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Faculty Books and Projects



Please note that this schedule is tentative and subject to change.  


  Tuesday Wednesday Thursday
4:15 - 6:15 PM


6:30 - 8:30 PM


Prof. McSweeney
DATA 73000
Visualization and Design:


FALL 2019


DATA 71000 - Data Analysis Methods #62519
TBA, 3 Credits, Rm. TBA, Prof. TBA

The goal of this course is to provide students with an introduction to basic statistical techniques for analyzing data. Students will develop an understanding of concepts underlying modern statistics and statistical reasoning that will equip them with tools to analyze variety of data types and data sources and also visualize it. We will first learn principles of descriptive statistics. Next, we will cover principles and techniques of inferential statistics, and design of experiments. Students will explore various statistical measures and techniques for analyzing data, and practice applying this knowledge to real-world data problems. Practical topics include: descriptive and inferential statistics, sampling, experimental design, statistical models, parametric and non-parametric tests, ordinary least squares regression, logistic regression, and explorative data analysis.

DATA 73200 - Interactive Data Visualization #62521
TBA, 3 Credits, Rm. TBA, Prof. TBA

DATA 73300 - Visualization and Design: Fundamentals #62520  
Thursday 6:30 - 8:30 PM, 3 Credits, Rm. TBA, Prof. Michelle McSweeney (
Cross-listed with DHUM 73300

Data are everywhere and the ability to manipulate, visualize, and communicate with data effectively is an essential skill for nearly every sector—public, private, academic, and beyond. Grounded in both theory and practice, this course will empower students to visualize data through hands-on experience with industry-standard tools and techniques and equip students with the knowledge to justify data analysis strategies and design decisions.

Using Tableau Software, students will build a series of interactive visualizations that combine data and logic with storytelling and design. We will dive into cleaning and structuring unruly data sets, identify which chart types work best for different types of data, and unpack the tactics behind effective visual communication. With an eye towards critical evaluation of both data and method, projects and discussions will be geared towards humanities and social science research. Regardless of academic concentration, students develop a portfolio of interactive and dynamic data visualization dashboards and an interdisciplinary skill set ready to leverage in academic and professional work. 

Note: This class will involve 9 in-person meetings and 6 hybrid (online) meetings.