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| data visualization cheat sheet: Cool Infographics Randy Krum, 2013-10-23 Make information memorable with creative visual design techniques Research shows that visual information is more quickly and easily understood, and much more likely to be remembered. This innovative book presents the design process and the best software tools for creating infographics that communicate. Including a special section on how to construct the increasingly popular infographic resume, the book offers graphic designers, marketers, and business professionals vital information on the most effective ways to present data. Explains why infographics and data visualizations work Shares the tools and techniques for creating great infographics Covers online infographics used for marketing, including social media and search engine optimization (SEO) Shows how to market your skills with a visual, infographic resume Explores the many internal business uses of infographics, including board meeting presentations, annual reports, consumer research statistics, marketing strategies, business plans, and visual explanations of products and services to your customers With Cool Infographics, you'll learn to create infographics to successfully reach your target audience and tell clear stories with your data. |
| data visualization cheat sheet: Better Presentations Jonathan Schwabish, 2016-11-15 Whether you are a university professor, researcher at a think tank, graduate student, or analyst at a private firm, chances are that at some point you have presented your work in front of an audience. Most of us approach this task by converting a written document into slides, but the result is often a text-heavy presentation saddled with bullet points, stock images, and graphs too complex for an audience to decipher—much less understand. Presenting is fundamentally different from writing, and with only a little more time, a little more effort, and a little more planning, you can communicate your work with force and clarity. Designed for presenters of scholarly or data-intensive content, Better Presentations details essential strategies for developing clear, sophisticated, and visually captivating presentations. Following three core principles—visualize, unify, and focus—Better Presentations describes how to visualize data effectively, find and use images appropriately, choose sensible fonts and colors, edit text for powerful delivery, and restructure a written argument for maximum engagement and persuasion. With a range of clear examples for what to do (and what not to do), the practical package offered in Better Presentations shares the best techniques to display work and the best tactics for winning over audiences. It pushes presenters past the frustration and intimidation of the process to more effective, memorable, and persuasive presentations. |
| data visualization cheat sheet: Beautiful Visualization Julie Steele, Noah Iliinsky, 2010-04-23 Visualization is the graphic presentation of data -- portrayals meant to reveal complex information at a glance. Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding. This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world. Explore the importance of storytelling with a simple visualization exercise Learn how color conveys information that our brains recognize before we're fully aware of it Discover how the books we buy and the people we associate with reveal clues to our deeper selves Recognize a method to the madness of air travel with a visualization of civilian air traffic Find out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include: Nick Bilton,Michael E. Driscoll,Jonathan Feinberg,Danyel Fisher,Jessica Hagy,Gregor Hochmuth,Todd Holloway,Noah Iliinsky,Eddie Jabbour,Valdean Klump,Aaron Koblin,Robert Kosara,Valdis Krebs,JoAnn Kuchera-Morin et al.,Andrew Odewahn,Adam Perer,Anders Persson,Maximilian Schich,Matthias Shapiro,Julie Steele,Moritz Stefaner,Jer Thorp,Fernanda Viegas,Martin Wattenberg,and Michael Young. |
| data visualization cheat sheet: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data visualization cheat sheet: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 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. You'll 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 you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data visualization cheat sheet: Microsoft Power BI For Dummies Jack A. Hyman, 2022-02-08 Reveal the insights behind your company’s data with Microsoft Power BI Microsoft Power BI allows intuitive access to data that can power intelligent business decisions and insightful strategies. The question is, do you have the Power BI skills to make your organization’s numbers spill their secrets? In Microsoft Power BI For Dummies, expert lecturer, consultant, and author Jack Hyman delivers a start-to-finish guide to applying the Power BI platform to your own firm’s data. You’ll discover how to start exploring your data sources, build data models, visualize your results, and create compelling reports that motivate decisive action. Tackle the basics of Microsoft Power BI and, when you’re done with that, move on to advanced functions like accessing data with DAX and app integrations Guide your organization’s direction and decisions with rock-solid conclusions based on real-world data Impress your bosses and confidently lead your direct reports with exciting insights drawn from Power BI’s useful visualization tools It’s one thing for your company to have data at its disposal. It’s another thing entirely to know what to do with it. Microsoft Power BI For Dummies is the straightforward blueprint you need to apply one of the most powerful business intelligence tools on the market to your firm’s existing data. |
