Skip to main content

Courses

Data Analysis and Communication

Core Classes:

EAM: Data Collection
This course prepares students to use both quantitative and qualitative research methods, and address how, when, and why different methods are deployed. In this course, students learn about data collection methods, sampling strategies for quantitative research, effective survey design, conducting focus groups and in-depth interviews, the role of sample size, categories of quantitative variables, and assessing the reliability and validity of their measurement tools.

EAM: Data Analysis I
This course provides an overview of the theoretical foundations of qualitative and quantitative data analysis, and teaches practical skills related to data management, analysis, and theory development. Students will learn to code and interpret qualitative data and to interpret statistics most commonly used in evaluation sciences.

EAM: Data Analysis II
This course introduces students to a variety of statistical methods for multivariate data.
Multivariate analyses are complex methods that build upon the univariate analyses covered in EAM 630 Data Analysis I. Topics will include methods for hypothesis testing (multivariate analysis of variance (MANOVA), multiple regression), data reduction (factor analysis, principal components) and classification (cluster analysis).

APT: Ethical Use of Technology and Data
In this course students will research, identify, formulate perspectives, and discuss ethical challenges in the use of technology and data. Ethical challenges investigated may include, but are not limited to: environmental impacts, privacy considerations, public safety, workplace exposure, data gathering and sharing, and intellectual property. Students analyze a range of case studies related to ethical issues in emerging technologies and data collection and use. This will be a cross-listed course between the Applied Technology and Data Visualization programs.

DVS: Principles of Data Visualization
This course covers the fundamental elements of the database, semi-structured data, and unstructured data. Students will gain familiarity with data visualization concepts, techniques, and tools, including acquisition, augmentation, and restructuring; data storage and aggregation; access to parallel and distributed computing; high-volume data, disparate sources, and performance; and streaming data and dynamic queries. The student will learn to use several programming languages and software packages to create a range of data analyses and visualizations.

DVS: Communication of Data
In this course, students will develop the oral and written presentation skills demanded in
data-driven environments. Students will learn to identify and articulate business questions and then translate data into compelling and effective narratives for decision-making. This course will introduce students to a variety of media that can be used in the analysis, interpretation, creation, and transfer of information. The importance of understanding the context, the audience, and the intended use of the data are emphasized.

Elective Options:

APT: Foundations of Applied Technology
This course uses problem based learning to introduce students to a variety of technology domains used to address human needs and challenges. Technology domains of study may include electrical, mechanical, fluid dynamics, thermodynamics, cybernetics and computer technology. Students analyze a range of case studies and identify technology solutions for scenarios presented. Additionally, students are introduced to methodologies to conduct experimentation and testing.

APT: Systems and Critical Thinking
This course introduces systems thinking as an approach to problem solving and a way to address future needs. Systems thinking allows students to view a problem or need from a holistic perspective rather than the individual parts. This course also incorporates concepts to promote critical thinking, creativity, and innovation.

APT: Modeling and Simulation for Insight
Students in this course utilize modeling and simulation to enhance their skills in communications, decision making, optimization, cause and effects analysis, and engineering economics. The course introduces deterministic and stochastic modeling techniques, as well as different simulation methodologies. Topics include problem formulation, conceptual modeling, simulation methodologies, verification and validation, design of experiments, simulation execution, and output analysis.

DVS: Advanced Data Visualization
This course focuses on designing effective, compound data visualizations that contain information-rich graphics and multiple elements in order to tell a story or create an argument. Emphasis will be placed on static and dynamic dashboards and report card style visualizations that are often utilized in organizational and management settings. Students will gain working knowledge of R, Python, and other Cs+ languages. To enroll in this course, students must have completed 9 credit hours of Data Visualization courses or receive permission of the instructor.

EAM: Principles of Evaluation Design
This course will examine the role of evaluations in organizations, policy making, programmatic decision-making and fundraising. It will introduce research designs commonly employed to monitor ongoing programs and measure outcomes. The course will also address strategies for engaging stakeholders in evaluations. By the end of the course, students will be able to identify the appropriate research design for a specific evaluation need, taking into consideration financial and logistical constraints. Students will also design logic models to guide evaluation planning.

EAM: Project Management
Successful interventions and evaluations depend on strong planning and project management skills. This course covers skills and strategies related to budgets, planning, stakeholder engagement, staff supervision, and fundraising. Additionally, students will learn about different leadership styles and will work to develop leadership skills.

WDC: Grant and Proposal Writing
This course covers the complete process of grant and proposal writing and the contexts and strategies of the philanthropic environment. Students will learn how to research funding opportunities, identify and plan successful projects, devise achievable goals and budgets, write proposals for public and private foundations, follow up on both successes and rejections, and incorporate digital technologies. This course satisfies an elective for the evaluation and assessment methods graduate certificate.

WDC: Visual Thinking and Web Design
Students will learn to expand their digital design skills to resolve visual problems, implementing line, texture, color, spatial illusion, materiality, compositional frameworks and subject matter. Emphasis is on the design process and conceptual development relating to web and mobile platforms. This course equips students with a working knowledge of industry-standard creative software and Cascading Style Sheets and acquaints them with principles of accessibility and UX.