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.
Finding Data Patterns in the Noise
Data science is often described as the intersection of statistics, computer science, and domain expertise. It is a multidisciplinary field that harnesses the power of data to uncover hidden patterns, make predictions, and inform critical decision-making processes. In an era where data is generated at an unprecedented rate and scale, the role of data scientists has become increasingly critical. They are the detectives of the digital age, using their analytical skills and technical expertise to turn raw data into actionable insights that can drive significant value.The primary purpose of this book is to demystify the complex world of data science and provide a comprehensive guide for those looking to enter the field or expand their existing knowledge. We will begin by exploring the basics of data science, including key concepts and the fundamental importance of recognizing and understanding data patterns. From there, we will journey through the various stages of a typical data science project, from data collection and cleaning to exploratory analysis and model building.
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
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.
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
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
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 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.
Data Governance Handbook
Build an actionable, business value driven case for data governance to obtain executive support and implement with excellenceKey Features- Develop a solid foundation in data governance and increase your confidence in data solutions- Align data governance solutions with measurable business results and apply practical knowledge from real-world projects- Learn from a three-time chief data officer who has worked in leading Fortune 500 companies- Purchase 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 learn- Comprehend data governance from ideation to delivery and beyond- Position data governance to obtain executive buy-in- Launch a governance program at scale with a measurable impact- Understand real-world use cases to drive swift and effective action- Obtain support for data governance-led digital transformation- Launch 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.Table of Contents- What is Data Governance?- How to Build a Coalition of Advocates- Building a High Performing Team- Baseline the Organisation- Define Success & Align on Outcomes- Metadata Management- Data Lineage- Data Quality- Data Architecture- Primary Data Management- Data Operations- Launch Powerfully- Delivering Quick Wins with Impact- Data Automation For Impact and More Powerful Results- Adoption that Drives Business Success- Delivering Trusted Results With Outcomes that Matter- Case Study - Large Highly Regulated Entity
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.
Databricks Certified Associate Developer for Apache Spark Using Python
Learn the concepts and exercises needed to get certified as a Databricks Associate Developer for Apache Spark 3.0 and validate your skills as a Spark expert with an industry-recognized credential Key Features- Understand the fundamentals of Apache Spark to help you design robust and fast Spark applications- Delve into various data manipulation components for each phase of your data engineering project- Prepare for the certification exam with sample questions and mock exams, and get closer to your goal- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionWith extensive data being collected every second, computing power cannot keep up with this pace of rapid growth. To make use of all the data, Spark has become a de facto standard for big data processing. Migrating data processing to Spark will not only help you save resources that will allow you to focus on your business, but also enable you to modernize your workloads by leveraging the capabilities of Spark and the modern technology stack for creating new business opportunities.This book is a comprehensive guide that lets you explore the core components of Apache Spark, its architecture, and its optimization. You'll become familiar with the Spark dataframe API and its components needed for data manipulation. Next, you'll find out what Spark streaming is and why it's important for modern data stacks, before learning about machine learning in Spark and its different use cases. What's more, you'll discover sample questions at the end of each section along with two mock exams to help you prepare for the certification exam.By the end of this book, you'll know what to expect in the exam and how to pass it with enough understanding of Spark and its tools. You'll also be able to apply this knowledge in a real-world setting and take your skillset to the next level.What you will learn- Create and manipulate SQL queries in Spark- Build complex Spark functions using Spark UDFs- Architect big data apps with Spark fundamentals for optimal design- Apply techniques to manipulate and optimize big data applications- Build real-time or near-real-time applications using Spark Streaming- Work with Apache Spark for machine learning applicationsWho this book is forThis book is for you if you're a professional looking to venture into the world of big data and data engineering, a data professional who wants to endorse your knowledge of Spark, or a student. Although working knowledge of Python is required, no prior Spark knowledge is needed. Additionally, experience with Pyspark will be beneficial. Table of Contents- Overview of Certification Guide and Exam- Understanding Apache Spark and Its Applications- Spark Architecture & Transformations- Spark Datarames and its Operations- Advanced Operations in Spark- SQL Queries in Spark- Structured Streaming in Spark- Machine Learning with Spark ML- Mock Test
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.
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
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
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.
Data Mesh
"Data Mesh: Principles, patterns, architecture, and strategies for data-driven decision making" introduces Data Mesh which is a macro data architecture pattern designed to harmonize governance with flexibility. This book guides readers through the nuances of Data Mesh topologies, explaining how they can be tailored to meet specific organizational needs while balancing central control with domain-specific autonomy. The book delves into the Data Mesh governance framework, which provides a structured approach to manage and control decentralized data assets effectively. It emphasizes the importance of a well-implemented governance structure that ensures data quality, compliance, and access control across various domains. Additionally, the book outlines robust data cataloging and sharing strategies, enabling organizations to improve data discoverability, usage, and interoperability between cross-functional teams. Securing Data Mesh architectures is another critical focus. The text explores comprehensive security strategies that protect data across different layers of the architecture, ensuring data integrity and protecting against breaches.
Open Data for Everybody
What if I told you something that could empower our third sector and activists to enhance their capacity? From gathering evidence for funding tenders to campaigning for crucial social issues and much more? It's called open data, yet many in social action remain unaware of it. Primarily shaped by corporate entities, open data seems tailored only for technologists, alienating the third sector. But in reality, it's a powerful tool for social change, bolstering civil society, and creating resilient communities.This book argues a simple point: if open data and the digital aspects that support it aren't accessible to all, then what is the point of it? In an age where technology should be seen as a fundamental human right, it's time to rethink outreach. Deeply rooted in grassroots social activism, this book explores a journey that led to collaborations with governments globally, based on real hands-on work, aiming to democratize open data. Through narrative storytelling, we share insights, best practices, procedures, and community-driven approaches. Regardless of your skill set or organization size, from grassroots workers to third-sector professionals and government officers, join us to reshape the perception of open data, fostering change in neighborhoods.Open Data for Everybody: Using Open Data for Social Good is a love letter to open data's transformative power. To create solutions, understanding the problem is crucial. This book seeks to return control to the real experts--those living and working within our communities.Discover more at: www.opendataforeverybody.com
Open Data for Everybody
What if I told you something that could empower our third sector and activists to enhance their capacity? From gathering evidence for funding tenders to campaigning for crucial social issues and much more? It's called open data, yet many in social action remain unaware of it. Primarily shaped by corporate entities, open data seems tailored only for technologists, alienating the third sector. But in reality, it's a powerful tool for social change, bolstering civil society, and creating resilient communities.This book argues a simple point: if open data and the digital aspects that support it aren't accessible to all, then what is the point of it? In an age where technology should be seen as a fundamental human right, it's time to rethink outreach. Deeply rooted in grassroots social activism, this book explores a journey that led to collaborations with governments globally, based on real hands-on work, aiming to democratize open data. Through narrative storytelling, we share insights, best practices, procedures, and community-driven approaches. Regardless of your skill set or organization size, from grassroots workers to third-sector professionals and government officers, join us to reshape the perception of open data, fostering change in neighborhoods.Open Data for Everybody: Using Open Data for Social Good is a love letter to open data's transformative power. To create solutions, understanding the problem is crucial. This book seeks to return control to the real experts--those living and working within our communities.Discover more at: www.opendataforeverybody.com
KeySQL The Definitive Guide
This guide serves as the definitive reference for understanding the KeySQL query language.If you are familiar with the SQL language that manages data held in relational database management systems (RDBMS), then you are already familiar with KeySQL.KeySQL was invented to readily manage all enterprise data, including the fast-growing volumes of non-flat data native to the likes of JSON and XML. It adheres to the SQL standard of productivity while dealing with data structures much more complex than relational tables. And it makes the powerful RDBMS functionality including joins, set operations, and aggregations, equally applicable to the non-flat data.
Murach’s Python for Data Science (2nd Edition)
These days, businesses are collecting massive amounts of data. But this data isn't valuable until someone analyzes it to gain insights that can be used to make decisions. That's why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade. Now, with Murach's Python for Data Science as your guide, you'll learn how to use Python libraries to get, clean, prepare, and analyze data at a professional level. To start, you'll learn how to use Pandas for data analysis and Seaborn for data visualization. Then, you'll learn how to use Scikit-learn to create regression models that you can use to make predictions. To tie everything together, this book contains four realistic analyses that use real-world data. That's because studying analyses like these is critical to the learning process.
