PPT-Data Visualization TIME Training

Neuhausen 15 September 2014 Representing Data Tables Charts Matrices Maps Photos Text T Design principles Above all else show the data Maximize the dataink ratio Erase nondataink. B.Tech Major Project. Project Guide. Dr. . Naresh. . Nagwani. Project Team Members. Pawan Singh. Sumit. . Guha. What is Data Visualisation ?. Data Visualisation . is the graphical representation. of information. Bar charts, scatter graphs, and. 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. Antônio José da Cunha Rodrigues. Tiago Garcia de Senna Carneiro*. TerraLAB. – . Laboratory. for . Earh. System . Modeling. . and. . Simulation. Computer. . Science. . Department. Federal . IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER . GRAPHICS, . DECEMBER 2012. Authors:. . Christian . Tominski. Heidrun. Schumann. Gennady . Andrienko. Natalia . Andrienko. BY:. Farah . Kamw. Introduction. Jamie Starke. Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations. J. . Heer. , N. Kong, M. . Agrawala. (2009). CI 2009 . Rethinking Visualization: A High-Level Taxonomy. 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. Representing programs through algorithm animation, . typographic source-code presentation, and interactive auralization . transforms the hunt for bugs into a cognitively accessible multimedia experience.. Michael Johnson. 1. , Thomas Villani. 1, . Nick Crider. 1. ,Colleen Wojenski. 2 . 1. Visikol Inc., North Brunswick, NJ, 08902, 2. Product Safety Labs, Dayton, NJ, 08810. Abstract. Developmental and reproductive toxicology (DART) studies are required for new chemical entities going to market to ensure that there are not adverse developmental consequences associated with the chemical. These studies involve dosing a pregnant female animal (e.g. mouse, rat, rabbit) with the compound and evaluating the effect of the chemical on the offspring. This evaluation involves the parallel processes of histopathology and gross morphological evaluation wherein the most time-consuming component of DART studies is the performance of gross skeletal evaluation. Since the introduction of Thalidomide into the marketplace, close attention has been paid to the impact of chemicals on skeletal development and DART studies require the visualization of gross skeletal morphology for abnormalities. The current methodology for conducting skeletal evaluation involves digesting soft tissue using a strong base (potassium hydroxide) and staining the bone with alizarin and cartilage in some cases with . Time Series Data. A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. 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?. Chong Ho (Alex) Yu. Agenda. What is data visualization?. What isn’t data visualization?. What is the difference between dynamic, semi-dynamic, and non-dynamic (static) visualization systems?. Why do we need 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 doesn't make apparent.. 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.