| data visualization cheat sheet: Effective Data Visualization Stephanie D. H. Evergreen, 2019-04-03 NOW IN FULL COLOR! Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate their data findings. This comprehensive how-to guide functions as a set of blueprints—supported by both research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for building the chosen graph in Excel. Now in full color with new examples throughout, the Second Edition includes a revamped chapter on qualitative data, nine new quantitative graph types, new shortcuts in Excel, and an entirely new chapter on Sharing Your Data With the World, which provides advice on using dashboards. New from Stephanie Evergreen! The Data Visualization Sketchbook provides advice on getting started with sketching and offers tips, guidance, and completed sample sketches for a number of reporting formats. Bundle Effective Data Visualization, 2e, and The Data Visualization Sketchbook, using ISBN 978-1-5443-7178-8! |
| data visualization cheat sheet: Excel 2007 Dashboards and Reports For Dummies Michael Alexander, 2011-03-16 What’s the use of putting out reports that no one reads? Properly created dashboards are graphical representations that put data in a context for your audience, and they look really cool! How cool? You’ll find out when you see the dazzling examples in Excel 2007 Dashboards & Reports For Dummies. And, before long, everyone’s eyes will be riveted to your dashboards and reports too! This revolutionary guide shows you how to turn Excel into your own personal Business Intelligence tool. You’ll learn the fundamentals of using Excel 2007 to go beyond simple tables to creating dashboard-studded reports that wow management. Get ready to catch dashboard fever as you find out how to use basic analysis techniques, build advanced dashboard components, implement advanced reporting techniques, and import external date into your Excel reports. Discover how to: Unleash the power of Excel as a business intelligence tool Create dashboards that communicate and get noticed Think about your data in a new way Present data more effectively and increase the value of your reports Create dynamic labels that support visualization Represent time and seasonal trending Group and bucket data Display and measure values versus goals Implement macro-charged reporting Using Excel 2007 as a BI tool is the most cost-efficient way for organizations of any size create powerful and insightful reports and distribute throughout the enterprise. And Excel 2007 Dashboards and Reports for Dummies is the fastest you for you to catch dashboard fever! |
| data visualization cheat sheet: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data visualization cheat sheet: Effective Data Storytelling Brent Dykes, 2019-12-10 Master the art and science of data storytelling—with frameworks and techniques to help you craft compelling stories with data. The ability to effectively communicate with data is no longer a luxury in today’s economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative—to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories. Narratives are more powerful than raw statistics, more enduring than pretty charts. When done correctly, data stories can influence decisions and drive change. Most other books focus only on data visualization while neglecting the powerful narrative and psychological aspects of telling stories with data. Author Brent Dykes shows you how to take the three central elements of data storytelling—data, narrative, and visuals—and combine them for maximum effectiveness. Taking a comprehensive look at all the elements of data storytelling, this unique book will enable you to: Transform your insights and data visualizations into appealing, impactful data stories Learn the fundamental elements of a data story and key audience drivers Understand the differences between how the brain processes facts and narrative Structure your findings as a data narrative, using a four-step storyboarding process Incorporate the seven essential principles of better visual storytelling into your work Avoid common data storytelling mistakes by learning from historical and modern examples Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals is a must-have resource for anyone who communicates regularly with data, including business professionals, analysts, marketers, salespeople, financial managers, and educators. |
| data visualization cheat sheet: Fundamentals of Data Visualization Claus O. Wilke, 2019-03-18 Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story |
