Unlock the potential of your BIM workflows with artificial intelligence and data visualization tools. This book provides guided instruction on using software like Revit, Dynamo, Python, and Power BI to automate processes, derive insights, and craft tailored dashboards that empower data-driven decisions in AEC projects. What this Book will help me do Effectively preprocess and manage BIM data for analysis and visualization. Design interactive and insightful dashboards in Power BI for project stakeholders. Integrate real-time IoT data and advanced analytics into BIM projects. Automate repetitive tasks in Revit using Dynamo and Python scripting. Understand the ethical considerations and emerging trends in AI for BIM. Author(s) Bruno Martorelli, a seasoned BIM manager, specializes in integrating technology and data analytics into construction workflows. With a background in architecture and programming, he bridges the gap between traditional methods and modern innovations. Bruno is dedicated to sharing practical strategies for data automation and visualization. Who is it for? This book is tailored for architects, engineers, and construction managers interested in elevating their BIM practices. If you're familiar with Revit and possess a basic understanding of data management, you'll find this resource invaluable. Beginners in Python or Power BI will also find accessible guidance to start applying advanced techniques in their workflows.

Generative AI has the potential to innovate and evolve business processes, but workers are still figuring out how to build with, optimize, and prompt GenAI tools to fit their needs. And of course, there are pitfalls to avoid, like security risks and hallucinations. Getting it right requires an intuitive understanding of the technology’s capabilities and limitations. This approachable guidebook helps learners of all levels navigate GenAI—and have fun while doing it. Loaded with insightful diagrams and illustrations, Visualizing Generative AI is the perfect entry point for curious IT professionals, business leaders who want to stay on top of the latest technologies, students exploring careers in cloud computing and AI, and anyone else interested in getting started with GenAI. You’ll traverse the generative AI landscape, exploring everything from how this technology works to the ways organizations are already leveraging it to great success. Understand how generative AI has evolved, with a focus on major breakthroughs Get acquainted with the available tools and platforms for GenAI workloads Examine real-world applications, such as chatbots and workflow automation Learn fundamentals that you can build upon as you continue your GenAI journey

Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of ‘artificial intelligence,’ has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and Python Detailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and others Case studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.

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

Radically improve the quality of your data visualizations by employing core principles of color, typography, chart types, data storytelling, and more. Everyday Data Visualization is a field guide for design techniques that will improve the charts, reports, and data dashboards you build every day. Everything you learn is tool-agnostic, with universal principles you can apply to any data stack. In Everyday Data Visualization you’ll learn important design principles for the most common data visualizations: Harness the power of perception to guide a user’s attention Bring data to life with color and typography Choose the best chart types for your data story Design for interactive visualizations Keep the user’s needs first throughout your projects This book gives you the tools you need to bring your data to life with clarity, precision, and flair. You’ll learn how human brains perceive and process information, wield modern accessibility standards, get the basics of color theory and typography, and more. About the Technology Even mundane presentations like charts, dashboards, and infographics can become engaging and inspiring data stories! This book shows you how to upgrade the visualizations you create every day by improving the layout, typography, color, and accessibility. You’ll discover timeless principles of design that help you highlight important features, compensate for missing information, and interact with live data flows. About the Book Everyday Data Visualization guides you through basic graphic design for the most common types of data visualization. You’ll learn how to enhance charts with color, encourage users to interact and explore data and create visualizations accessible to everyone. Along the way, you’ll practice each new skill as you take a dashboard project from research to publication. What's Inside Bring data to life with color and typography Choose the best chart types for your data story Design interactive visualizations About the Reader For readers experienced with data analysis tools. About the Author Desireé Abbott has over a decade of experience in product analytics, business intelligence, science, design, and software engineering. The technical editor on this book was Michael Petrey. Quotes A delightful blend of data viz principles, guidance, and design tips. The treasure trove of insights I wish I had years ago! - Alli Torban, Author of Chart Spark With vibrant enthusiasm and engaging conversational style, this book shines. - RJ Andrews, data storyteller Elegantly simplifies complex concepts, making them accessible even to beginners. An enlightening journey. - Renato Sinohara, Westwing Group SE Desiree’s approachable writing style makes it easy to dive straight into this book, and you’re in deep before you even know it. I guarantee you’ll learn plenty. - Neil Richards, 5xTableau Visionary, Author of Questions in Dataviz

