DSA 500 Introduction to Statistics and Matrix Methods (3)
Elements of probability, statistics, and matrix methods for data science. Topics include basic set theory, probability rules, probability distributions, and an introduction to inference including descriptive statistics, estimation and hypothesis testing. Bayesian paradigm reviewed. Matrix methods topics include vector and matrix operations, systems of linear equations, eigenvalues and eigenvectors, matrix factorizations, and least-squares problems.
DSA 501 Statistical Inference (3)
An advanced exposition and practice of inferential statistics. Topics include point estimation, interval estimation, hypothesis testing, model selection, and measures of significance. Co-requisite: DSA 504 or Permission of the instructor.
DSA 503 Data Collection and Design of Experiments (3)
An exposition of the practice of data collection methods and experimental design methods. Topics include data collection for qualitative and quantitative research, access to public open data sources, data extraction from computer information systems, principles of experiment design, randomized and non-randomized statistical designs, reliability and validity analysis of experimental designs. No prerequisites.
DSA 504 Data Analytics Tools (3)
Practical script language programming for data analytics and data management. Topics include R programming, Python programming, operating system shell scripting, and programmatic data access and storage. No prerequisite.
DSA 506 Visual Analytics and Communication (3)
Introduction to the fields of visual analytics and data visualization, which involve the development of cognitive artifacts that help analysts communicate and gain new insights into the data. Design principles, cognitive science, and aesthetics are applied to visually represent and analyze large-scale data sets. The course includes a survey of data visualization work in various domains (art, journalism, information design, network analysis, science, and map-based applications) as well as different media (print, screen, interactive, 3D). Finally, insights are provided into debates about data aesthetics, data privacy, and the social, ethical, and regulatory context of analytics. No prerequisites.
DSA 507 Introduction to Machine Learning (3)
Introduction to fundamental techniques and software artifacts of machine learning. Topics include supervised learning (classification, regression), unsupervised learning (clustering, representation learning, dimensionality reduction), reinforcement learning and basic deep learning. Prerequisite: DSA 504 or permission of instructor
DSA 508 Big Data Platforms and Analytics (3)
Fundamentals of computation models and processing platforms for big data. Topics include characterization of present-day datasets (including image collection, text corpora, graph data and streaming data), databases for large datasets (distributed file systems, NoSQL), distributed and parallel processing models (distributed memory models, shared-memory models, and MapReduce model), batch processing v. stream processing, the Hadoop ecosystem, and Spark analytics. Prerequisite: DSA 504 or permission of instructor.
DSA 597 Internship (3)
One of three options students may select as the required exit course (Capstone Experience) for the MS Data Science and Analytics. Working with the internship supervisor, students are responsible for securing their own internships. However, they may contact the Career Services Office for assistance in identifying and applying for opportunities of their interest. The experience requires a minimum of 8 hours a week on site and a final internship project report. Prerequisite: Permission of the academic advisor and the internship supervisor.
DSA 598 Project (3)
One of three options students may select as the required exit course (Capstone Experience) for the MS Data Science and Analytics. Students are responsible for selecting the area, title and scope of the project, subject to approval of a project supervisor. At the end of the project, students shall produce a written and archivable technical report detailing the project, including its accomplishments. Students shall also give a public oral presentation based on the technical report. Prerequisite: Permission of the academic advisor and the project supervisor.
DSA 599 Thesis (3)
One of three options students may select as the required exit course (Capstone Experience) for the MS Data Science and Analytics. Students are responsible for selecting the area, title, and scope of the thesis, subject to approval of a thesis supervisor. The thesis must be a research project exhibiting original research and contribution to scholarship. Students shall give a public oral presentation for thesis defense. Prerequisite: Permission of academic advisor and a thesis supervisor.