| data visualization cheat sheet: Creating More Effective Graphs Naomi B. Robbins, 2005 A succinct and highly readable guide to creating effective graphs The right graph can be a powerful tool for communicating information, improving a presentation, or conveying your point in print. If your professional endeavors call for you to present data graphically, here's a book that can help you do it more effectively. Creating More Effective Graphs gives you the basic knowledge and techniques required to choose and create appropriate graphs for a broad range of applications. Using real-world examples everyone can relate to, the author draws on her years of experience in graphical data analysis and presentation to highlight some of today's most effective methods. In clear, concise language, the author answers such common questions as: What constitutes an effective graph for communicating data? How do I choose the type of graph that is best for my data? How do I recognize a misleading graph? Why do some graphs have logarithmic scales? In no time you'll graduate from bar graphs and pie charts to graphs that illuminate data like: Dot plots Box plots Scatterplots Linked micromaps Trellis displays Mosaic plots Month plots Scatterplot matrices . . . most of them requiring only inexpensive, easily downloadable software. Whether you're a novice at graphing or already use graphs in your work but want to improve them, Creating More Effective Graphs will help you develop the kind of clear, accurate, and well-designed graphs that will allow your data to be understood. |
| data visualization cheat sheet: Data Points Nathan Yau, 2013-03-25 A fresh look at visualization from the author of Visualize This Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Shares intriguing ideas from Nathan Yau, author of Visualize This and creator of flowingdata.com, with over 66,000 subscribers Focuses on visualization, data graphics that help viewers see trends and patterns they might not otherwise see in a table Includes examples from the author's own illustrations, as well as from professionals in statistics, art, design, business, computer science, cartography, and more Examines standard rules across all visualization applications, then explores when and where you can break those rules Create visualizations that register at all levels, with Data Points: Visualization That Means Something. |
| data visualization cheat sheet: People Analytics For Dummies Mike West, 2019-03-19 Maximize performance with better data Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them. People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce. Start a people analytics project Work with qualitative data Collect data via communications Find the right tools and approach for analyzing data If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier. |
| data visualization cheat sheet: The Grammar of Graphics Leland Wilkinson, 2013-03-09 Written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. It was designed for a distributed computing environment, with special attention given to conserving computer code and system resources. While the tangible result of this work is a Java production graphics library, the text focuses on the deep structures involved in producing quantitative graphics from data. It investigates the rules that underlie pie charts, bar charts, scatterplots, function plots, maps, mosaics, and radar charts. These rules are abstracted from the work of Bertin, Cleveland, Kosslyn, MacEachren, Pinker, Tufte, Tukey, Tobler, and other theorists of quantitative graphics. |
| data visualization cheat sheet: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| data visualization cheat sheet: Better Data Visualizations Jonathan Schwabish, 2021-02-09 Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message. |
| data visualization cheat sheet: Predictive Analytics For Dummies Anasse Bari, Mohamed Chaouchi, Tommy Jung, 2014-03-06 Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies. |
| data visualization cheat sheet: Data Visualization with Python and JavaScript Kyran Dale, 2016-06-30 Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library |
| data visualization cheat sheet: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data visualization cheat sheet: Effective Data Visualization Stephanie D. H. Evergreen, 2016-04-22 Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints—supported by research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel. |
| data visualization cheat sheet: Research Data Visualization and Scientific Graphics MARTINS. ZAUMANIS, 2021-07-22 Poor data charts and graphics are hindering the effective transfer of knowledge in academia. To get your research noticed and respected, this step-by-step guide will demonstrate essential techniques for creating informative scientific visualizations. By reading this book, you will learn: ✓ Eight bulletproof progressions for turning research data into convincing charts ✓ Eleven graphical features for converting scientific concepts into self-explanatory diagrams and scientific illustrations ✓ Straightforward visualization principles that help to interpret research results The straightforward approaches presented in this book are designed with efficiency in mind. By reviewing a variety of good and bad examples, you will learn how to include scientific visualization in your research communication routine. No artistic talent required. Clear data charts and informative graphics draw citations to papers, make presentations memorable, and encourage reviewers to approve research proposals. This book will show you how to make it happen. What's included: 1) Web resources, including a comparison of different software and online tools for data visualization and scientific illustrations 2) Two printable cheat sheets that summarize the advice from the book 3) A book full of actionable advice for efficiently creating convincing data charts and illuminating scientific graphics About the author My name is Martins Zaumanis and I am obsessed with finding ways to communicate science visually. For example, during the last half a year of my Ph.D., I spent almost every evening hand-drawing my research just so that I could present a memorable TEDx talk. But I don't expect you to become obsessed. Quite the opposite: I developed an approach that will allow you to create great data charts and scientific illustrations without taking away time from what you love most - research |
