Applied Urban Informatics Graduate Certificate

Data is everyone's responsibility now. City planners and urban professionals no need to have the words "data" or "analyst" in their job title to be immersed in data. They are expected to use them regularly as the trend towards evidence-based decision making continues. Government agencies, foundations, nonprofits, and planning firms all recognize the importance of leveraging data to create effective policy decisions and urban plans. However, planners and urban professionals grapple with how to analyze large and complex datasets. In this class, students will learn about the applied dimensions of the emerging field of urban informatics which is used to understand cities and to inform urban planning practice and policymaking. [4 credits]

Evidence-based and data-driven approaches to urban problems are the industry standard among municipalities, agencies, and non-governmental organizations. This course will cover a variety of statistical "tools" from three broad areas: (1) descriptive statistics, (2) inferential statistics and hypothesis testing, and (3) measures of association. Students will learn how to develop research questions, describe, and draw conclusions from quantitative data, and interpret statistical research findings, and be able to present these findings to a variety of audiences in a clear and accurate way -- to be able to "tell a story" with numbers. In addition, students will develop a proficiency working with large data sets and conducting analysis with a critical lens, using the analytical software -- Statistical Package for the Social Sciences (SPSS) -- commonly used in urban planning and related fields. [4 credits]

Geographic Information Systems for Planners provides an introduction to Geographic Information Systems (GIS) specifically with a focus on applications in urban planning. The role of spatial analysis in local, state and regional planning has steadily increased over the last decade with the infusion of windows-based GIS software such as ESRI ArcGIS. The class focus is to prepare students to feel comfortable communicating with other GIS users, research spatial data, and produce high quality digital maps in an applied learning environment. [4 credits]

Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. [4 credits]

Formerly titled CS 688 Web Analytics and Mining.
The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. [4 credits]

This course aims to study basic concepts and techniques of data mining. The topics include data preparation, classification, performance evaluation, association rule mining, ?regressions and clustering. We will discuss basic data mining algorithms in the class, and students will practice data mining techniques using Python or R. Prereq: CS 521, and CS 546 and either CS 579 or CS 669. Or instructor's consent. [4 credits]