COURSE CURRICULUM

The Master’s in Mathematics at University at Albany (UAlbany) is structured as a rigorous, two-phase graduate program that blends advanced mathematical theory with applied statistical methods, culminating in a capstone experience that integrates academic learning with real-world problem solving. In the general sequence, students complete at least 18 credits in mathematics, which may include advanced coursework in areas such as Real Analysis, Complex Analysis, Algebra I, and Topology I. These courses build strong foundations in abstract reasoning, mathematical structures, and theoretical problem-solving. Students may also take approved supporting courses in related fields, typically up to 12 credits, allowing for interdisciplinary exploration aligned with individual academic or professional goals. A recommended set of courses helps deepen mathematical maturity and includes topics such as analytical functions, complex numbers, Cauchy’s theorem, Laurent series, and foundational concepts in higher mathematics. In the statistics sequence, students again complete at least 18 credits in mathematics, with a focus on statistical theory and data analysis. Recommended coursework includes Introduction to Theory of Statistics I and II, Methods of Data Analysis I and II, and Introduction to Bayesian Inference I, which collectively prepare students to interpret and model complex data. Additional specialized courses such as Analysis of Categorical Data and Sample Survey Methodology provide applied tools for understanding real-world statistical problems, including risk analysis, survey design, and multivariate relationships. The program concludes with a capstone experience, which can take the form of a Master’s Seminar or a Master’s Thesis. In the seminar option, students research multiple topics from mathematical literature and present their findings to peers and faculty, while the thesis option requires independent research and an oral defense. Overall, the program develops strong analytical, theoretical, and applied statistical skills for careers in research, data science, education, and quantitative analysis.