| data visualization cheat sheet: Data Lakes For Dummies Alan R. Simon, 2021-07-14 Take a dive into data lakes “Data lakes” is the latest buzz word in the world of data storage, management, and analysis. Data Lakes For Dummies decodes and demystifies the concept and helps you get a straightforward answer the question: “What exactly is a data lake and do I need one for my business?” Written for an audience of technology decision makers tasked with keeping up with the latest and greatest data options, this book provides the perfect introductory survey of these novel and growing features of the information landscape. It explains how they can help your business, what they can (and can’t) achieve, and what you need to do to create the lake that best suits your particular needs. With a minimum of jargon, prolific tech author and business intelligence consultant Alan Simon explains how data lakes differ from other data storage paradigms. Once you’ve got the background picture, he maps out ways you can add a data lake to your business systems; migrate existing information and switch on the fresh data supply; clean up the product; and open channels to the best intelligence software for to interpreting what you’ve stored. Understand and build data lake architecture Store, clean, and synchronize new and existing data Compare the best data lake vendors Structure raw data and produce usable analytics Whatever your business, data lakes are going to form ever more prominent parts of the information universe every business should have access to. Dive into this book to start exploring the deep competitive advantage they make possible—and make sure your business isn’t left standing on the shore. |
| data visualization cheat sheet: Data-Driven Storytelling Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale, 2018-03-28 This book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners. |
| data visualization cheat sheet: Excel Dashboards and Reports Michael Alexander, John Walkenbach, 2010-08-06 The go to resource for how to use Excel dashboards and reports to better conceptualize data Many Excel books do an adequate job of discussing the individual functions and tools that can be used to create an “Excel Report.” What they don’t offer is the most effective ways to present and report data. Offering a comprehensive review of a wide array of technical and analytical concepts, Excel Reports and Dashboards helps Excel users go from reporting data with simple tables full of dull numbers, to presenting key information through the use of high-impact, meaningful reports and dashboards that will wow management both visually and substantively. Details how to analyze large amounts of data and report the results in a meaningful, eye-catching visualization Describes how to use different perspectives to achieve better visibility into data, as well as how to slice data into various views on the fly Shows how to automate redundant reporting and analyses Part technical manual, part analytical guidebook, Excel Dashboards and Reports is the latest addition to the Mr. Spreadsheet’s Bookshelf series and is the leading resource for learning to create dashboard reports in an easy-to-use format that’s both visually attractive and effective. |
| data visualization cheat sheet: Visualization Analysis and Design Tamara Munzner, 2014-12-01 Learn How to Design Effective Visualization SystemsVisualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques |
| data visualization cheat sheet: Pandas Cookbook Theodore Petrou, 2017-10-23 Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Style and approach The author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data. |
| data visualization cheat sheet: Raspberry Pi Projects For Dummies Mike Cook, Jonathan Evans, Brock Craft, 2015-07-13 Join the Raspberry revolution with these fun and easy Pi projects The Raspberry Pi has opened up a whole new world of innovation for everyone from hardware hackers and programmers to students, hobbyists, engineers, and beyond. Featuring a variety of hands-on projects, this easy-to-understand guide walks you through every step of the design process and will have you creating like a Raspberry Pi pro in no time. You’ll learn how to prepare your workspace, assemble the necessary tools, work with test equipment, and find your way around the Raspberry Pi before moving on to a series of fun, lively projects that brings some power to your plain ol’ Pi. Introduces Raspberry Pi basics and gives you a solid understanding of all the essentials you’ll need to take on your first project Includes an array of fun and useful projects that show you how to do everything from creating a magic light wand to enhancing your designs with Lego sensors, installing and writing games for the RISC OS, building a transistor tester, and more Provides an easy, hands-on approach to learning more about electronics, programming, and interaction design for Makers and innovators of all ages Bring the power of Pi to your next cool creation with Raspberry Pi Projects For Dummies! |
