecedently evolving landscape of technology and business, the terms Business Intelligence (BI) and Artificial Intelligence (AI) represent different facets of "intelligence." However, when combined, they create a powerful synergy that transforms enterprises into dynamic, highly adaptive entities capable of thriving in an ever-changing ecosystem.This book is the first in a series designed to guide corporations from lumbering entities to becoming agile, high-performing organisms. By integrating BI structures into an Enterprise Knowledge Graph (EKG), businesses can develop a central nervous system more on par with those of living organisms, to enhance decision-making and performance.The main topics covered include: The Sudden Leap Forward: Understand the problem we face with the sudden advent of high-quality large language models (LLMs) and how they bridge the gap between human and machine intelligence.The Intelligence of a Business: Explore the desires, fears, and competitive strategies of enterprises, and the need for an expansive field of vision and a central nervous system akin to living organisms.Knowledge Graphs and LLMs: Delve into the components of the EKG, including a Knowledge Graph (KG) authored by subject matter experts, a Data Catalog (DC) that organizes metadata, and BI-derived structures like the Insight Space Graph (ISG) and Tuple Correlation Web (TCW).Building the Corporate Brain: Learn how to capture the insights and patterns from BI analysts' activities across the enterprise, creating a single integrated source of insights that functions like a human brain.Architecture and Implementation: Gain practical guidance on the architecture of the EKG, BI-charged components, and special patterns for implementation to solve complex business problems.With the advent of highly capable and accessible AI, the pieces needed to build an integrated enterprise "brain" are now within reach. This book provides the essential knowledge and tools to harness BI and AI, transforming your business into a thriving, intelligent organism ready to navigate the complexities of the modern world.
Getting Started with DuckDB
Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL databaseKey Features- Use DuckDB to rapidly load, transform, and query data across a range of sources and formats- Gain practical experience using SQL, Python, and R to effectively analyze data- Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB's versatile capabilities- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionDuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.What you will learn- Understand the properties and applications of a columnar in-process database- Use SQL to load, transform, and query a range of data formats- Discover DuckDB's rich extensions and learn how to apply them- Use nested data types to model semi-structured data and extract and model JSON data- Integrate DuckDB into your Python and R analytical workflows- Effectively leverage DuckDB's convenient SQL enhancements- Explore the wider ecosystem and pathways for building DuckDB-powered data applicationsWho this book is forIf you're interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R.Table of Contents- An Introduction to DuckDB- Loading Data into DuckDB- Data Manipulation with DuckDB- DuckDB Operations and Performance- DuckDB Extensions- Semi-Structured Data Manipulation- Setting up the DuckDB Python Client- Exploring DuckDB's Python API- Exploring DuckDB's R API- Using DuckDB Effectively- Hands-On Exploratory Data Analysis with DuckDB- DuckDB - The Wider Pond
Data and Decision Sciences - Recent Advances and Applications
This book provides an overview of Data and Decision Sciences (DDS) and recent advances and applications in space-based systems and business, medical, and agriculture processes, decision optimization modeling, and cognitive decision-making. Written by experts, this volume is organized into four sections and seven chapters. It is a valuable resource for educators, engineers, scientists, and researchers in the field of DDS.
Anomaly Detection - Recent Advances, AI and ML Perspectives and Applications
This book discusses and addresses anomaly detection in the context of artificial intelligence and machine learning advancements. Building on the existing literature, this thorough and timely work is an invaluable resource. It highlights various problems, offers workable solutions to those problems, and allows academic and professional researchers and practitioners to engage in new technologies linked to anomaly detection. This book demystifies the challenges and presents solutions for detecting and understanding network anomalies. Whether you are a seasoned network professional or an enthusiast keen on cyber security, this volume promises insights that will fortify our connected futures. Join us in navigating the complexities of modern networks and championing a safer, more transparent digital era.
