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General Info:

Instructor: Prof. Klaus Mueller
    Office hours: Th Fr 4-5pm (or send email for other arrangements)
    Phone / location: 2-1524 / New CS Buiilding (NCSB) room 261
    Send email

TA: Amol Damare, Tithi Gupta (grading only), Ayush Kumar (STRIDE preferred), Jinghuan Shang, Yunxiang Wan
    Office hours: A. Damare (Fr 2-4 pm), T. Gupta (Fr 4-6 pm), A. Kumar (Th 3-5 pm), J. Shang (We 2-4 pm), Y. Wan (Fr 2-4 pm)
    Location: A. Damare, A. Kumar (NCS 134), T. Gupta, J. Shang, Y Wan (Old CS 2217)
    email: Amol Damare, Tithi Gupta, Ayush Kumar, Jinghuan Shang, Yunxiang Wan

Meeting time and venue:

   TuTh 7:00-8:20pm, Javits Lecture Center 102

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 will discuss both 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 constituents are coupled into an effective visual analytics pipeline that allows humans to interactively reason with data and gain insight. Students will have the opportunity to hone their skills by a set of projects and then more deeply explore a topic of their choice by ways of a final programming project. We will use the public-domain library 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. 

Prerequisites:
    Graduate standing
    Working knowledge of Javascript

Texts:
   Required:    For additional reference:
Grading:
    Projects (3): 10% each
    Examination (online): 40%
    Final Project: 30% (proposal 5%, final report, lighting talk video, single poster slide 25%)