COM 3571: Data Visualization

Abstract

Visual representations and tools are widely used in science, business, and government to extract, and effectively share, insights derived from large, dynamic data sets. This use is driven by two realities. First, the explosive growth in the amount of data that is created, gathered, and retained means that the data sets relevant to any given question or decision are too large for a human being to process one data point at a time. As such, effective visual representations that are “worth a thousand words” and concisely communicate the information latent in the data are essential. Second, an organization’s investment in data science, which uses mathematical techniques to derive insight from data, can only pay off if the insight can be clearly communicated to decision makers who are not scientists. Visualization is therefore an increasingly critical skill for a data scientist. Data visualization utilizes a set of techniques and algorithms to programmatically transform data into (interactive) graphical representations that effectively tell a story and guide decisions (i.e. explain), or that facilitate interactive analysis (i.e. explore). In this course, students will learn the algorithmic and artistic techniques needed to design and develop effective explanatory and exploratory data visualizations. ___  Students will be able to use visualization tools and techniques to explore data  Students will be able to both choose and design visualizations that will accurately and effectively convey information  Students will be able to write programs to create both static and interactive visualizations  Students will know where to look for current innovations in visualization and thus will be able to stay up to date with the latest visualization approaches ___  Visual design and aesthetics  Perception and visual encoding  Algorithms for creation of static and interactive visualizations  Visual design patterns to support specific analyses, including: time series, comparisons, dependencies/interactions, deviations, multivariate, and spatial  Applications of explanatory and exploratory visualization  Overview of important visualization tools  Spotlights on the work and contributions of highly impactful visualization researchers and practitioners