Datafangst og dataanalyse

Organisationer fokuserer i stigende grad på, hvordan de kan bruge Big Data strategisk. De investerer milliarder af kroner i denne ny teknologi, men værdien og afkastet viser sig tit at være begrænset. Sådanne udfordringer kan meget vel skyldes ledernes mindset, der begrænser hvordan organisationen bruger Big Data-teknologi til at styrke interaktionen og samspillet med kunder og andre interessenter.Det første skridt på vejen mod at få succes med Big Data er derfor at identificere og udfordre sit ledelsesmæssige mindset. I denne bog identificerer og diskuterer vi fire dominerende ledelsesmæssige mindset, der hver især fører til fire unikke Big Data strategier. Når ledere inden for samme team har forskellige mindset, kan det føre til manglende mental og organisatorisk alignment og problemer med at håndtere disruption. Da mindsets ofte er ubevidste, har vi udviklet en onlinetest, der hjælper dig og dine kolleger med at identificere jeres dominerende mindset. Det vil give jer et det bedste afsæt til at vurdere og beslutte hvad der er jeres optimale strategiske vej til Big Data succes.Omtale"Bogen giver en praktisk og teoretisk indførelse i big data. (…) Bogen har mange eksempler, som understøtter bogens budskaber."5/6 stjerner- anmeldt af Henrik Ørholst, Børsen, 1. februar 2018“Det er næsten provokerende, at sammenhængen mellem lederes mindset og succesfuld innovation kan være så markant. Læs bogen og få rystet rundt på dit eget mindset.”- Camilla Laudrup, leder af DRs public outreach sekretariat

Product Operations is the discipline of helping your Product Management function scale well. Surrounding the team with all of the essential inputs to set strategy, prioritize, and streamline ways of working.Many companies want to reap the benefits of economies of scale that comes with being a product-led company. As our businesses change shape to focus more on software, so do our ways of working. We need to make sure we're breaking down these silos of information and capabilities that arise at scale. To react quickly and set great Product Strategies, leaders and team members alike need access to high quality data and a process to implement their decisions.In this book, Melissa Perri and Denise Tilles explain how Product Operations helps solve many of scaling issues companies face today, like:Connecting financial metrics back to the delivery of software featuresBetter working relationships between go-to-market teams and software teamsGetting the right information to set and monitor company and product strategiesStreamlining the flow of user insights into the right teamsHow to set up a Product Management Operating ModelExecutives and leaders of SAAS scale up and enterprise companies will discover the benefits of the Three Pillars of Product Operations as they read through the stories of real companies who have implemented the function, like Stripe, Uber, athenahealth, Oscar Health, and Fidelity. This book covers the following topics:What is Product Operations and why is it an essential function in scaling software companiesThe Three Pillars of Product Operations: Data and Insights; Customer and Market Insights; and Process and GovernanceHow to Introduce and Build a Team for Product Operations

Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?

Learn the latest version of ArcGIS Pro with the newest edition of this bestselling series. Getting to Know ArcGIS Pro 2.8 introduces the tools and functions of ArcGIS Pro, the powerful desktop GIS application. Geographic information system (GIS) software is making a huge impact in businesses and organizations with mapping and analytic capabilities.  Getting to Know ArcGIS Pro 2.8 uses practical project workflows to teach best practices to readers of all skill levels. Readers will explore data visualizations, build a geodatabase, discover 3D GIS, create maps for web and physical presentations, and more. With more than 300 full-color images, Getting to Know ArcGIS Pro 2.8 clarifies complicated processes such as developing a geoprocessing model, using Python to write a script tool, and creating space-time cubes for analysis. Throughout the book, short sidebars about real-world GIS scenarios in specific industries help readers understand how ArcGIS Pro can be applied widely to solve problems. At the end of each chapter, a summary and glossary help reinforce the skills learned.  This edition has been completely updated for use with ArcGIS Pro 2.8. Other updates include new chapters on ArcGIS Online and geocoding. The Getting to Know series has been teaching readers about GIS for more than 20 years. Ideal for students, self-learners, and professionals who want to learn the premier GIS desktop application, Getting to Know ArcGIS Pro 2.8 is a textbook and desk reference designed to show users how they can use ArcGIS Pro successfully on their own.