Programming Big Data Applications
In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. These data, commonly referred to as big data, are challenging current storage, processing and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from big data.Programming Big Data Applications introduces and discusses models, programming frameworks and algorithms to process and analyze large amounts of data. In particular, the book provides an in-depth description of the properties and mechanisms of the main programming paradigms for big data analysis, including MapReduce, workflow, BSP, message passing, and SQL-like. Through programming examples it also describes the most used frameworks for big data analysis like Hadoop, Spark, MPI, Hive and Storm. Each of the different systems is discussed and compared, highlighting their main features, their diffusion (both within their community of developers and among users), and their main advantages and disadvantages in implementing big data analysis applications.
Exploring the Language of Adventure Tourism
This collective volume deals with the language of adventure tourism from different approaches, such as linguistics, semantics, and pragmatics. The papers selected delve into different languages (Spanish, English, and Italian), either with a monolingual or a bilingual approach. They revolve around several parts of speech (e.g., verbs, adjectives), distinct phraseological units (e.g., collocations, compounds), and other aspects (e.g., accessibility, natural language processing) by relying on a corpus-based or corpus-driven methodology. Given the complete analysis of the main features of this language, this volume enhances the understanding of current terminology and also offers techniques that can be replicated in the study of other areas of knowledge.
Cloud Data Warehousing Volume II
Cloud. Data. Warehousing. The mythos conjured by three simple words has taken marketing to new levels of hyperbole. Cloud data warehousing, together with AI (of course) will change all business decision making and action taking.In addition to the expected cloud data warehouse model, three new delivery patterns have been spawned: data lakehouse, fabric, and mesh. However, as seen in Volume I of this series, the conceptual and logical architectures change only minimally as we move from on premises to the cloud. So, why these very different solutions?In Volume II, Dr. Barry Devlin-a founder of data warehousing-offers a framework of architectural design patterns (ADPs) to allow implementers to easily compare and contrast these different, proposed solutions. To evaluate their pros and cons according to a common model and in consistent terms. To choose an approach best suited to particular business needs and specific technical starting points.And looking to the behemoth of AI bearing down upon us, Barry proposes a set of conceptual models that allow possible answers to the foundational questions: In a world of burgeoning data and information, how do we really make decisions and should we entrust them to AI built upon cloud data warehousing?
Blockchain and AI
In the rapidly evolving landscape of the digital age, two technologies stand out for their transformative potential: Artificial Intelligence (AI) and Blockchain. This book offers an incisive exploration of the confluence between these technological titans, shedding light on the synergies, challenges, and innovations that arise at this intersection. The chapters explore thought-provoking analyses, informed by cutting-edge research and expert perspectives, that navigate the nuanced interplay of decentralized ledger technology and intelligent systems. From potential applications in teaching and learning, finance, healthcare, and governance to ethical considerations and future trajectories, this volume serves as an essential compendium for scholars, professionals, and anyone keen to grasp the future of digital innovation.
Corpora and Language Change in Late Modern English
Late Modern English has traditionally been considered a period of linguistic stability in terms of language standardization. However, a careful examination of crucial aspects of its internal and external history reveals that this period still deserves scholarly attention. This book aims to offer valuable tools for the study of Late Modern English, along with a selection of studies that approach linguistic variation from various perspectives.In the first part, the book provides an account of some available corpora for the study of Late Modern English, representing different text types such as medical English or private correspondence, among others. Additionally, these corpora cover various dialects and early new varieties of English.In the second part, several corpus-based studies assess Late Modern English at different levels shedding light on the language of the period.
