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.
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.
Research for Master’s in Applied Statistics
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 a master’s in applied statistics student, you will have many opportunities to work alongside faculty experts who are making breakthroughs in these cutting-edge areas:
As an MS applied statistics student, 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.
- Bayesian statistics
- Biostatistics and bioinformatics
- High-dimensional data analysis
- Statistical genetics
- Survey methodology
- Time series
Graduate Studies Series.
Team members from Graduate & Professional Studies host quick and convenient webinars designed to highlight popular topics when starting grad school. Take a deep dive into specific areas of interest such as how to secure funding, how to ace your application, an overview of student services, and more!
Have questions?
WPI's dedicated graduate student support team can help.
Faculty Profiles
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.
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 centers on quantitative analysis of dependent data, and my current interests include time series analysis, spatial statistics, spatial econometrics, and financial econometrics. My work involves both statistical and econometric methodology development and empirical studies related to financial market and remote sensing data. Although anchored in statistics, my research projects are interdisciplinary and lie at the intersection of statistics, applied probability, data science, finance, and economics.
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.
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 centers on quantitative analysis of dependent data, and my current interests include time series analysis, spatial statistics, spatial econometrics, and financial econometrics. My work involves both statistical and econometric methodology development and empirical studies related to financial market and remote sensing data. Although anchored in statistics, my research projects are interdisciplinary and lie at the intersection of statistics, applied probability, data science, finance, and economics.
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.
Refer a Friend
Do you have a friend, colleague, or family member who might be interested in a WPI graduate program? Click below to tell them about our programs.
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.
After Graduation
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 master’s in applied statistics students often continue their education at prestigious universities and find career opportunities in diverse sectors, including insurance, communications, government, life sciences, and weather services.
Solve Complex Problems with a Master’s in Applied Mathematics Instead
Maybe you prefer something a bit broader than a statistical focus? Our master’s in applied mathematics explores science, engineering, business, computer science, & industry to give graduate students a taste of mathematical applications in a variety of fields.
Just Starting to Think About a Career in Statistics? Explore Our Undergrad Options!
If you’re interested in mastering mathematical theory to solve real-world problems, a bachelor’s in mathematical science may be a good place to start. Our undergraduate degree covers a range of curriculum from applied mathematics to stats and pure mathematics. This degree is also offered as a BS/MS option.
Maybe you’re interested in expanding your knowledge of advanced statistical methods, but already selected a major? WPI’s minor in statistics can give you a strong base in how to analyze results and model complex data.
Forge New Paths with a PhD in Statistics
Ready to forge new paths and solve deep problems as a statistician? Be sure to explore our PhD in statistics in which students translate and present findings from vast amounts of numerical data. From healthcare to banking, you’ll have the opportunity to work in private and public sectors. If you enjoy gathering data to help develop solutions, a statistics PhD may be for you.
Make Essential Connections with an MS in Data Science
Are you interested in analyzing patterns that data reveals? WPI’s MS in data science lets you develop the technical expertise to determine how data shapes the way we live, work, and interact. You’ll use massive amounts of data to help implement targeted solutions.