An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration.Doing Science with Python introduces readers to the most popular coding tools for scientific research, such as Anaconda, Spyder, Jupyter Notebooks, and JupyterLab, as well as dozens of important Python libraries for working with data, including NumPy, matplotlib, and pandas. No prior programming experience is required! You'll be guided through setting up a professional coding environment, then get a crash course on programming with Python, and explore the many tools and libraries ideal for working with data, designing visualizations, simulating natural events, and more. In the book's applied projects, you'll use these tools to write programs that perform tasks like counting tree rings, creating an interactive science slideshow, and simulating the foraging patterns of animals.

Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey Features- Grasp the principles of data centricity and apply them to real-world scenarios- Gain experience with quality data collection, labeling, and synthetic data creation using Python- Develop essential skills for building reliable, responsible, and ethical machine learning solutions- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learn- Understand the impact of input data quality compared to model selection and tuning- Recognize the crucial role of subject-matter experts in effective model development- Implement data cleaning, labeling, and augmentation best practices- Explore common synthetic data generation techniques and their applications- Apply synthetic data generation techniques using common Python packages- Detect and mitigate bias in a dataset using best-practice techniques- Understand the importance of reliability, responsibility, and ethical considerations in ML/AIWho this book is forThis book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.Table of Contents- Exploring Data-Centric Machine Learning- From Model-Centric to Data-Centric - ML's Evolution- Principles of Data-Centric ML- Data Labeling Is a Collaborative Process- Techniques for Data Cleaning- Techniques for Programmatic Labeling in Machine Learning- Using Synthetic Data in Data-Centric Machine Learning- Techniques for Identifying and Removing Bias- Dealing with Edge Cases and Rare Events in Machine Learning- Kick-Starting Your Journey in Data-Centric Machine Learning

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data FactoryKey Features:Learn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory's visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description:This new edition of the Azure Data Factory Cookbook, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF.You'll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discovering the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage.With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.Two new chapters covering Azure Data Explorer and key best practices have been added, along with new recipes throughout.By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.What You Will Learn:Create an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Databricks and Delta tablesWork with big data in Azure Data Lake using Spark PoolMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for:This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite.

Unlock the full potential of your data by mastering the art of cleaning, preparing, and transforming data with Power BI for smarter insights and data visualizationsKey Features:Implement best practices for connecting, preparing, cleaning, and analyzing multiple sources of data using Power BIConduct exploratory data analysis (EDA) using DAX, PowerQuery, and the M languageApply your newfound knowledge to tackle common data challenges for visualizations in Power BIPurchase of the print or Kindle book includes a free PDF eBookBook Description:Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization.This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You'll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you'll explore Power BI's data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you'll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier.By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.What You Will Learn:Connect to data sources using both import and DirectQuery optionsUse the Query Editor to apply data transformationsTransform your data using the M query languageDesign clean and optimized data models by creating relationships and DAX calculationsPerform exploratory data analysis using Power BIAddress the most common data challenges with best practicesExplore the benefits of using OpenAI, ChatGPT, and Microsoft Copilot for simplifying data cleaningWho this book is for:If you're a data analyst, business intelligence professional, business analyst, data scientist, or anyone who works with data on a regular basis, this book is for you. It's a useful resource for anyone who wants to gain a deeper understanding of data quality issues and best practices for data cleaning in Power BI. If you have a basic knowledge of BI tools and concepts, this book will help you advance your skills in Power BI.