Advanced Data Analytics with AWS
Master the Fundamentals of Data Analytics at ScaleBook DescriptionEmbark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook.Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets.The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons.The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence.Table of Contents1. Introduction to Data Analytics and AWS2. Getting Started with AWS3. Collecting Data with AWS4. Processing Data on AWS5. Descriptive Analytics on AWS6. Advanced Data Analysis on AWS7. Additional Use Cases for Data Analysis8. Data Visualization and Interaction on AWS9. The Future of Data Analytics10. Conclusion and Next Steps Index
Ultimate Snowflake Architecture for Cloud Data Warehousing
"Unlocking the Power of Snowflake: Unveiling the Architectural Wonders of Modern Data Management"Book DescriptionUnlock the revolutionary world of Snowflake with this comprehensive book which offers invaluable insights into every aspect of Snowflake architecture and management.Beginning with an introduction to Snowflake's architecture and key concepts, you will learn about cloud data warehousing principles like Star and Snowflake schemas to master efficient data organization. Advancing to topics such as distributed systems and data loading techniques, you will discover how Snowflake manages data storage and processing for scalability and optimized performance.Covering security features like encryption and access control, the book will equip you with the tools to ensure data confidentiality and compliance. The book also covers expert insights into performance optimization and schema design, equipping you with techniques to unleash Snowflake's full potential.By the end, you will have a comprehensive understanding of Snowflake's architecture and be empowered to leverage its features for valuable insights from massive datasets.Table of Contents1. Getting Started with Snowflake Architecture2. Managing Organizations and Accounts3. Virtual Warehouse Compute4. Role-Based Access Control5. Snowflake Data Governance6. Snowflake Security Framework7. Deployment Considerations8. Data Storage in Snowflake9. Snowflake Marketplace: 10. SnowparkIndex
Optimizing DAX
This book is the state of the art of optimizing DAX expressions in Power BI and Analysis Services semantic models. Learn how to locate performance bottlenecks, read the query plan and performance metrics, rewrite the slower part of an expression, and measure the performance improvements.Use tools like Power BI Desktop Performance Analyzer and DAX StudioUnderstand the DAX formula engine Learn characteristics of different storage engines (VertiPaq, DirectQuery over SQL)Read DAX query plansOptimize common DAX constructsSolve security performance issuesImprove DirectQuery response timeControl the data placement in composite models to avoid bottlenecksPrerequisites: proficiency in DAX and SQL (for DirectQuery), understanding of the Tabular engine used by semantic models, and familiarity with DAX Studio.
Duckdb in Action
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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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 author 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.
Optimizing DAX
This book is the state of the art of optimizing DAX expressions in Power BI and Analysis Services semantic models. Learn how to locate performance bottlenecks, read the query plan and performance metrics, rewrite the slower part of an expression, and measure the performance improvements.Use tools like Power BI Desktop Performance Analyzer and DAX StudioUnderstand the DAX formula engine Learn characteristics of different storage engines (VertiPaq, DirectQuery over SQL)Read DAX query plansOptimize common DAX constructsSolve security performance issuesImprove DirectQuery response timeControl the data placement in composite models to avoid bottlenecksPrerequisites: proficiency in DAX and SQL (for DirectQuery), understanding of the Tabular engine used by semantic models, and familiarity with DAX Studio.The hardcover version of this book is in full color, with maximum fidelity of screenshots and different background colors to make code snippets language easy to identify.
Practical MongoDB Aggregations
Begin your journey toward efficient data manipulation with this robust technical guide and enhance your aggregation skills while building efficient pipelines for a variety of tasksKey Features: Build effective aggregation pipelines for increased productivity and performanceSolve common data manipulation and analysis problems with the help of practical examplesLearn essential strategies to aggregate time series data in financial datasets and IoTPurchase of the print or Kindle book includes a free PDF eBookBook Description: Officially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks.This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale.Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you'll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.What You Will Learn: Develop dynamic aggregation pipelines tailored to changing business requirementsMaster essential techniques to optimize aggregation pipelines for rapid data processingAchieve optimal efficiency for applying aggregations to vast datasets with effective sharding strategiesEliminate the performance penalties of processing data externally by filtering, grouping, and calculating aggregated values directly within the databaseUse pipelines to help you secure your data access and distributionWho this book is for: This book is for intermediate-level developers, architects, analysts, engineers, and data scientists who are interested in learning about aggregation capabilities in MongoDB. Working knowledge of MongoDB is needed to get the most out of this book.
