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Course Criteria
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3.00 Credits
This course gives an investigation of the role of social networking technologies in creating communities in digital and physical spaces. Students will examine how social networking and peer collaboration technologies have engendered participation in campaigns and movements for social change in the digital information age. Students will thoroughly explore the concept of ""social change"" itself by identifying the values embedded in dominant cultural narratives of progress and decline. Students will then turn their attention to the ways individuals and groups implement social media technologies to support or forestall social, political, and cultural changes. There will be particular focus on the social media tools that communities use to disseminate and preserve valuable cultural information and knowledge when freedoms of expression are limited by external controls. Students will analyze and apply concepts of network theory to create a project that traces the presence and function of social media in relation to a particular community campaign or movement.
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3.00 Credits
Geographic information system (GIS) technology offers a means for understanding how human beings inhabit and construct identities across time and space. Mapping Time, Space, and Identity explores how practitioners in the field of digital humanities deploy GIS tools to capture, analyze, and present data that illuminates how humans understand and create location in relation to selfhood. Students analyze scholarship based on nonlinear models of historical change, models that can be expressed in the spatial logics of trees, graphs, and maps. Considering such models of analysis, students will implement GIS and visualization technologies to conduct and support their investigations. Students will emerge from the course with a better understanding of how GIS mapping tools can be applied to the study of the humanities as well as in personal narrative.
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3.00 Credits
Through relevant and applied business examples, Digital Marketing Analytics provides learners the opportunity to interpret, evaluate, and integrate digital marketing data. Students will learn to formulate and enact intelligent data-driven strategies and incorporate fundamental web marketing analytics into existing business practices. Core content will focus on identifying and understanding digital marketing metrics to gauge success of traditional, digital, interactive, and social media marketing efforts. Through an examination of available systems and relevant examples, learners will further their understanding of the digital value chain and how to capitalize on emerging trends.
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3.00 Credits
Analyze This! Interpretive Data Analysis critically appraises how data is visualized, analyzed, computed, modeled, and applied to answer questions. This course develops critical-thinking skills to solve real-world problems in an applied setting using data visualization, pattern recognition, human perception, and understanding of statistical concepts in a nonmathematical framework.
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3.00 Credits
Python programming enables students to implement fundamental principles of modern programming using the Python programming language and problem-solving techniques related to computing.
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3.00 Credits
This course introduces essential concepts and techniques of programming in the R computer programming language. It covers R variables, data types, arithmetic and logical operations, environments, functions, flow control, and loops. The course also discusses using R to get clean and transform data, which is a critical step in any data analysis project. Upon completion of this course, students should be able to set up an R programming environment and perform common R programming tasks.
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3.00 Credits
This course builds upon the fundamental principles of Python and prepares students to utilize Python for data analysis. It covers Python skills and data structures, how to load data from different sources, rearrange and aggregate it, and how to analyze and visualize it to create high-quality products. Python is a powerful programming language and has a mature and growing ecosystem of open-source tools for mathematics and data analysis. This course covers working with strings, lists and dictionaries (in addition to variables), reading and writing data, use of Pandas for data analysis, group, aggregage, merge and join, time series and data frames, matplotlib for visualization, and creating format and output figures. This course prepares students for further study of predictive analytics using Python.
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3.00 Credits
This course is for students who have an introductory background in R programming. Students will learn how R works with numeric vectors and special values, and how to deal with special values. Students will start working with R to handle text data and learn about regular expressions, dates, classes, and generic functions as well as matrices, data frames, and lists.
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3.00 Credits
In this course students will learn how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: regression models, smoothing methods including moving average (MA) and exponential smoothing, and autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
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3.00 Credits
In this course students will learn a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks. Students will also learn how to identify influential individuals, track the spread of information through networks, and see how to use these techniques on real problems. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
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