Troubleshoot query performance issues, identify anti-patterns in your code, and write efficient T-SQL queries with this guide for T-SQL developersKey Features:- A definitive guide to mastering the techniques of writing efficient T-SQL code- Learn query optimization fundamentals, query analysis, and how query structure impacts performance- Discover insightful solutions to detect, analyze, and tune query performance issues- Purchase of the print or Kindle book includes a free PDF eBookBook Description:Data professionals seeking to excel in Transact-SQL for Microsoft SQL Server and Azure SQL Database often lack comprehensive resources. Learn T-SQL Querying second edition focuses on indexing queries and crafting elegant T-SQL code enabling data professionals gain mastery in modern SQL Server versions (2022) and Azure SQL Database. The book covers new topics like logical statement processing flow, data access using indexes, and best practices for tuning T-SQL queries.Starting with query processing fundamentals, the book lays a foundation for writing performant T-SQL queries. You'll explore the mechanics of the Query Optimizer and Query Execution Plans, learning to analyze execution plans for insights into current performance and scalability. Using dynamic management views (DMVs) and dynamic management functions (DMFs), you'll build diagnostic queries. The book covers indexing and delves into SQL Server's built-in tools to expedite resolution of T-SQL query performance and scalability issues. Hands-on examples will guide you to avoid UDF pitfalls and understand features like predicate SARGability, Query Store, and Query Tuning Assistant.By the end of this book, you'll have developed the ability to identify query performance bottlenecks, recognize anti-patterns, and avoid pitfallsWhat You Will Learn:- Identify opportunities to write well-formed T-SQL statements- Familiarize yourself with the Cardinality Estimator for query optimization- Create efficient indexes for your existing workloads- Implement best practices for T-SQL querying- Explore Query Execution Dynamic Management Views- Utilize the latest performance optimization features in SQL Server 2017, 2019, and 2022- Safeguard query performance during upgrades to newer versions of SQL ServerWho this book is for:This book is for database administrators, database developers, data analysts, data scientists and T-SQL practitioners who want to master the art of writing efficient T-SQL code and troubleshooting query performance issues through practical examples. A basic understanding of T-SQL syntax, writing queries in SQL Server, and using the SQL Server Management Studio tool will be helpful to get started.Table of Contents- Understanding Query Processing- Mechanics of the Query Optimizer- Exploring Query Execution Plans- Indexing for T-SQL Performance- Writing Elegant T-SQL Queries- Discovering T-SQL Anti-patterns in Depth - Building diagnostic queries using DMVs and DMFs- Building XEvent profiler traces- Comparative analysis of Query Plans- Tracking performance history with Query Store- Troubleshooting Live Queries- Managing optimizer changes with the Query Tuning Assistant

Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much moreKey FeaturesAcquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learningGain the confidence to explain complex statistical, machine learning, and deep learning theoryExtend your expertise beyond model development with version control, shell scripting, and model deployment fundamentalsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company.Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you'll find tips on job hunting, resume writing, and creating a top-notch portfolio. You'll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview.By the end of this interview guide, you'll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learnExplore data science trends, job demands, and potential career pathsSecure interviews with industry-standard resume and portfolio tipsPractice data manipulation with Python and SQLLearn about supervised and unsupervised machine learning modelsMaster deep learning components such as backpropagation and activation functionsEnhance your productivity by implementing code versioning through GitStreamline workflows using shell scripting for increased efficiencyWho this book is forWhether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews.Table of ContentsExploring the Modern Data Science LandscapeFinding a Job in Data ScienceProgramming with PythonVisualizing Data and Data StorytellingQuerying Databases with SQLScripting with Shell and Bash Commands in LinuxUsing Git for Version ControlMining Data with Probability and StatisticsUnderstanding Feature Engineering and Preparing Data for ModelingMastering Machine Learning ConceptsBuilding Networks with Deep LearningImplementing Machine Learning Solutions with MLOpsMastering the Interview RoundsNegotiating Compensation

Turn raw data into meaningful solutions¿DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions.This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making.By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment.WHAT YOU WILL LEARN¿ Master key data science tools like Python, NumPy, Pandas, and more.¿ Build a strong foundation in statistics and probability for data analysis.¿ Learn and apply machine learning, from regression to deep learning.¿ Expertise in NLP and recommender systems for advanced analytics.¿ End-to-end data project from data collection to model deployment, with planning and execution.WHO THIS BOOK IS FORThis book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts.

