Certificate in Data Visualization

Data Visualization and Interpretation – Theory and practical knowledge about how to design, read, and understand visual representations of data. Hands-on knowledge about state-of the-art tools, e.g., Tableau, Python, and web-based libraries like D3.js.
Spatio-Temporal Urban Analytics - Essential concepts and skills needed to efficiently develop spatiotemporal thinking. Big data analysis and visualization techniques applied to spatio-temporal urban data. Knowledge about the R programming environment.
Elements of Visual Design- theories of design, techniques of composition, and technologies of electronic and print publishing. Modules include both design principles and hands-on practice in visual literacy, layout and design, and graphic tools. 
Advanced Information Design -  Design and creation of multimedia objects, usability heuristics, navigation theory, contemporary design practices and online community building
Visual Informatics for Network and Flow  - Knowledge of open source tools to visualize and interpret network and flow data. Collect network and flow data and create their own visual applications.
Customer Discovery – User-centered design evaluation techniques for understanding potential user's practice, preferences and mental models. Knowledge of a basic set of qualitative user/customer discovery methods which is essential for both the lean startup entrepreneur and those engaged in design innovation.
User Experience Design – Process of creating compelling interaction designs for digital products from the idea stage into creating a simple and intuitive user experience blueprint. You will 'learn by doing' in a team environment, enabling you to practice the techniques with coaching from instructors. 
Web Systems Development - Learn web development principles, as well as professionally relevant skills including industry standards, conventions, and procedures within large-scale programming projects. 
Data Analytics for Information Systems - Learn and conduct Python, MATLAB and R based manipulation of data, along with graduate level introduction to data analysis, probability and statistics from an information systems perspective.