| data visualization cheat sheet: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| data visualization cheat sheet: Data Visualization, Part 2 Tarek Azzam, Stephanie Evergreen, 2013-12-31 This issue delivers concrete suggestions for optimally using data visualization in evaluation, as well as suggestions for best practices in data visualization design. It focuses on specific quantitative and qualitative data visualization approaches that include data dashboards, graphic recording, and geographic information systems (GIS). Readers will get a step-by-step process for designing an effective data dashboard system for programs and organizations, and various suggestions to improve their utility. The next section illustrates the role that graphic recording can play in helping programs and evaluators understand and communicate the mission and impact that an intervention is having in a democratic and culturally competent way. The GIS section provides specific examples of how mapped data can be used to understand program implementation and effectiveness, and the influence that the environment has on these outcomes. Discusses best practices that inform and shape our data visualization design choices Highlights the best use of each tool/approach Provides suggestions for effective practice Discuss the strengths and limitations of each approach in evaluation practice This is the 140th volume of the Jossey-Bass quarterly report series New Directions for Evaluation, an official publication of the American Evaluation Association. |
| data visualization cheat sheet: Adobe Analytics For Dummies David Karlins, Eric Matisoff, 2019-04-02 Use Adobe Analytics as a marketer —not a programmer! If you're a marketer in need of a non-technical, beginner's reference to using Adobe Analytics, this book is the perfect place to start. Adobe Analytics For Dummies arms you with a basic knowledge of the key features so that you can start using it quickly and effectively. Even if you're a digital marketer who doesn't have their hands in data day in and day out, this easy-to-follow reference makes it simple to utilize Adobe Analytics. With the help of this book, you'll better understand how your marketing efforts are performing, converting, being engaged with, and being shared in the digital space. Evaluate your marketing strategies and campaigns Explore implementation fundamentals and report architecture Apply Adobe Analytics to multiple sources Succeed in the workplace and expand your marketing skillset The marketing world is continually growing and evolving, and Adobe Analytics For Dummies will help you stay ahead of the curve. |
| data visualization cheat sheet: Graph Algorithms Mark Needham, Amy E. Hodler, 2019-05-16 Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark |
| data visualization cheat sheet: Statistics and Data Visualization Using R David S. Brown, 2021-08-24 Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research. |
| data visualization cheat sheet: Data Visualisation Andy Kirk, 2019-07-08 With over 200 images and extensive how-to and how-not-to examples, the new edition of the book The Financial Times voted one of the ‘six best books for data geeks’ has everything students and scholars need to understand and create effective data visualisations. |
| data visualization cheat sheet: Information is Beautiful David McCandless, 2009 Miscellaneous facts and ideas are interconnected and represented in a visual format, a visual miscellaneum, which represents a series of experiments in making information approachable and beautiful -- from p.007 |
| data visualization cheat sheet: Data at Work Jorge Camões, 2016-04-08 Information visualization is a language. Like any language, it can be used for multiple purposes. A poem, a novel, and an essay all share the same language, but each one has its own set of rules. The same is true with information visualization: a product manager, statistician, and graphic designer each approach visualization from different perspectives. Data at Work was written with you, the spreadsheet user, in mind. This book will teach you how to think about and organize data in ways that directly relate to your work, using the skills you already have. In other words, you don’t need to be a graphic designer to create functional, elegant charts: this book will show you how. Although all of the examples in this book were created in Microsoft Excel, this is not a book about how to use Excel. Data at Work will help you to know which type of chart to use and how to format it, regardless of which spreadsheet application you use and whether or not you have any design experience. In this book, you’ll learn how to extract, clean, and transform data; sort data points to identify patterns and detect outliers; and understand how and when to use a variety of data visualizations including bar charts, slope charts, strip charts, scatter plots, bubble charts, boxplots, and more. Because this book is not a manual, it never specifies the steps required to make a chart, but the relevant charts will be available online for you to download, with brief explanations of how they were created. |
| data visualization cheat sheet: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data visualization cheat sheet: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover. |
| data visualization cheat sheet: Interactive Data Visualization with Python Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar, 2020-04-14 Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas. |
| data visualization cheat sheet: Data Visualization Kieran Healy, 2018-12-18 An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
