Industry experts stress the importance of often-overlooked communication skills in data science. Rachel Hawley, Analytic Solutions Architect at the SAS Institute, states “it is extremely important that potential candidates have effective communication and presentation skills. It’s not enough to just have the technical chops, a data scientist must be able to effectively explain how he or she came to a specific conclusion and convince the internal or external customer that their results should be leveraged.” This course is designed to explore this intersection between communication and data science. Topics will include assessing and improving communication skills, interpersonal and intercultural communication, teamwork, and leadership. The development of effective presentational skills, particularly oral skills, will be stressed.

A problem-solving approach to learning computer programming. Topics include variables, data types, conditional statements, loops, arrays, recursion, principles of software engineering, object-oriented programming, data structures, algorithms, and the use of standard libraries available in a variety of programming languages. The course will use commerically common programming languages and integrated development environments (IDEs).

Basic concepts of databases. Topics include conceptual data modeling, database design and normalization, and database implementation. Use of SQL for data definition, manipulation, and query processing. While primary emphasis will be on the relational model and traditional RDBMS, discussion will also include a survey of techniques for handling non-relational data models, massive datasets, and unstuctured data, including data warehousing, in-memory databases, NewSQL, NoSQL and Hadoop.

This course is about mining knowledge from data in order to gain useful insights and predictions. From theory to practice, the course investigates all stages of the knowledge discovery process, which includes data preprocessing, exploratory data analysis, prediction and discovery through regression and classification, clustering, association analysis, anomaly detection, and postprocessing.

Advanced Topics in Data Science is a comprehensive course designed to provide students with both foundational and advanced concepts in data science and machine learning. The course begins with an introduction to programming and data analysis using Python, equipping students with essential coding and analytical skills. It covers core machine learning techniques, including regression methods, classification approaches, and anomaly detection, before advancing into deep learning with neural networks (NNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). Students will also explore dimensionality reduction with Principal Component Analysis (PCA) and learn about autoencoders for unsupervised representation learning. A key focus of the course is the practical application of these techniques, culminating in model deployment using Amazon SageMaker. This hands-on approach prepares students to develop and implement scalable machine learning solutions in real-world environments. Prerequisite:DSCI 501

This course is an introduction to data visualization. It includes data preprocessing and focuses on specific tools and techniques necessary to visualize complex data. Data visualization topics covered include design principles, perception, color, statistical graphs, maps, trees and networks, and other topics as appropriate. Visualization tools may include JavaScript D3 library, Python, and R, and commercially available software such as Tableau, etc. The course introduces the techniques necessary to successfully implement visualization projects using the programming languages studied.

Thesis credit may be earned for significant work toward the writing of a master’s thesis. This thesis may be used to fulfill the culminating project requirement.

The practicum is an opportunity to directly experience the work of a data scientist or data analytics professional. It consists of project-based learning on a significant and contributory business objective in conjunction with practicing professionals in one of many appropriate industries. May be repeated up to 6 credits.

This course teaches skills in written, visual, and verbal communication of particular importance to data science professionals. It engages with foundational concepts of rhetoric, composition, and design that can be applied in any setting while also addressing the forms and conventions of technical writing in a professional setting that students will encounter as practicing researchers and data analysts. The course stresses the seamless continuity between analysis of data and communication about that analysis.

An application-focused approach to linear algebra used in data science. Topics include matrices, Gaussian elimination, vector spaces, inner products, orthogonality, least squares, eigenvalues/vectors, matrix factorizations, singular value decomposition and principal component analysis, quadratic forms, data/image processing, and other topics pertinent to data analysis.

This course provides a comprehensive, application-focused overview of essential statistical methods for data science. Topics include data collection techniques, descriptive statistics, and exploratory data analysis. Foundational concepts such as sampling distributions and the Central Limit Theorem set the groundwork for estimation, confidence intervals, and hypothesis testing. Students will explore techniques in ANOVA and categorical data analysis, as well as nonparametric techniques and permutation tests. Advanced methods include the bootstrap, linear and logistic regression, generalized linear models, and linear discriminant analysis, equipping students with a versatile toolkit for real-world data analysis and decision-making in data-driven contexts.

This course is designed to equip DNP students with essential knowledge and skills in nursing informatics, data analytics, and their application to improve healthcare outcomes. This course emphasizes the significant role of nursing informatics in today's evolving healthcare landscape and explores the opportunities and challenges of integrating informatics and data analytics in various healthcare settings. Students will gain an understanding of the use of electronic health records, telehealth, and clinical decision support systems in enhancing patient care and safety. Students will develop competencies in using informatics tools and techniques to analyze large volumes of data, supporting evidence-based nursing practice. Additionally, the course will examine ethical and legal considerations and advocacy related to informatics and data analytics in nursing practice. Prerequisites: NURS 612; NURS 620; NURS 622.

Data about us is collected continuously, and in many ways makes our lives as we know them possible—enabling your doctors to treat you efficiently, letting Amazon show you what you need to buy before you even know it exists, helping Spotify introduce you to the next music you’ll love. But is there a dark side to all this data-driven convenience? In this one credit hour course, students will engage with the ethical challenges posed by data collection, analysis and use, through class discussion, case study analysis and course readings. We begin by considering various ethical frameworks, including utilitarianism and deontology. We then engage with the history of data collection, looking at the abuse of humans, particularly from marginalized groups, in the Nazi experiments, the Tuskegee syphilis experiments and the history of eugenics in the US. Turning to contemporary methods of collecting and using data, we consider key areas of ethical concern including: issues of autonomy and consent, privacy and surveillance, artificial intelligence and machine learning, disinformation and bias, and algorithmic discrimination. Students demonstrate mastery of the material in online discussion, brief writing assignments, and analysis of a self-chosen contemporary case study.