This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhance their visualization skills, it explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by Gemini-generated code samples. Companion files with source code and figures are available for downloading. FEATURES: Covers Python-based data visualization libraries and techniques Includes practical examples and Gemini-generated code samples for efficient learning Integrates Google Gemini for advanced data visualization capabilities Sets up a conducive development environment for a seamless coding experience Includes companion files for downloading with source code and figures

Install data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sources Organize and analyze data Use data to tell a story with Tableau Expand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data. Get ready to watch as your career in data takes off.

This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.

SAS ODS graphics users will learn in this book how to visually understand and communicate the significance of data to deliver images for quick and easy insight, with precise numbers. Many charts or plots require the viewer to run the eye from a bar end or plot point to some point on an axis, and then to interpolate between tick marks to estimate the value. Some design choices can lead to wrong conclusions or mistaken impressions. Graphic software relies on defaults to deliver something if you make a minimal effort, but that something is not likely to be exactly what you want. Visual Data Insights Using SAS ODS Graphics provides examples using experience-based design principles. It presents examples of bar charts, pie charts, and trend lines or time series plots, the graph types commonly used in business, other organizations, and the media for visual insight into data. Newer graphs are also included: dot plots, needle plots, waterfall charts, butterflycharts, heat maps, bubble plots, step plots, high-low plots, and donut charts. In addition, there are basic tools of statistics: scatter plots, box plots, histograms, fit and confidence plots, and distributions. Author LeRoy Bessler introduces unique creations, including sparsely annotated time series, maximally informative bar charts, better box plots, histograms based on interesting atypical rationales, and much more. The examples use SAS sample data sets as input. Any SAS user can experiment with the code presented to see what else is possible, or adapt it to repurpose the design and apply it with a customized version of that code. What You’ll Learn Create graphs that are easily and quickly interpreted, and without ambiguity Supply precise data values that are correct on the graph and correctly associated with the graphic visual elements Take advantage of widely applicable (but not necessarily available elsewhere) design examples Avoid bad practices that are encouraged by poor examples elsewhere Get past sub-optimal designs and results that are built into software defaults Take advantage of less familiar capabilities available in the software Who This Book Is For SAS software users who want to understand their data and/or visually deliver their results

How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries

What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https://www.functionalaestheticsbook.com

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well. Source code is available at github.com/Apress/beg-data-science-r4. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Easy-to-follow visual walkthrough of every important part of the Google Cloud Platform The Google Cloud Platform incorporates dozens of specialized services that enable organizations to offload technological needs onto the cloud. From routine IT operations like storage to sophisticated new capabilities including artificial intelligence and machine learning, the Google Cloud Platform offers enterprises the opportunity to scale and grow efficiently. In Visualizing Google Cloud: Illustrated References for Cloud Engineers & Architects, Google Cloud expert Priyanka Vergadia delivers a fully illustrated, visual guide to matching the best Google Cloud Platform services to your own unique use cases. After a brief introduction to the major categories of cloud services offered by Google, the author offers approximately 100 solutions divided into eight categories of services included in Google Cloud Platform: Compute Storage Databases Data Analytics Data Science, Machine Learning and Artificial Intelligence Application Development and Modernization with Containers Networking Security You’ll find richly illustrated flowcharts and decision diagrams with straightforward explanations in each category, making it easy to adopt and adapt Google’s cloud services to your use cases. With coverage of the major categories of cloud models—including infrastructure-, containers-, platforms-, functions-, and serverless—and discussions of storage types, databases and Machine Learning choices, Visualizing Google Cloud: Illustrated References for Cloud Engineers & Architects is perfect for Every Google Cloud enthusiast, of course. It is for anyone who is planning a cloud migration or new cloud deployment. It is for anyone preparing for cloud certification, and for anyone looking to make the most of Google Cloud. It is for cloud solutions architects, IT decision-makers, and cloud data and ML engineers. In short, this book is for YOU.

