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General Info:

Instructor: Prof. Klaus Mueller
    Office hours: Fridays 4-5pm in NCS 261 (or send email for appointment)
    Phone: 2-1524
    Email: mueller{remove_this}@cs.stonybrook.edu

Grader: Usama Mehmood
    Office hours: Mondays and Wednesdays 2:30-4:00 pm in NCS 106
    Phone: none
    Email: umehmood{remove this}@cs.stonybrook.edu

Meeting time and venue:

   TuTh 5:30-6:50pm, Javits 101

Summary:
Visualization plays an increasingly important role in the understanding of the massive data that are nowadays being collected in almost any domain – science, medicine, business, commerce, finance, social networks, and many more. As such, visualization is often deeply integrated into the analytics tools developed for data science. This course is an introduction to both the foundations and applications of this emerging paradigm, known as visual analytics. It will begin with the basics – visual perception, cognition, human-computer interaction, the sense-making process, data mining, computer graphics, and information visualization. It will then move to discuss how these elementary techniques are coupled into an effective visual analytics pipeline that allows humans to interactively think with data and gain insight. Students will get hands-on experience via several programming projects, using the public-domain statiiscs library R or python for data analytics and the popular javascript library D3.js for interactive information visualization directly in the web browser. In addition, students will also gain practical experience with a state of the art volume renderer for the visualization of medical data.

ABET course outcomes:
Prerequisites:
    CSE 219 (Computer Science III); AMS 210 or MAT 211 (Linear Algebra), AMS 310 (Survey of Probability and Statistics) 
    Working knowledge in Java programming

Texts:
    Required:
    For additional reference and on reserve in the Science & Engineering library:
Grading:
    Lab assignments: 40%
    Midterm: 30%
    Final: 40%

Lab assignments:
    There will be five lab assignments to provide you with hands-on experience in visual data analytics. You will use the public-domain statiiscs library R or python for data analytics and the popular javascript library D3.js for interactive information visualization directly in the web browser.A tentative list of these lab assignements is: