Online Master of Data Science: Curriculum

An introduction to the principles and practices of effective data visualization as an essential skill for learning from data and for communicating with others, both within and outside of an organization. Students will combine principles from statistics, psychology, and computer science to design and create visualizations and use them to communicate with and about data to stakeholders and others. Prerequisites: Intro Programming.

An introduction to ethical frameworks and their applications in the context of data science. This course will help students reflect on the moral dimensions of the practice of data science in relation to what we believe, what we do, and what sorts of people we want to be. Ethical issues such as fairness, privacy, justice, transparency, and why we should try to be morally good will be considered both theoretically and practically (by applying them to issues that arise in the practice of data science and in contemporary social life more generally). Students will reflect on both historical and contemporary sources and consider what difference Christian faith makes to the theory and the practice of morality.

An introduction to collecting, managing, and processing large data sets. This course focuses on the core skills and concepts needed to pull data from a range of sources both inside and outside of organizations; to filter, transform, and combine data sets in preparation for data cleaning and analysis; and to construct quantitative summaries and basic visualizations. Prerequisites: Intro Programming.

Organizations leverage increasingly large collections of data about people, products, and processes. Predictive analytics provides tools to discover patterns and anticipate trends in such data. This course introduces the foundational principles of predictive analytics and provides a survey of a wide range of predictive methods, both battle-tested classics and emerging high-capacity deep learning models. Prerequisites: Intro Programming.

An introduction to the use and implementaiton of neural network (deep learning) models. Students will select machine learning algorithms appopriate to a given task. Then they will train, tune, and assess them using standard software packages, and assess the suitability of a machine learning model for a given task. Applications may include text classification, image classification, and price prediction. Prerequisites: DATA 502.

This course takes a practical approach to the fitting, assessment, interpretation, and presentation of statistical models, introducing students to a broad range of applied regression methods via case studies with real datasets. Topics include data preparation and visualization, the generalized linear model framework, and specific examples of frequently used models (such as multiple regression, regression for count and binary data, generalized additive models, and models to account for hierarchical structure or dependence).
Prerequisites: Intro Statistics.

An introduction to matrix algebra methods useful in data data science and their implementation in software. Topics include representing data using matrices and higher dimensional arrays, transforming data in these formats, matrix decompositions, and applications of linear algebra in data science.

This course introduces students to software design concepts and practices commonly used to develop, deploy, and maintain production-quality data sciecne applications. Topics include project management, software and data design, configuration management, and system deployment. Concepts, techniques, and tools are integrated within a term-long, team project. Prerequisites: Intro Programming.

Healthy and successful organizations are structured in alignment with their strategy, have strong, values-based cultures, and are able to adapt to change. This course will enhance an understanding of life inside organizations through an interdisciplinary examination of common organizational practices such as organizational structure, culture differentiation and internal integration, culture typologies, as well as leadership attributes and styles. The course is designed to increase effectiveness in dealing with multiple aspects of organizational change—by understanding conditions that may require it, increasing awareness of multiple ways that organizations change, managing change, receiving and participating in it, and understanding approaches and responses to change.

This is the first course in a two-course sequence in which students will complete a masters-level data science project. This project experience will give students the opportunity to apply concepts and techniques learned in several masters courses by developing a significant data application. The work will include necessary library research, design and prototyping, implementation, analysis, and deployment. The student will submit regular progress reports to a supervising faculty member and submit a final report (in DATA 698) on the project’s status for evaluation. Prerequisites: DATA 545, DATA 675, at least 20 hours in the MDS program.

Upon completion of the course, students will be able to:

• Complete the design, implementation, analysis, and testing of a significant data project.
• Manage the conduct and progress of this project.
• Communicate technically through written reports and oral presentations.

A continuation of DATA 696. The student will submit regular progress reports to a supervising faculty member and submit a final report on the project’s status for evaluation. Prerequisites: DATA 696.

Upon completion of the course, students will be able to:

• Complete the design, implementation, analysis, and testing of a significant data project.
• Manage the conduct and completion of this project.
• Communicate technically through written reports and oral presentations.
• Explain the role of data science in their chosen vocation.

An introduction to the support of organizational information technology cloud computing and services. This course covers cloud computing platforms, services, and their use in information technology administration using both open source and commercial platforms. Students will demonstrate their skills through system implementation labs and team projects. Prerequisites: TBD.

An introduction to the principles of causal inference and statistical design. Students will learn principles that can guide the collection of data and the creation and interpretation of models. Prerequisites: DATA 545.

An introduction to optimization from a statistical perspective, with emphasis on computation and problem-solving. Computer software will be used to explore practical examples. Prerequisites: DATA 555.

An introduction to simulation for statistics and data science, with emphasis on computation and practical examples. Computer software will be used to explore practical applications. Prerequisites: Intro Programming, DATA 545 or 502.

Advanced studies in machine learning. Topics include supervised and unsupervised learning, with applications to natural language processing and generation of text and images. Students will additionally study the social, ethical, and religious issuse that arise in automated processing and production of language and images. Prerequisites: DATA 675.

An introduction to the structures necessary to implement a database management system. Topics include data models (including hierarchical, network, and relational data models), normal forms for data relations, data description languages, and query facilities. An introduction to existing database management systems is given. Prerequisites: Intro Programming.

This course focuses on geographic information systems (GIS) and the art and science of mapping for spatial analysis. Map-design techniques and visual communication using GIS vector and raster data forms will be explored, as well as a variety of methods for analyzing spatial relationships. Topics include those of the physical world and landscape, social justice, poverty, and a significant end-of-semester project. This course has a lecture and lab component, and lab work will give practical experience to students using the ArcGIS suite. Students will complete a GIS project tailored to their disciplinary interest and also explore religious faith in professional GIS life.

This course introduces advanced themes in Geographic Information Systems including spatial database design, spatial algorithms, implementation and design, and advanced GIS applications including designs for community development and service tailored to individual students’ industrial application.