Create stunning and complex visualizations using the amazing Charticulator custom visuals in Power BI. Charticulator offers users immense power to generate visuals and graphics. To a beginner, there are myriad settings and options that can be combined in what feels like an unlimited number of combinations, giving it the unfair label, “the DAX of the charting world”. This is not true. This book is your start-to-finish guide to using Charticulator, a custom visualization software that Microsoft integrated into Power BI Desktop so that Power BI users can create incredibly powerful, customized charts and graphs. You will learn the concepts that underpin the software, journeying through every building block of chart design, enabling you to combine these parts to create spectacular visuals that represent the story of your data. Unlike other custom Power BI visuals, Charticulator runs in a separate application window within Power BI with its own interface and requires a different set of interactions and associated knowledge. This book covers the ins and outs of all of them. What You Will Learn Generate inspirational and technically competent visuals with no programming or other specialist technical knowledge Create charts that are not restricted to conventional chart types such as bar, line, or pie Limit the use of diverse Power BI custom visuals to one Charticulator custom visual Alleviate frustrations with the limitations of default chart types in Power BI, such as being able to plot data on only one categorical axis Use a much richer set of options to compare different sets of data Re-use your favorite or most often used chart designs with Charticulator templates Who This Book Is For The average Power BI user. It assumes no prior knowledge on the part of the reader other than being able to open Power BI desktop, import data, and create a simple Power BI visual. User experiences may vary, from people attending a Power BI training course to those with varying skills and abilities, from SQL developers and advanced Excel users to people with limited data analysis experience and technical skills.

Managing and Visualizing Your BIM Data is an essential guide for AEC professionals who want to harness the power of data to enhance their projects. Designed with a hands-on approach, this book delves into using Autodesk Dynamo for data collection and Microsoft Power BI for creating insightful dashboards. By the end, readers will be adept at connecting BIM models to interactive visualizations. What this Book will help me do Gain a deep understanding of data collection workflows in Autodesk Dynamo. Learn to connect Building Information Modeling (BIM) data to Power BI dashboards. Master the basics and advanced features of Dynamo for BIM data management. Create dynamic and visually appealing Power BI dashboards for AEC projects. Explore real-world use cases with expert-guided hands-on examples. Author(s) The authors, None Pellegrino, None Bottiglieri, None Crump, None Pieper, and None Touil, are experienced professionals in the AEC and software development industries. With extensive backgrounds in Building Information Modeling (BIM) and data visualization, they bring practical insights combined with a passion for teaching. Their approach ensures readers not only learn the tools but also understand the reasoning behind best practices. Who is it for? This book is ideal for BIM managers and coordinators, design technology managers, and other Architecture, Engineering, and Construction (AEC) professionals. Readers with a foundational knowledge of BIM will find it particularly beneficial for enhancing their data analysis and reporting capabilities. If you're aiming to elevate your skill set in managing BIM data and creating impactful visualizations, this guide is for you.

Use R 4, RStudio, Tidyverse, and Shiny to interrogate and analyze your data, and then use the D3 JavaScript library to format and display that data in an elegant, informative, and interactive way. You will learn how to gather data effectively, and also how to understand the philosophy and implementation of each type of chart, so as to be able to represent the results visually. With the popularity of the R language, the art and practice of creating data visualizations is no longer the preserve of mathematicians, statisticians, or cartographers. As technology leaders, we can gather metrics around what we do and use data visualizations to communicate that information. Pro Data Visualization Using R and JavaScript combines the power of the R language with the simplicity and familiarity of JavaScript to display clear and informative data visualizations. Gathering and analyzing empirical data is the key to truly understanding anything. We can track operational metrics to quantify the health of our products in production. We can track quality metrics of our projects, and even use our data to identify bad code. Visualizing this data allows anyone to read our analysis and easily get a deep understanding of the story the data tells. This book makes the R language approachable, and promotes the idea of data gathering and analysis mostly using web interfaces. What You Will Learn Carry out data visualization using R and JavaScript Use RStudio for data visualization Harness Tidyverse data pipelines Apply D3 and R Notebooks towards your data Work with the R Plumber API generator, Shiny, and more Who This Book Is For Programmers and data scientists/analysts who have some prior experience with R and JavaScript.

