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CS 91.540 — Visual Analytics - Spring 2010

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CS 91.540 — Visual Analytics - Spring 2010 CS 91.540 — Visual Analytics - Spring 2010 Description Whether the cause is an epidemic, natural disaster or an act of terrorism, accurate prediction of risks, timing, and consequences of destructive events can prevent loss of life and damage to property and natural resources. Whether one is looking for a genetic marker, a trend in the stock market, patterns in educational settings, quality of life indicators, accurate analysis can help the discovery process, provide insights, and identify and measure ambiguities and uncertainties. Visual analytics is the use of information and communication technologies designed to aid analysts in a variety of fields from health, manufacturing, security, education, finance, scientific and social science research, and others, deal with massive, dynamic sets of structured (spreadsheets, databases) and unstructured data (text, web pages, video), and often conflicting data. Analysts use these technologies to provide timely, defensible, and understandable assessments and to communicate these effectively for action to both the public and decision-makers. The overall goal is to detect the expected and discover the unexpected. What you will get out of the course A student who successfully fulfills the course requirements will have demonstrated an understanding of the fundamentals of visual analytics and its applications, an understanding of the analytical reasoning process, an understanding of cognition, perception, and designing for human users, an ability to design, build, and evaluate suitable visual representations to a real-world dataset for decision makers or the public. The course The course will discuss and combine principles from cognitive science, information visualization, geospatial information systems, machine-based reasoning and learning, and data mining. There will be a number of guest lecturers. Several focus areas include Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making; Visual representations and interaction techniques that take advantage of human vision’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once; Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis; Techniques that support the production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences. This course is about the what and why of visual analytics, and in some sense the ‘how an analyst does visual analytics’. Our goal is to build an understanding of basic ideas in visual analytics, then focus on understanding the analysis process--what is it we do when we use the tools? Students will be exposed to both basic visual analytics tools and as well to some of the latest research and commercial-grade visual analytic tools. Analytic reasoning is a process that encompasses perception, cognition, discourse, and collaboration. This course considers methods and tools that support analytic reasoning by combining human visual capabilities with computational devices and algorithms. Topics include data representation and transformation, visual representation and interaction, production, presentation, and dissemination of knowledge, sense-making, and the challenges that information complexity and scalability pose for the very human process of reasoning. Goals of course The goals of this course are for students to develop a comprehensive understanding of this emerging, multidisciplinary field; apply that understanding to a tightly focused research/discovery problem in a domain of personal interest Class format The class format will include reading, discussion, and application of existing software environments to problems in visual analytics. Class meetings will typically include: (1) discussion of two or three journal or conference publications that cover a range of topics from information visualization, geographic information systems, visual data mining, cognitive science, user- centered design, and the semantic web; (2) learning about, applying, extending, or assessing aspects of visual analysis tools and techniques. Students will be expected to take an active role in discussions, with selected students assigned the role of organizing and leading most discussions. Class project The class project is the main examination criteria for this course. Projects may be carried out individually or in groups of two, and will be research projects within the field of visual analytics. Students will choose a topic by the second week of classes, and will work on their project for the duration of the semester. Topics can come from such areas as finance (stock market, credit), health care (homeless, emergency room patterns), geospatial (toxic use, disease spread, world indicators), medicine (genomics, drug discovery), education (measures, patterns), law enforcement (money laundering, capital crimes), homeland security (avert terrorism, drug traffic, secure borders), national security (intelligence, information access), and information technology (cyber terrorism, internet security, network analysis). Instructor approval is necessary for all topics, and the instructors will also furnish students with some suggestions for suitable topics during the first week of classes. The project will focus on building, applying, or evaluating visual analytics methods and tools. Teams will present their results and submit a paper with content, style, length, and quality typical of current conference proceedings. Projects that are particularly promising may be selected by the instructors to be further developed into research papers suitable for submission to the IEEE Symposium on Visual Analytics Science and Technology (VAST 2010), with a submission deadline on March 31st, 2010. More details on the class project will be given in a separate handout during the first week of classes. ======== Assignments Assignment 7 Using your chosen project dataset, use a combination of visualization and analysis to write up problems, discoveries, and insights. ________________________________ Assignment 6 Read the sixth chapter of Illuminating the Path. Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract). You may skip this part of the assignment if you are presenting a paper this week. Using your chosen dataset, provide some hypotheses you might aim for. Discuss your data set and write up the kinds of problems, discoveries, and insights that you are looking for. Visualize the dataset using a visualization system of your choice. ________________________________ Assignment 5 Read the fifth chapter of Illuminating the Path. Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract). Sign up to give a presentation on a visual analytics topic. Refer to the reading list for details. ________________________________ Assignment 4 Read the fourth chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Run Tableau on a dataset of your choice, write up issues and key discoveries Post a link to the source of your chosen dataset with a brief description on your group page. ________________________________ Assignment 3 Read the third chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Install and run Tableau on a dataset of your choice Start looking for a dataset to work with on your project ________________________________ Assignment 2 Read the second chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Use R or an R derivative to analyze the new assigned data set - write up issues and key discoveries. Select area of interest for class discussion. ________________________________ Assignment 1 Read the first chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Take the Challenger Data set and produce a report that convinces me NOT to launch You will get an email about how to place your results on the class web page ======================== DataSets Visual Analysis Examples from the VAST Contest From the Visual Analytics Benchmark Repository http://hcil.cs.umd.edu/localphp/hcil/vast/archive/ Award: Excellent analytical technique featuring integration of data mining and visual analytics http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/UKN- AVA1/traffic/traffic_11/UKN-KNIME-MC1/UKN-KNIME-MC1/index.htm Award: Excellent example of Analytic tradecraft http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/VIS(US)%20Stuttgart/gr and/grand_19/visus-stuttgart-gc/index.htm Award: Good Analytical debrief http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/sztaki/flitter/flitter _3/sztaki-socialVis-MC2/sztaki-socialVis-MC2/index.htm Award: Intuitive analytic information presentation: http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/gami/traffic/traffic_1 /gami-BNT-MC1/gami-BNT-MC1/index.htm Award: Intuitive traffic visualization and video description of the analysis process http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/Palantir_VAST09/traffi
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