Data Governance Handbook
Build an actionable, business value driven case for data governance to obtain executive support and implement with excellenceKey FeaturesDevelop a solid foundation in data governance and increase your confidence in data solutionsAlign data governance solutions with measurable business results and apply practical knowledge from real-world projectsLearn from a three-time chief data officer who has worked in leading Fortune 500 companiesPurchase of the print or Kindle book includes a free PDF eBookBook Description2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it's their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls.If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes.By the end, you'll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders.What you will learnComprehend data governance from ideation to delivery and beyondPosition data governance to obtain executive buy-inLaunch a governance program at scale with a measurable impactUnderstand real-world use cases to drive swift and effective actionObtain support for data governance-led digital transformationLaunch your data governance program with confidenceWho this book is forChief data officers, data governance leaders, data stewards, and engineers who want to understand the business value of their work, and IT professionals seeking further understanding of data management, will find this book useful. You need a basic understanding of working with data, business needs, and how to meet those needs with data solutions. Prior coding experience or skills in selling data solutions to executives are not required.
Python Data Cleaning Cookbook - Second Edition
Learn the intricacies of data description, issue identification, and practical problem-solving, armed with essential techniques and expert tips.Key Features: - Get to grips with new techniques for data preprocessing and cleaning for machine learning and NLP models- Use new and updated AI tools and techniques for data cleaning tasks- Clean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AIBook Description: Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook will show you tools and techniques for cleaning and handling data with Python for better outcomes.Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. The current edition emphasizes advanced techniques like machine learning and AI-specific approaches and tools to data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI and NLP models You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.What You Will Learn: - Using OpenAI tools for various data cleaning tasks- Produce summaries of the attributes of datasets, columns, and rows- Anticipating Data Cleaning Issues when Importing Tabular Data into Pandas- Apply validation techniques for imported tabular data- Improve your productivity in Python pandas by using method chaining- Recognize and resolve common issues like dates and IDs- Set up indexes to streamline data issue identification- Use data cleaning to prepare your data for ML and AI modelsWho this book is for: This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data with practical examples.Working knowledge of Python programming is all you need to get the most out of the book.
Mastering Power BI
Take a deep dive into the dynamic world of Power BI!DESCRIPTION Mastering Power BI covers the entire Power BI implementation process. The readers will be able to understand all the concepts covered in this book, from data modeling to creating powerful visualizations.This book begins with concepts and terminology such as the star-schema, dimensions, and facts. It explains multi-table dataset and demonstrates how to load these tables into Power BI. It shows how to load stored data in various formats and create relationships. Readers will also learn more about Data Analysis Expressions (DAX). This book is a must for developers to learn how to extend the usability of Power BI, to explore meaningful and hidden data insights. Throughout the book, you keep on learning about the concepts, techniques, and expert practices on loading and shaping data, visualization design, and security implementation.The second edition of Mastering Power BI book adheres to the first edition in terms of providing the basics of business intelligence and Power BI; however, it introduces new concepts and features in terms of data transformation, data profiling, custom hierarchies, AI visuals, and many more. WHAT YOU WILL LEARN● Learn about Business Intelligence (BI) concepts and their contribution in business analytics.● Learn to connect, load, and transform data from disparate data sources.● Create and execute powerful DAX calculations.● Design various visualizations to prepare insightful reports and dashboards. WHO THIS BOOK IS FORThis book is for anyone interested in learning how to use Power BI desktop or starting a career in business intelligence and analytics. While it covers all the fundamentals, it is recommended that the reader be familiar with MS Excel and database concepts.
Microservices Design Patterns with Java
Java microservices: The ultimate pattern guideDESCRIPTION Microservices, a popular software architecture style, breaks down applications into small, independent services built with Java, a versatile and widely used programming language. This book serves as a roadmap for mastering design patterns that solve common problems encountered during microservices development in Java.Start with microservices setup for team success. Discover various architectural styles and communication approaches for seamless service interaction. Learn effective data management within microservices. Acquire skills for handling unforeseen scenarios in transactions and crafting secure APIs for user service access. Lastly, grasp crucial monitoring, testing, and deployment practices to identify and address issues, ensuring smooth production deployment."Microservices Design Patterns with Java" positions itself as an indispensable tool in the arsenal of today's software professionals. It not only aids in navigating the complexities of microservices architecture but also enhances the reader's ability to deliver robust, high-quality software solutions efficiently.WHAT YOU WILL LEARN● Architect scalable, resilient microservices using Java-based design patterns.● Implement efficient communication and data management strategies within microservices.● Design secure, robust external APIs for microservices integration and interaction.● Monitor and maintain microservices with advanced logging, tracing, and health checks.● Deploy microservices with Docker, Kubernetes, and serverless platforms effectively.● Automate CI/CD pipelines for microservices for streamlined development and deployment.WHO THIS BOOK IS FORThis book is for seasoned microservices developers seeking to expand their repertoire of design patterns and practices, as well as for newcomers looking for comprehensive guidance on patterns and practices throughout the entire development lifecycle. It is tailored for architects, developers, team leads, and DevOps engineers.