Integrate D3.js into a React TypeScript project and create a chart component working in harmony with React. This book will show you how utilize D3 with React to bring life to your charts. Seasoned author Elad Elrom will show you how to create simple charts such as line, bar, donut, scatter, histogram and others, and advanced charts such as a world map and force charts. You'll also learn to share the data across your components and charts using React Recoil state management. Then integrate third-party chart libraries that are built on D3 such as Rechart, Visx, Nivo, React-vi, and Victory and in the end deploy your chart as a server or serverless app on popular platforms. React and D3 are two of the most popular frameworks in their respective areas – learn to bring them together and take your storytelling to the next level. What You'll Learn Set up your project with React, TypeScript and D3.js Create simple and advanced D3.js charts Work with complex charts such as world and force charts Integrate D3 data with React state management Improve the performance of your D3 components Deploy as a server or serverless app and debug test Who This Book Is ForReaders that already have basic knowledge of React, HTML, CSS and JavaScript.

Not a data expert? Here’s an engaging and entertaining guide to interpreting and drawing insights from any chart, graph, or other data visualization you’ll encounter. You’re a business professional, not a data scientist. How do you make heads or tails of the data visualizations that come across your desk—let alone make critical business decisions based on the information they’re designed to convey? In The Big Picture, top data visualization consultant Steve Wexler provides the tools for developing the graphical literacy you need to understand the data visualizations that are flooding your inbox—and put that data to use. Packed with the best four-color examples created in Excel, Tableau, Power BI, and Qlik, among others, this one-stop resource empowers you to extract the most important information from data visualizations quickly and accurately, act on key insights, solve problems, and make the right decisions for your organization every time.

Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub. Hands-On Data Visualization takes you step-by-step through tutorials, real-world examples, and online resources. This practical guide is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and maps Learn key data visualization concepts to help you choose the right tools Convert and transform tabular and spatial data to tell your data story Edit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others

Master the syntax for working with R’s plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This book will take advantage of the new features added to R 4 where appropriate including a refreshed color palette for charts, Cairo graphics with more fonts/symbols, and improved performance from grid graphics including ggplot 2 rendering speed. Visualizing Data in R 4 starts with an introduction and then is split into two parts and six appendices. Part I covers the function plot() and the ancillary functions you can use with plot(). You’ll also see the functions par() and layout(), providing for multiple plots on a page. Part II goes over the basics of using the functions qplot() and ggplot() in the package ggplot2. The default plots generated by the functions qplot() and ggplot() give more sophisticated-looking plots than the default plots done by plot() and are easier to use, but the function plot() is more flexible. Both plot() and ggplot() allow for many layers to a plot. The six appendices will cover plots for contingency tables, plots for continuous variables, plots for data with a limited number of values, functions that generate multiple plots, plots for time series analysis, and some miscellaneous plots. Some of the functions that will be in the appendices include functions that generate histograms, bar charts, pie charts, box plots, and heatmaps. What You Will Learn Use R to create informative graphics Master plot(), qplot(), and ggplot() Discover the canned graphics functions in stats and graphics Format plots generated by plot() and ggplot() Who This Book Is For Those in data science who use R. Some prior experience with R or data science is recommended.

