Inne

Data Modeling with Tableau. A practical guide to building data models using Tableau Prep and Tableau Desktop

Kirk Munroe

Tableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google’s Looker). That doesn’t mean Tableau doesn’t have enterprise data governance; it is both robust and highly flexible. This book will help you effectively use Tableau governance models to build a data-driven organization.Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you’ll learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You’ll also explore the components of Tableau Server and Tableau Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you’ll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will guide you through the entire Tableau stack and the techniques required to build the right level of governance into Tableau data models for the correct use cases.By the end of this Tableau book, you’ll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.

Data Quality in the Age of AI. Building a foundation for AI strategy and data culture

Andrew Jones

As organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies.Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.

Data science od podstaw. Analiza danych w Pythonie. Wydanie II

Joel Grus

Analityka danych jest uważana za wyjątkowo obiecującą dziedzinę wiedzy. Rozwija się błyskawicznie i znajduje coraz to nowsze zastosowania. Profesjonaliści biegli w eksploracji danych i wydobywaniu z nich pożytecznych informacji mogą liczyć na interesującą pracę i bardzo atrakcyjne warunki zatrudnienia. Jednak aby zostać analitykiem danych, trzeba znać matematykę i statystykę, a także nauczyć się programowania. Umiejętności w zakresie uczenia maszynowego i uczenia głębokiego również są ważne. W przypadku tak specyficznej dziedziny, jaką jest nauka o danych, szczególnie istotne jest zdobycie gruntownych podstaw i dogłębne ich zrozumienie. W tym przewodniku opisano zagadnienia związane z podstawami nauki o danych. Wyjaśniono niezbędne elementy matematyki i statystyki. Przedstawiono także techniki budowy potrzebnych narzędzi i sposoby działania najistotniejszych algorytmów. Książka została skonstruowana tak, aby poszczególne implementacje były jak najbardziej przejrzyste i zrozumiałe. Zamieszczone tu przykłady napisano w Pythonie: jest to język dość łatwy do nauki, a pracę na danych ułatwia szereg przydatnych bibliotek Pythona. W drugim wydaniu znalazły się nowe tematy, takie jak uczenie głębokie, statystyka i przetwarzanie języka naturalnego, a także działania na ogromnych zbiorach danych. Zagadnienia te często pojawiają się w pracy współczesnego analityka danych. W książce między innymi: elementy algebry liniowej, statystyki i rachunku prawdopodobieństwa zbieranie, oczyszczanie i eksploracja danych algorytmy modeli analizy danych podstawy uczenia maszynowego systemy rekomendacji i przetwarzanie języka naturalnego analiza sieci społecznościowych i algorytm MapReduce Nauka o danych: bazuj na solidnych podstawach!

Data Storytelling with Google Looker Studio. A hands-on guide to using Looker Studio for building compelling and effective dashboards

Sireesha Pulipati

Presenting data visually makes it easier for organizations and individuals to interpret and analyze information. Looker Studio is an easy-to-use, collaborative tool that enables you to transform your data into engaging visualizations. This allows you to build and share dashboards that help monitor key performance indicators, identify patterns, and generate insights to ultimately drive decisions and actions.Data Storytelling with Looker Studio begins by laying out the foundational design principles and guidelines that are essential to creating accurate, effective, and compelling data visualizations. Next, you’ll delve into features and capabilities of Looker Studio – from basic to advanced – and explore their application with examples. The subsequent chapters walk you through building dashboards with a structured three-stage process called the 3D approach using real-world examples that’ll help you understand the various design and implementation considerations. This approach involves determining the objectives and needs of the dashboard, designing its key components and layout, and developing each element of the dashboard.By the end of this book, you will have a solid understanding of the storytelling approach and be able to create data stories of your own using Looker Studio.

Data Visualization: a successful design process

Andy Kirk

Do you want to create more attractive charts? Or do you have huge data sets and need to unearth the key insights in a visual manner? Data visualization is the representation and presentation of data, using proven design techniques to bring alive the patterns, stories and key insights locked away.Data Visualization: a Successful Design Process explores the unique fusion of art and science that is data visualization; a discipline for which instinct alone is insufficient for you to succeed in enabling audiences to discover key trends, insights and discoveries from your data. This book will equip you with the key techniques required to overcome contemporary data visualization challenges. You'll discover a proven design methodology that helps you develop invaluable knowledge and practical capabilities.You'll never again settle for a default Excel chart or resort to fancy-looking graphs. You will be able to work from the starting point of acquiring, preparing and familiarizing with your data, right through to concept design. Choose your killer visual representation to engage and inform your audience.Data Visualization: a Successful Design Process will inspire you to relish any visualization project with greater confidence and bullish know-how; turning challenges into exciting design opportunities.

Data Visualization with D3.js Cookbook. Turn your digital data into dynamic graphics with this exciting, leading-edge cookbook. Packed with recipes and practical guidance it will quickly make you a proficient user of the D3 JavaScript library

Nick Zhu

D3.js is a JavaScript library designed to display digital data in dynamic graphical form. It helps you bring data to life using HTML, SVG, and CSS. D3 allows great control over the final visual result, and it is the hottest and most powerful web-based data visualization technology on the market today.Data Visualization with D3.js Cookbook is packed with practical recipes to help you learn every aspect of data visualization with D3.Data Visualization with D3.js Cookbook is designed to provide you with all the guidance you need to get to grips with data visualization with D3. With this book, you will create breathtaking data visualization with professional efficiency and precision with the help of practical recipes, illustrations, and code samples.Data Visualization with D3.js Cookbook starts off by touching upon data visualization and D3 basics before gradually taking you through a number of practical recipes covering a wide range of topics you need to know about D3.You will learn the fundamental concepts of data visualization, functional JavaScript, and D3 fundamentals including element selection, data binding, animation, and SVG generation. You will also learn how to leverage more advanced techniques such as custom interpolators, custom tweening, timers, the layout manager, force manipulation, and so on. This book also provides a number of pre-built chart recipes with ready-to-go sample code to help you bootstrap quickly.

Data Wrangling with R. Load, explore, transform and visualize data for modeling with tidyverse libraries

Gustavo Santos

In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you’ll need plenty of tools that enable you to extract the most useful knowledge from data.Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization.The book begins by teaching you how to load and explore datasets. Then, you’ll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you’ll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards.By the end of this book, you’ll have learned how to create your first data model and build an application with Shiny in R.

Data Wrangling with SQL. A hands-on guide to manipulating, wrangling, and engineering data using SQL

Raghav Kandarpa, Shivangi Saxena

The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data.The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling.By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.