Data Analytics for Marketing
Conduct data-driven marketing research and analysis with hands-on examples using Python by leveraging open-source tools and libraries Key FeaturesAnalyze marketing data using proper statistical techniquesUse data modeling and analytics to understand customer preferences and enhance strategies without complex mathImplement Python libraries like DoWhy, Pandas, and Prophet in a business setting with examples and use casesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMost marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial.In this book, you'll learn how to give context to your data and turn it into useful information. You'll understand how and where to use a tool or dataset for a specific question, exploring the "what and why questions" to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you'll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you'll delve into customer analytics and insights. Finally, you'll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making.By the end of this book, you'll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.What you will learnUnderstand the basic ideas behind the main statistical models used in marketing analyticsApply the right models and tools to a specific analytical questionDiscover how to conduct causal inference, experimentation, and statistical modeling with PythonImplement common open source Python libraries for specific use cases with immediately applicable codeAnalyze customer lifetime data and generate customer insightsGo through the different stages of analytics, from descriptive to prescriptiveWho this book is forThis book is for data analysts and data scientists working in a marketing team supporting analytics and marketing research, who want to provide better insights that lead to data-driven decision-making. Prior knowledge of Python, data analysis, and statistics is required to get the most out of this book. Table of ContentsWhat is Marketing Analytics? Extracting and Exploring Data with Singer and pandasDesign Principles and Presenting Results with StreamlitEconometrics and Causal Inference with Statsmodels and PyMCForecasting with Prophet, ARIMA, and Other Models Using StatsForecastAnomaly Detection with StatsForecast and PyMCCustomer Insights - Segmentation and RFMCustomer Lifetime Value with PyMC MarketingCustomer Survey AnalysisConjoint Analysis with pandas and StatsmodelsMulti-Touch Digital AttributionMedia Mix Modeling with PyMC MarketingRunning Experiments with PyMC
Mastering Time Series Analysis and Forecasting with Python
Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.Book Description"Mastering Time Series Analysis and Forecasting with Python" is an essential handbook tailored for those seeking to harness the power of time series data in their work.The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection.Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains.Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data.Table of Contents1. Introduction to Time Series2. Overview of Time Series Libraries in Python3. Visualization of Time Series Data4. Exploratory Analysis of Time Series Data5. Feature Engineering on Time Series6. Time Series Forecasting - ML Approach Part 17. Time Series Forecasting - ML Approach Part 28. Time Series Forecasting - DL Approach9. Multivariate Time Series, Metrics, and Validation Index
Extending Excel with Python and R
Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivityKey Features: - Perform advanced data analysis and visualization techniques with R and Python on Excel data- Use exploratory data analysis and pivot table analysis for deeper insights into your data- Integrate R and Python code directly into Excel using VBA or API endpoints- Purchase of the print or Kindle book includes a free PDF eBookBook Description: - Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics.- This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations.- Working through the chapters, you'll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency.- Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level.- By the end of this book, you'll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What You Will Learn: - Read and write Excel files with R and Python libraries- Automate Excel tasks with R and Python scripts- Use R and Python to execute Excel VBA macros- Format Excel sheets using R and Python packages- Create graphs with ggplot2 and Matplotlib in Excel- Analyze Excel data with statistical methods and time series analysis- Explore various methods to call R and Python functions from ExcelWho this book is for: - If you're a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you.- The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners.- A basic understanding of Excel, Python, and R is all you need to get started. Table of Contents- Reading Excel Spreadsheets- Writing Excel Spreadsheets- Executing VBA Code from R and Python- Automating Further (Email Notifications and More)- Formatting Your Excel sheet- Inserting ggplot2/matplotlib Graphs- Pivot Tables (tidyquant in R and with win32com and pypiwin32 in Python)/Summary Table {gt}- Exploratory Data Analysis with R and Python- Statistical Analysis: Linear and Logistic Regression- Time Series Analysis: Statistics, Plots, and Forecasting- Calling R/Python Locally from Excel Directly or via an API- Data Analysis and Visualization with R and Python for Excel Data - A Case Study
Machine Learning for Networking
This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning for Networking, MLN 2023, held in Paris, France, during November 28-30, 2023. The 18 full papers included in this book were carefully reviewed and selected from 34 submissions. The conference aims at providing a top forum for researchers and practitioners to present and discuss new trends in machine learning, deep learning, pattern recognition and optimization for network architectures and services.