Uncover valuable business insights by leveraging the power of Kibana to navigate and interpret datasets for improved decision makingKey FeaturesGain profound understanding of the end-to-end workings of KibanaExplore the powerful administration features in Kibana 8.x for managing and supporting data ingestion pipelinesBuild your own analytics and visualization solution from scratchPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnleash the full potential of Kibana-an indispensable tool for data analysts to seamlessly explore vast datasets, uncover key insights, identify trends and anomalies, and share results. This book guides you through its user-friendly interface, interactive visualizations, and robust features, including real-time data monitoring and advanced analytics, showing you how Kibana revolutionizes your approach to navigating and analyzing complex datasets.Starting with the foundational steps of installing, configuring, and running Kibana, this book progresses systematically to explain the search and data visualization capabilities for data stored in the Elasticsearch cluster. You'll then delve into the practical details of creating data views and optimizing spaces to better organize the analysis environment. As you advance, you'll get to grips with using the discover interface and learn how to build different types of extensive visualizations using Lens.By the end of this book, you'll have a complete understanding of how Kibana works, helping you leverage its capabilities to build an analytics and visualization solution from scratch for your data-driven use case.What you will learnCreate visualizations using the Visualize interface in KibanaBuild shareable search dashboards to drill down and perform advanced analysis and reportingSearch data to make correlations and identify and explain trendsEmbed dashboards, share links, and export PNG, PDF, or CSV files and send as an attachmentConfigure and tweak advanced settings to best manage saved objects in KibanaImplement several types of aggregations working behind the scenes of extensive visualizationsWho this book is forIf you're a data analyst or a data engineer, this book is for you. It's also a useful resource to database administrators, analysts, and business users looking to build a foundation in creating intuitive dashboards using Kibana 8.x and data analysis techniques for improved decision making. Foundational knowledge of Elasticsearch fundamentals will provide an added advantage.Table of ContentsIntroduction to KibanaCreating Data Views and Introducing SpacesDiscovering Data through DiscoverHow About We Visualize?Powering Visualizations with Near Real-Time UpdatesData Analysis with Machine LearningGraph VisualizationFinally, the DashboardES|QL and Advanced Kibana ConceptsQuery DSL and Management through Kibana

Unleash the full potential of Microsoft Excel's latest version and elevate your data-driven prowess with this comprehensive resourceKey Features- Create robust and automated dashboards in Excel for M365- Apply data visualization principles and employ dynamic charts and tables to create constantly updated and informative dashboards for your organization- Uncover the best practices for effective dashboard creation- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionM365 Excel is a modern Excel version that is constantly updated with features that make creating and automating analyses, reports, and dashboards very easy compared with older Excel versions. This book will help you leverage its full capabilities, beginning with a quick overview of what dashboards are and how they are different from other types of reports. Then, you'll familiarize yourself with the different standard dashboards currently available and what they are meant to accomplish for organizations. As you progress, you'll get to grips with the use of new powerful tools such as Power Query and dynamic array formulae in the automation of analysis, gaining insights into the right approach to take in building effective dashboards. You'll equip yourself with not only all the essential formulae, charts, and non-chart visuals but also learn how to set up your dashboard perfectly. Along the way, you'll build a couple of awesome dashboards from scratch to utilize your newfound knowledge.By the end of this book, you will be able to carry out an impressive and robust level of analysis on business data that may come from multiple sources or files, using better processes, formulae, and best practices in M365 to create insightful dashboards faster.What you will learn- Understand the importance of dashboards in today's business analytics environment- Delve into the various essential formulae in Excel- Utilize Power Query to shape and transform data to extract insights easily- Explore the power of the new dynamic array functions in M365- Employ PivotTable and Power Pivot to automate your dashboards- Master the setup and optimization of your dashboard canvas- Discover best practices for visualization, charts, and effective dashboard creation- Consolidate your knowledge through a hands-on concluding projectWho this book is forThis book is for Microsoft Excel users, especially those tasked with creating dynamic reports and dashboards that require data and help support to decision-makers within an organization with visually engaging and actionable insights. Financial analysts, data analysts, business analysts, and BI professionals will also greatly benefit from this book. Some familiarity with the Microsoft Excel interface is a prerequisite.Table of Contents- Dashboards, Reports, and M365 Excel- Common Dashboards in Large Companies- The Importance of Connecting Directly to the Primary Data Sources- Power Query: the Ultimate Data Transformation Tool- PivotTable and Power Pivot- Must-Know Legacy Excel Functions- Dynamic Array Functions and Lambda Functions- Getting Comfortable with the 19 Excel Charts- Non-Chart Visuals- Setting Up the Dashboard's Data Model- Perfecting the Dashboard- Best Practices for Real-World Dashboard Building

