DataIsBeautiful What's The Big Data? | The evolving IT landscape Extreme Presentations Digital Data Science and the Analytics Warehouse Here at EY, we spent a good chunk of time last year helping client’s build out digital capabilities in the analytics warehouse. In some cases, that meant building traditional data stores and traditional data models in Oracle and SQL-Server. But for the most part, it meant building analytics capabilities on top of Hadoop systems; that’s been bracing, difficult, sometimes frustrating, and always interesting. I remain convinced that the future of analytics lies in working at a very detailed level of the data (though I think there’s much to be debated about exactly which level of detail is right). Over the course of 2015, I hope to tackle some of the key issues in pursuing this type of new technology analytics warehouse. Modeling Digital Data: What does it mean to create a data model in the big data world? Perhaps this sounds too theoretical. Statistical ETL: We often describe a basic digital data model as having five levels (ranging from hit level up to visitor level).
storytelling with data Ann's Blog | Visualization Visualizing data through charts, graphs, and diagrams helps you deliver bite-sized information that viewers will understand at a glance and retain for the long run. During my workshops, webinars, and training videos, we focus on researcher-specific considerations: designing with stakeholders’ information needs front and center, using readily available software like Microsoft Excel, and thinking through dozens of chart types—dot plots, small multiples, heat maps, and more—that can be applied to the social sciences. My goal is to equip you with critical thinking skills and technical know-how create visualizations faster and easier than you ever thought was possible. Read my latest articles about selecting appropriate chart types, applying best practices to your charts, and more. View excerpts from my latest conference presentations and read my articles that are guest-published through other organizations’ blogs. Is your team overdue to step up your data game? Sample Agenda Length Location
The Functional Art: An Introduction to Information Graphics and Visualization Visual Business Intelligence We typically think of quantitative scales as linear, with equal quantities from one labeled value to the next. For example, a quantitative scale ranging from 0 to 1000 might be subdivided into equal intervals of 100 each. Linear scales seem natural to us. If we took a car trip of 1000 miles, we might imagine that distance as subdivided into ten 100 mile segments. It isn’t likely that we would imagine it subdivided into four logarithmic segments consisting of 1, 9, 90, and 900 mile intervals. Logarithms and their scales are quite useful in mathematics and at times in data analysis, but they are only useful for presenting data on those relatively rare cases when addressing an audience that consists of those who have been trained to think in logarithms. For my own analytical purposes, I use logarithmic scales primarily for a single task: to compare rates of change. I decided to write this blog piece when I ran across the following graph in Steven Pinker’s new book Enlightenment Now:
The Work of Edward Tufte and Graphics Press Edward Tufte is a statistician and artist, and Professor Emeritus of Political Science, Statistics, and Computer Science at Yale University. He wrote, designed, and self-published 4 classic books on data visualization. The New York Times described ET as the "Leonardo da Vinci of data," and Business Week as the "Galileo of graphics." He is now writing a book/film The Thinking Eye and constructing a 234-acre tree farm and sculpture park in northwest Connecticut, which will show his artworks and remain open space in perpetuity. He founded Graphics Press, ET Modern gallery/studio, and Hogpen Hill Farms LLC. Visual Display of Quantitative Information 200 pages Envisioning Information 128 pages Visual Explanations 160 pages Beautiful Evidence 214 pages Same paper and printing as in original clothbound editions. All 4 clothbound books, autographed by author $150 Available directly from Graphics Press. Die visuelle Darstellung quantitativer Informationen, (200 Seiten), $12 数量情報の視覚的表示, (200 ページ)、$12
ImageThink | Innovate and communicate with graphic recording & facilitation. System Mapping | FSG System mapping, the process of creating visual tools that describe a system, is a critical step in systems change that brings together stakeholders from across organizations and sectors to develop a common understanding of a given system. Breaking down the mapping process into 3 stages—preparation, facilitation, and revision—this guide provides detailed instructions, helpful hints, and visual examples for practitioners to follow as they create one type of system map called an actor map. Top Takeaways Actor maps identify individuals and organizations that are key players in a certain space and shows how they are connected.Due to the complexity of systems, no 2 actor maps will be exactly alike. This guide helps practitioners create an actor mapping process customized to the specific context of their evaluation or initiative.Creating a comprehensive actor map requires both explicit data from evaluations and studies, and implicit knowledge from the participants.
Data Visualization Fundamentals Ready to watch this entire course? Become a member and get unlimited access to the entire skills library of over 4,900 courses, including more Design and personalized recommendations. Start Your Free Trial Now Overview Transcript View Offline Exercise Files Released Got a big idea? Topics include: Channeling your audience Understanding your data Determining the information hierarchy Sketching and wireframing your ideas Defining your narrative Using typography, color, contrast, and shape to convey meaning Making your visualization interactive Skill Level Beginner 3h 41m Duration Views Show More Show Less - [Voiceover] Welcome to Data Visualization Fundamentals. Continue Assessment You started this assessment previously and didn't complete it.