The Certificate in Data Analytics provides an avenue for individuals who wish to earn a credential in data analytics, but who may not yet be ready to commit to a full master's program. As a student in the program, you'll learn the skills needed to gather, interpret, and guide data-driven decisions.
Students who complete this four-course certificate program may decide to continue on and apply to the MS in Data Analytics. If accepted into the master's program, the credits earned toward the completion of the certificate will be counted toward the master's program.
Data analytics is a fast-growing field and often the key to business strategy and the solution to complex questions. Data analysts are sought after in nearly every field.
Please include courses and grades for the prerequisites of Calculus II, Statistics, and Computer Programming on the provided space in the application.
See Tuition and Financial Aid information for GSAS Programs. Note: This program is not eligible for federal loans or Tufts tuition scholarships.
The interdisciplinary Data Analytics Program is guided by an advisory board of professionals in the field, who help ensure the program provides rigorous courses in the analytical skills that are sought after across the arts, humanities, and sciences as well as within the business community.
Research/Areas of Interest: Applied Urban, Housing, Education, Environmental, and Labor economics.
Research/Areas of Interest: Condensed Matter Physics, Soft materials, Colloids, Liquid Crystals, Computational Physics, Physics Education Soft matter physics is the study of matter that is all around us in everyday life: soaps, oil, foods, sand, foams, and biological matter. All of these are readily deformable at room temperature and combine properties of both fluids and solids. Despite their ubiquity, these materials are extremely complicated. Unlike simple fluids like water, they have rich internal structure; unlike crystalline solids they are typically not periodically ordered. Moreover, they exist in long-lived metastable states far from equilibrium and respond to stimuli such as applied electric and magnetic fields, temperature and pressure. My work seeks to understand how these materials respond to shape: how they self-organize on curved surfaces or in complex geometries and how this knowledge can be used both to sculpt desirable shapes at the microscopic scale and create shape changing systems like soft robots. We use high performance computing to simulate and predict these behaviors and work closely with experimentalists at Tufts and beyond.
Research/Areas of Interest: Greek religion, Greek epigraphy, Medieval Latin, Digital Humanities
Research/Areas of Interest: Applied dynamical systems, applied probability theory, kinetic theory, agent-based modeling, mathematical models of the economy, theoretical and computational fluid dynamics, complex systems science, quantum computation Current research emphasis is on mathematical models of economics in general, and agent-based models of wealth distributions in particular. The group's work has shed new light on the tendency of wealth to concentrate, and has discovered new results for upward mobility, wealth autocorrelation, and the flux of agents and wealth. The group's mathematical description of the phenomenon of oligarchy has also shed new light on functional analysis in general and distribution theory in particular. Secondary projects include new directions in lattice Boltzmann and lattice-gas models of fluid dynamics, kinetic theory, and quantum computation.
Research/Areas of Interest: Animal Cognition and Learning
Research/Areas of Interest: Labor economics, public health, nursing
Research/Areas of Interest: Categorical data analysis, survival data analysis, longitudinal data analysis, latent variable analysis, smoking behavior, substance abuse, major depression, disparity in financial access
Research/Areas of Interest: Urban Analytics; Big Data Analytics; Urban Planning and Science; Spatial Data Science; Urban mobility;
Research/Areas of Interest: Machine learning, harmonic analysis, statistical learning, graph theory, data science, computational mathematics, image processing, signal processing