Fundamentals of Analytics Engineering
Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineeringKey Features- Discover how analytics engineering aligns with your organization's data strategy- Access insights shared by a team of seven industry experts- Tackle common analytics engineering problems faced by modern businesses- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer.After conquering data ingestion and techniques for data quality and scalability, you'll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You'll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You'll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance.By the end of this book, you'll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn- Design and implement data pipelines from ingestion to serving data- Explore best practices for data modeling and schema design- Scale data processing with cloud based analytics platforms and tools- Understand the principles of data quality management and data governance- Streamline code base with best practices like collaborative coding, version control, reviews and standards- Automate and orchestrate data pipelines- Drive business adoption with effective scoping and prioritization of analytics use casesWho this book is forThis book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.Table of Contents- What is Analytics Engineering?- The Modern Data Stack- Data Ingestion- Data Warehouses- Data Modeling- Data Transformation- Serving Data- Hands-on: Building a Data Platform- Data Quality & Observability- Writing Code in a Team- Writing Robust Pipelines- Gathering Business Requirements- Documenting Business Logic- Data Governance
Predictive Analytics for the Modern Enterprise
The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow
Knowledge-Augmented Methods for Natural Language Processing
Over the last few years, natural language processing has seen remarkable progress due to the emergence of larger-scale models, better training techniques, and greater availability of data. Examples of these advancements include GPT-4, ChatGPT, and other pre-trained language models. These models are capable of characterizing linguistic patterns and generating context-aware representations, resulting in high-quality output. However, these models rely solely on input-output pairs during training and, therefore, struggle to incorporate external world knowledge, such as named entities, their relations, common sense, and domain-specific content. Incorporating knowledge into the training and inference of language models is critical to their ability to represent language accurately. Additionally, knowledge is essential in achieving higher levels of intelligence that cannot be attained through statistical learning of input text patterns alone. In this book, we will review recent developmentsin the field of natural language processing, specifically focusing on the role of knowledge in language representation. We will examine how pre-trained language models like GPT-4 and ChatGPT are limited in their ability to capture external world knowledge and explore various approaches to incorporate knowledge into language models. Additionally, we will discuss the significance of knowledge in enabling higher levels of intelligence that go beyond statistical learning on input text patterns. Overall, this survey aims to provide insights into the importance of knowledge in natural language processing and highlight recent advances in this field.
Data Science for Business
Data Science for Business: A practical and hands-on guide that empowers businesses to solve real-world challenges and create value through data science. Learn how to collect, analyze, and visualize data, apply machine learning and statistics, and deploy solutions. Includes case studies, exercises, and code examples.
Artificial Intelligence
Artificial Intelligence (AI) is already present in our daily routines, and in the future, we will encounter it in almost every aspect of life - from analyzing X-rays for medical diagnosis, driving autonomous cars, maintaining complex machinery, to drafting essays on environmental problems and drawing imaginative pictures. The potentials of AI are enormous, while at the same time many myths, uncertainties and challenges circulate that need to be tackled. The English translation of the book "K羹nstliche Intelligenz - Was steckt hinter der Technologie der Zukunft?" originally published in German (Springer Vieweg, 2020), this book is addressed to the general public, from interested citizens to corporate executives who want to develop a better and deeper understanding of AI technologies and assess their consequences. Mathematical basics, terminology, and methods are explained in understandable language. Adaptations to different media such as images, text, and speech and the corresponding generative models are introduced. A concluding discussion of opportunities and challenges helps readers evaluate new developments, demystify them, and assess their relevance for the future.
