PPT-Data Visualization Data visualization

When working with data it is often useful to display that data in a graphical format This can often help us to see correlations trends or other relationships in the data that looking at the raw data doesnt make apparent. Eliot Feibush. Plasma Princeton Physics Laboratory. PICSciE. Princeton Institute for Computational Science and Engineering. Getting to Know Your Data. Geometric range. Numerical domain (min, max). Histogram. Zhonghua. . Qu. and . Ovidiu. . Daescu. December. . 24, 2009. University of Texas at Dallas. Introduction. Assisting Visualization System. Implementation. . Examples. Conclusion. Outline. Introduction. Nicholas Del . Rio. University of Texas at El Paso Computer Science. Workshop Objectives. U. nderstand . notion . and purpose of visualization . queries. Understand why writing queries may be easier that writing visualization programs. Yingcai Xiao. Computation with and without Visual Assistance. 67 x 89 = ? . Visualized Data Analysis. 67. x 89. ---------. 603. + 536. ---------. 5963. . Week . 9 . NJ Kang. What is creative visualization?. 5. Create new and fresh/. From language to visualization. http://pageturneradventures.com/2011/09/imagination-workout-visualization/. From word to image. Creates interactive visualization . and dashboards . Fast and easy to use. Connect to any . data regardless . of its size and . complexity. To present . data from multiple sources. It gives . you . the ability . VOLUME. VARIETY. VELOCITY. VALUE. TODAY. THE FUTURE. DATA SIZE. THRIVING IN THE BIG DATA ERA. High performance computing. Computers have become increasingly powerful.. Required for Big Data Analytics. Databases, continued. What kind of power do visualizations have?. A brief history of visualization. How to lie with dataviz. Create your own. gapminder.org/world. http://bit.ly/dh1014b. David Granholm. , Senior Director PM . Matt Milella, VP Engineering. Oracle Analytics. October 4, 2017. 2. 3. Oracle Analytic Apps Portfolio. OTBI. DV. Content. Packs. Oracle Analytic Cloud (OAC). Next-Gen. iPhone. Capstone Team#5. Fall 2009. Table of Contents. Topic. Slide Number. Team information. ………………………………………………3. Instructor/Mentors Information. ………………………………………………4. ParaViewWeb. Web3D – . Paris 2011. Julien. . Jomier. , . Kitware. . julien.jomier@kitware.com. Motivation. Acquiring Data (2). Tissue rotates. Knife advances. This tissue ribbon is collected by a submerged conveyor belt. Spring 2018. Course Introduction. What the course is about. What will you learn. Teaching team. Schedule. What you need to do to succeed. 1. Course overview. Visualization analysis & design. What visualization can do?. 4 MUVA a Flexible Visualization Architecture for Multiple Client Platforms1 Introduction Information visualization is a growing area of interest and research As opposed to traditional data repre Principles of Graphical Excellence. Clarity. Precision. Efficiency. Maximize ideas, minimize ink. (from . Tufte. ). Data . Viz. in a Nutshell. History. Graphical Basics. Details to Make Your Viz More Understandable.