Master’s in Applied Statistics
WPI’s Master’s in Applied Statistics will give you the knowledge and hands-on experience needed to tackle challenges of statistical design, analysis, and control that arise across a wide variety of fields.
Following WPI’s distinctive educational model, in which theory and practice go together, our master’s in applied statistics will provide an in-depth understanding of important statistical methods while deploying them to solve critical problems in the world around you. You’ll have the flexibility to tailor a path that matches your career goals and do research that matters to you in areas ranging from biostatistical application to big data analytics, to social policy analysis.
Curriculum for MS Applied Statistics
Our MS applied statistics curriculum is designed to provide breadth in diverse areas of statistics. Through core courses, you’ll master the theoretical foundations of applied statistics and learn to use powerful computational tools to gather and analyze large amounts of numerical data. You can choose electives with a theoretical or industrial emphasis that focus on topics such as regression, multivariate analysis, sample surveys, Bayesian analysis, and temporal analysis. You will culminate your studies with a capstone experience entailing a research-based thesis or an intensive project that may be conducted with an outside sponsor.
Graduate Studies Series
Learn from our enrollment team members and other guests by attending quick and convenient 30-minute webinars we designed to highlight popular topics when starting grad school. Take a deep dive into specific areas of interest such as how to funding, how to ace your application, student services, and more!
Faculty Profiles
Professor Zou's research focuses on financial time series and spatial statistics with applications to epidemiology, public health and climate change. His most recent research on statistical theory and methodology addressed a wide range of challenges including high dimensionality, complex dependencies, and space and time variations. His research in high-frequency financial data tackled problems with high dimensionality, which is currently a hot topic in statistics.
My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.
Professor Wu's research interest lies in applying the power of statistical science to promote biomedical researches. In statistical genetics, he is developing novel statistical theory and methodology to analyze genome-wide association (GWA) data and deep (re)sequencing data to hunt new genetic factors for complex human diseases. In epigenetics, he is studying gene expression regulation mechanisms through chromatin interaction, and RNA silencing pathways in the developmental stages of germ-line cells.
Teaching is an art. It is something you develop within yourself. It needs hard work, time, creativity and dedication. Giving clear and understandable lectures, facilitating group activities, and interacting with students are all important parts of my teaching. My objectives as a statistics educator are: (1) to teach students about statistical tools and how to use them correctly, (2) to expose students to the structure of statistical analysis, and (3) to teach students how to communicate statistical results and concepts to a variety of audiences.
My research in applied statistics focuses on methods for causal inference using large, administrative datasets, primarily with applications in learning sciences and social sciences. I have developed and worked on methods combining machine learning with design-based analysis of randomized trials and matched observational studies, principal stratification and mediation analysis using log data from intelligent tutoring systems, and regression discontinuity designs.
Getting Involved as an MS Applied Statistics Student
Outside of class, you may join other students in the WPI Math Club, an organization that organizes weekly math discussion sessions and sponsors events and contests including WPI’s Pi Day and Mathematics Awareness Month. You may also take advantage of diverse campus resources and activities, including our state-of-the-art Sports and Recreation Center.