The only data visualization book written by and for health and healthcare professionals. In health and healthcare, data and information are coming at organizations faster than they can consume and interpret it. Health providers, payers, public health departments, researchers, and health information technology groups know the ability to analyze and communicate this vast array of data in a clear and compelling manner is paramount to success. However, they simply cannot find experienced people with the necessary qualifications. The quickest (and often the only) route to meeting this challenge is to hire smart people and train them. Visualizing Health and Healthcare Data: Creating Clear and Compelling Visualizations to "See how You're Doing" is a one-of-a-kind book for health and healthcare professionals to learn the best practices of data visualization specific to their field. It provides a high-level summary of health and healthcare data, an overview of relevant visual intelligence research, strategies and techniques to gather requirements, and how to build strong teams with the expertise required to create dashboards and reports that people love to use. Clear and detailed explanations of data visualization best practices will help you understand the how and the why. Learn how to build beautiful and useful data products that deliver powerful insights for the end user Follow along with examples of data visualization best practices, including table and graph design for health and healthcare data Learn the difference between dashboards, reports, multidimensional exploratory displays and infographics (and why it matters) Avoid common mistakes in data visualization by learning why they do not work and better ways to display the data Written by a top leader in the field of health and healthcare data visualization, this book is an excellent resource for top management in healthcare, as well as entry-level to experienced data analysts in any health-related organization.

Elevate your storytelling with SAS Visual Analytics Data visualization is the gateway to artificial intelligence (AI) and big data. Insightful Data Visualization with SAS Viya shows how the latest SAS Viya tools can be used to create data visualizations in an easier, smarter, and more engaging way than ever before. SAS Visual Analytics combined with human creativity can produce endless possibilities. In this book, you will learn tips and techniques for getting the most from your SAS Visual Analytics investment. From beginners to advanced SAS users, this book has something for everyone. Use AI wizards to create data visualization automatically, learn to use advanced analytics in your dashboards to surface smarter insights, and learn to extend SAS Visual Analytics with advanced integrations and options. Topics covered in this book include: SAS Visual Analytics Data visualization with SAS Reports and dashboards SAS code examples Self-service analytics SAS data access Extending SAS beyond drag and drop

Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations. You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets. The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. WithPractical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface. What You'll Learn Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas Who This Book Is For Data Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.

In "The Data Visualization Workshop," you will explore the fascinating world of data visualization and learn how to turn raw data into compelling visualizations that clearly communicate your insights. This book provides practical guidance and hands-on exercises to familiarize you with essential topics such as plotting techniques and interactive visualizations using Python. What this Book will help me do Prepare and clean raw data for visualization using NumPy and pandas. Create effective and visually appealing charts using libraries like Matplotlib and Seaborn. Generate geospatial visualizations utilizing tools like geoplotlib. Develop interactive visualizations for web integration with the Bokeh library. Apply visualization techniques to real-world data analysis scenarios, including stock data and Airbnb datasets. Author(s) Mario Döbler and Tim Großmann are experienced authors and professionals in the field of Python programming and data science. They bring a wealth of knowledge and practical insights to data visualization. Through their collaborative efforts, they aim to empower readers with the skills to create compelling data visualizations and uncover meaningful data narratives. Who is it for? This book is ideal for beginners new to data visualization, as well as developers and data scientists seeking to enhance their practical skills. It is approachable for readers without prior visualization experience but assumes familiarity with Python programming and basic mathematics. If you're eager to bring your data to life in insightful and engaging ways, this book is for you.

Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft’s continually evolving visualization product. New in this edition is help with storytelling—adapted to PCs, tablets, and smartphones—and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience’s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurate, and interactive information with Microsoft’s free self-service BI tool. This book shows you how to choose from a wide range of built-in and third-party visualization types so that your message is always enhanced. You will be able to deliver those results on PCs, tablets, and smartphones, as well as share results via the cloud. The book helps you save time by preparing the underlying data correctly without needing an IT department to prepare it for you. What You Will Learn Deliver attention-grabbing information, turning data into insight Find new insights as you chop and tweak your data as never before Build a data narrative through interactive reports with drill-through and cross-page slicing Mash up data from multiple sources into a cleansed and coherent data model Build interdependent charts, maps, and tables to deliver visually stunninginformation Create dashboards that help in monitoring key performance indicators of your business Adapt delivery to mobile devices such as phones and tablets Who This Book Is For Power users who are ready to step up to the big leagues by going beyond what Microsoft Excel by itself can offer. The book also is for line-of-business managers who are starved for actionable data needed to make decisions about their business. And the book is for BI analysts looking for an easy-to-use tool to analyze data and share results with C-suite colleagues they support.