Information Technologies and Intelligent Decision Making Systems
This book constitutes the refereed post proceedings of the Third International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023, held in Moscow, Russia, during December, 12-14, 2023. The 18 full papers included in this book were carefully reviewed and selected from 54 submissions. The papers presented in this volume focus on topics such as digital, intellectual and information transformation, the development of computer models and the improvement of automated and computing processes.
Service Desk Manager Bootcamp
���� Welcome to the Service Desk Manager Bootcamp! ����Are you ready to take your service desk management skills to the next level? Introducing our exclusive book bundle: "Service Desk Manager Bootcamp: ITIL 4 Standards, KPI & SLA Management." ����In this comprehensive bundle, you'll gain access to four essential books that cover everything you need to know to excel in service desk management: ���� BOOK 1: Service Desk Essentials: A Beginner's Guide to ITIL 4 Standard Get started on the right foot with this beginner's guide to ITIL 4 Standard. Learn the core principles and practices of IT service management and align IT services with business objectives.���� BOOK 2: Mastering KPIs: Optimizing Service Desk Performance Unlock the secrets to optimizing service desk performance with this book. Discover how to identify, define, and track key performance indicators (KPIs) to enhance customer satisfaction and drive organizational success.���� BOOK 3: SLA Mastery: Advanced Strategies for Service Desk Managers Take your SLA management skills to the next level with advanced strategies and techniques. Learn how to negotiate, implement, and manage SLAs to ensure compliance and alignment with business objectives.���� BOOK 4: Beyond Basics: Expert Insights into Service Desk Management in the Digital Age Stay ahead of the curve with expert insights into service desk management in the digital age. Explore innovative approaches, emerging technologies, and best practices for delivering exceptional customer experiences.With over 3000 characters of expert guidance and practical strategies, this book bundle is your ticket to service desk excellence. Whether you're a beginner looking to build a strong foundation or an experienced manager seeking advanced techniques, the Service Desk Manager Bootcamp has something for everyone.Don't miss out on this opportunity to elevate your service desk management skills and drive organizational success. Get your hands on the Service Desk Manager Bootcamp book bundle today! ����
A Methodology for Building the Data Lakehouse
The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. This book covers the essential topics prior to building the full methodology for the data lakehouse.Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after. Deep dive into one specific implementation of a data lakehouse: the Databricks Lakehouse Platform.
Deep Learning for Time Series Cookbook
Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipesKey FeaturesLearn the fundamentals of time series analysis and how to model time series data using deep learningExplore the world of deep learning with PyTorch and build advanced deep neural networksGain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detectionPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMost organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You'll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you'll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.What you will learnGrasp the core of time series analysis and unleash its power using PythonUnderstand PyTorch and how to use it to build deep learning modelsDiscover how to transform a time series for training transformersUnderstand how to deal with various time series characteristicsTackle forecasting problems, involving univariate or multivariate dataMaster time series classification with residual and convolutional neural networksGet up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)Who this book is forIf you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.Table of ContentsGetting Started with Time SeriesGetting Started with PyTorchUnivariate Time Series ForecastingForecasting with PyTorch LightningGlobal Forecasting ModelsAdvanced Deep Learning Architectures for Time Series ForecastingProbabilistic Time Series ForecastingDeep Learning for Time Series ClassificationDeep Learning for Time Series Anomaly Detection
Formalizing Natural Languages: Applications to Natural Language Processing and Digital Humanities
This book constitutes the refereed proceedings of the 17th International Conference on Formalizing Natural Languages with NooJ 2023 and Its Natural Language Processing Applications, NooJ 2023, held in Zadar, Croatia, during May 31-June 2, 2023. The 19 full papers included in this book were carefully reviewed and selected from 64 submissions. They were organized in topical sections as follows: morphological and lexical resources; syntactic and semantic resources; corpus linguistics and discourse analysis; and natural language processing applications.
Tableau for Salesforce
Tableau is a powerful data visualization tool, and Salesforce CRM is the most successful customer relationship management software. Companies often use these two tools to analyze data and create visualizations. The book starts with an introduction to both Tableau and Salesforce CRM. It then goes on to show how you can connect Tableau with Salesforce CRM and link the two tools. Then, it walks through some of the key features of this connection that allow for seamless data analysis and visualization. It then moves on to advanced use cases, combining Salesforce and Tableau for advanced analytics and decision-making