Unlock the Full Potential of Your Data with the "Power BI Data Mastery Made Easy" Book Bundle!Are you ready to transform your data into actionable insights and make informed decisions that drive success? Look no further! Introducing the "Power BI Data Mastery Made Easy" book bundle, a comprehensive collection of resources that will empower you to harness the true power of Microsoft's leading business intelligence and data visualization tool-Power BI.Here's what you'll discover in this incredible bundle:Book 1 - Power BI Essentials: A Beginner's Guide to Data Visualization Mastery· Ideal for beginners: Build a solid foundation in data visualization.· Learn to import and transform data from various sources.· Master the art of data analysis and visualization.Book 2 - Mastering Power BI: Advanced Techniques and Best Practices for Analysts· Elevate your skills to the next level with advanced techniques.· Discover best practices for tackling complex analytical challenges.· Master DAX formulas and optimize data models.· Become an analytics expert and excel in your field.Book 3 - Power BI Data Modeling: Building Robust Datasets for Effective Analysis· Unlock the full potential of Power BI with robust data modeling.· Design efficient and flexible data models.· Gain the skills to create powerful data sets for effective analysis.Book 4 - Expert Power BI: Advanced Analytics and Custom Visualizations Mastery· Dive into the world of advanced analytics and custom visuals.· Explore machine learning integration and geographic analysis.· Become a Power BI expert and stand out in your field.Grab the "Power BI Data Mastery Made Easy" book bundle today and start your exciting adventure into the world of Power BI-where data mastery is within reach for everyone!

Getting to Know Mobile GIS empowers readers to creatively use Mobile GIS and drive innovation in this fast-advancing field."Mobile-first" and the "post-PC era" aren't just buzzwords; they represent today's reality. Within the GIS industry, the mobile internet and Mobile GIS have profoundly transformed how we acquire, visualize, analyze, and disseminate geospatial information. With its ubiquitous use and location-aware capabilities, the mobile platform is an essential part of enterprise GIS across industries, making Mobile GIS crucial to the business operations and future success of wide-ranging organizations.Getting to Know Mobile GIS helps GIS professionals and GIS students grasp the opportunities presented by Mobile GIS. It provides principles and detailed tutorials to teach today's cutting-edge Mobile GIS technologies. Using a no-code, low-code approach, it makes Mobile GIS easy and fun to learn. Readers can immediately become productive in creating enterprise GIS solutions, ranging from online and offline spatial data visualization, data collection, location sharing, and workforce coordination to integration with enterprise workflows.Dr. Pinde Fu leads the Configuration team at Esri Professional Services and teaches at universities, including Harvard University Extension School. Dr. Pinde Fu champions the configure-first approach, maximizing Esri's out-of-the-box Web and Mobile GIS technologies to swiftly build GIS solutions. Additionally, as a Senior Principal Software Developer, Dr. Fu employs full-stack programming to develop custom solutions as needed. Several of his projects have won special achievement awards. Fu is the author of Getting to Know Web GIS (Esri Press, 2022) and lead author of Web GIS: Principles and Applications (Esri Press, 2010).

