WPI’s Master of Science 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, you’ll gain 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.
Our 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.
Our faculty and student researchers are passionate about exploring the theoretical foundations of statistics as well as using applied statistical methods to make important discoveries.
As an MS student, you will have many opportunities to work alongside faculty experts who are making breakthroughs in these cutting-edge areas:
You will have access to state-of-the art computing facilities and industry-standard software programs including SAS, R, MATLAB, Python, and Turing, our high-performance compute cluster.
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.
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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.
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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.
[...]Statisticians are in high demand in many industries, and the broad education you gain at WPI will prepare you to jump right in and make an impact right away. Our students often continue their education at prestigious universities and find career opportunities in diverse sectors, including insurance, communications, government, life sciences, weather services.