Take your organization's data maturity to the next level by operationalizing data governanceKey FeaturesDevelop the mindset and skills essential for successful data stewardshipApply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardshipFollow a step-by-step program to develop a data operating model and implement data stewardship effectivelyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement-it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency.From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You'll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You'll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you'll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship.By the end of this book, you'll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learnEnhance your job prospects by understanding the data stewardship field, roles, and responsibilitiesDiscover how to develop a data strategy and translate it into a functional data operating modelDevelop an effective and efficient data stewardship programGain practical experience of establishing a data stewardship initiativeImplement purposeful governance with measurable ROIPrioritize data use cases with the value and effort matrixWho this book is forThis book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started.Table of ContentsFrom Business Strategy to Data Strategy to Data StewardshipHow Data Stewardship can help your organizationGetting Started with the Data Stewardship ProgramDeveloping a Comprehensive Data Management StrategyPeople, Process, TechnologyEstablishing a Data Governance OrganizationData Steward Roles and ResponsibilitiesEffective Data StewardshipSupercharge Data Governance and Stewardship with GPTData Stewardship Best PracticesTheory versus Real LifeCase Studies

Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey Features:- Learn practical data science combined with data theory to gain maximum insights from data- Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models- Explore actionable case studies to put your new skills to use immediately- Purchase of the print or Kindle book includes a free PDF eBookBook Description:Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What You Will Learn:- Master the fundamentals steps of data science through practical examples- Bridge the gap between math and programming using advanced statistics and ML- Harness probability, calculus, and models for effective data control- Explore transformative modern ML with large language models- Evaluate ML success with impactful metrics and MLOps- Create compelling visuals that convey actionable insights- Quantify and mitigate biases in data and ML modelsWho this book is for:If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you'll find this book useful. Familiarity with Python programming will further enhance your learning experience.Table of Contents- Data Science Terminology- Types of Data- The Five Steps of Data Science- Basic Mathematics- Impossible or Improbable - A Gentle Introduction to Probability- Advanced Probability- What are the Chances? An Introduction to Statistics- Advanced Statistics- Communicating Data- How to Tell if Your Toaster is Learning - Machine Learning Essentials- Predictions Don't Grow on Trees, or Do They?- Introduction to Transfer Learning and Pre-trained Models- Mitigating Algorithmic Bias and Tackling Model and Data Drift- AI Governance- Navigating Real-World Data Science Case Studies in Action

Unveiling insights, unleashing potential: Navigating the depths of data warehousing and mining for a data-driven tomorrow¿¿DESCRIPTION Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets.The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. By the end of this book, you will be equipped with the skills and knowledge to confidently translate business questions into actionable strategies, extracting valuable insights for informed decisions.WHAT YOU WILL LEARN¿ Designing and building efficient data warehouses¿ Handling diverse data types for comprehensive insights¿ Mastering various data mining techniques¿ Translating business questions into mining strategies¿ Techniques for pattern discovery and knowledge extractionWHO THIS BOOK IS FORFrom aspiring data analysts, data professionals, IT managers, to business intelligence practitioners, this book caters to a diverse audience.

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.

Novice or expert, learn to simplify and optimize data transformations¿DESCRIPTION "The Ultimate Power Query Cookbook for Power BI and Excel" serves up easy-to-follow recipes that transform data into meaningful insights. You will learn to clean messy files, combine datasets, and even use AI magic to Power BI and Excel.This book will walk you through the basics of getting connected to data with Power Query. You will understand how to ingest data from files, folders, databases, websites, APIs, and other third party sources. Once connected, you will learn how to transform the data so it is ready for your use. We will clean up columns, filter, replace, extract, and classify data in Power Query to meet your needs. The book offers over 100 practical recipes, ensuring you understand each step with clear explanations and examples. Lastly, we will go over advanced techniques to help optimize and simplify your transformations allowing fast refreshes all while helping you manage them in the future.This book will help you know how to apply these techniques and recipes to your data all while understanding the implications of making certain decisions. This will enable you to have better conversations with other data professionals who are providing data for your use.WHAT YOU WILL LEARN¿ Learn to connect to files, databases, and third-party services.¿ Manage data types and formats to optimize storage.¿ Transform, create, and manipulate queries.¿ Combine, merge, filter, and cleanse queries.¿ Integrate artificial intelligence to accelerate insights.¿ Perform complex and scalable transformations.WHO THIS BOOK IS FORNovice or expert, this book is designed for all Excel users, data analysts, Power BI power users, business professionals and data enthusiasts to get the most out of your data solutions when transforming your data in Power Query.

Mathematik für die Informatik: Dieses Buch bringt Ihnen die Grundlagen beiDieses Lehrbuch bietet eine Einführung in die grundlegenden Begriffe und Strukturen der Mathematik, welche am Anfang eines Informatikstudiums relevant sind. Weiterhin demonstriert es Anwendungen von mathematischen Konzepten und Methoden in der Informatik. Diese betreffen insbesondere formale Methoden der Programmverifikation und -entwicklung und den Entwurf von generischen Programmen. Ein spezielles Konzept mit einer leicht verständlichen Vermittlung des Stoffs, vielen Beispielen mit Rückgriffen auf die Schulmathematik und detaillierten Beweisen (verbunden mit der Erklärung des logischen Hintergrunds) erleichtert den Einstieg in die Mathematik an einer wissenschaftlichen Hochschule. Dadurch werden die Studierenden auch auf spätere Begriffe und tiefergehende Anwendungen der Mathematik in der Informatik gut vorbereitet. Die 5. Auflage erweitert den Inhalt um die Grundlagen der linearen Algebra. Die Übungsaufgaben zu den einzelnen Kapitel helfen, das Erlernte zu festigen und zu kontrollieren. Zahlreiche Lösungsvorschläge am Ende des Buchs ermöglichen die Überprüfung der eigenen Lösungen.Der InhaltMengentheoretische Grundlagen ¿ Logische Grundlagen ¿ Allgemeine direkte Produkte und Datenstrukturen ¿ Mathematische Beweise ¿ Spezifikation und Programmverifikation ¿ Spezielle Funktionen ¿ Spezielle Relationen und gerichtete Graphen ¿ Elementare Kombinatorik und ungerichtete Graphen ¿ Diskrete Wahrscheinlichkeitstheorie ¿ Generische Programmierung ¿ Grundbegriffe algebraischer Strukturen ¿ Formale Einführung der natürlichen Zahlen - Grundbegriffe der linearen AlgebraMit diesem Buch gelingt der Einstieg ins Informatik-StudiumMit diesem Buch schaffen Sie eine solide Basis für die Mathematikausbildung im Rahmen desInformatikstudiums. Zudem sind Sie durch die vorgestellten Problemstellungen in der Lage, selbstständig mathematische Konzepte und Methoden anzuwenden. Zielgruppen dieses Buchs über Mathematik in der Informatik sind Bachelor-Studierende in den ersten Studiensemestern folgender Fachbereiche:InformatikMathematikIngenieurwissenschaften

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.

Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics, tools, and techniques related to empowering IoT with big data technologies and big data analytics solutions, thus allowing for better processing, analysis, protection, distribution, and visualization of data for the benefit of IoT applications and second, a better deployment of IoT applications on the ground. This book covers big data in the IoT era, its application domains, current state-of-the-art in big data and IoT technologies, standards, platforms, and solutions. This book provides a holistic view of the big data value-chain for IoT, including storage, processing, protection, distribution, analytics, and visualization. Big data is a multi-disciplinary topic involving handling intensive, continuous, and heterogeneous data retrieved from different sources including sensors, social media, and embedded systems. The emergence of Internet of Things (IoT) and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data.