Best Accredited Universities for Doctoral Phd program in Data Science

In the PHD program offered by the Department of Statistics and Data Science, the fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining, and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

The QBS Masters of Science degree in Health Data Science provides training in data wrangling, exploratory data analysis, statistical modelling, machine learning, data visualization and communication. Graduates will have competencies in the management, analysis and interpretation of data from medicine, computational biology, pharma and genomics.

The PhD program in Data Science advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solution of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

The Biomedical Data Science PHD program provides students the training they need to make sense of large-scale biomedical data, and to be scientific leaders in the team science that invariably accompanies such data. Unique features of the program include cross-training in computer science and biostatistics, and research rotations mentored by a program faculty member jointly with a scientific collaborator. The current explosion of biomedical data provides an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care.

The Advanced Data Science option aims to educate the next generation of thought leaders who will both build and apply new methods for data science. This option will help to educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools. The goal of this option is not to educate all students in the foundations of data science but rather to provide advanced education to the students who will push the state-of-the-art in data science method.

WPI’s interdisciplinary PhD in Data Science is one of the first programs of its kind in the nation. The program recognizes that traditional data processing applications can no longer handle today’s large and complex datasets. New models are needed to handle big data, and knowledgeable graduates with expertise in turning those observations into meaningful recommendations are in high demand. Students are working alongside faculty and industry partners to analyze, capture, search, share, store, transfer, query, and visualize huge amounts of data to solve real-world challenges.

At Stevens, the interdisciplinary Ph.D. in Data Science prepares inquisitive students to become pioneers in this space through a rigorous curriculum emphasizing mathematical and statistical modeling, machine learning, computational systems and data management. The program is administered through both the Schaefer School of Engineering and Science and the School of Business at Stevens, ensuring a diverse curriculum that responds to demand for data scientists with extensive knowledge of the theories, techniques and applications associated with data and artificial intelligence.

The pan-disciplinary Ph.D. in Complex Systems and Data Science is housed in the College of Engineering and Mathematical Sciences, providing strong computational and theoretical training, but varying with each student's chosen area of focus. Students work within research groups across campus. With core courses such as Data Science, Principles of Complex Systems and Modeling Complex Systems and electives such as Machine Learning, Complex Networks, Evolutionary Computation, Human Computer Interaction and Data Mining, students receive core training in empirical, computational and theoretical methods for describing and understanding complex systems and predicting, controlling, managing and creating such systems.

PhD in Computational and Data-enabled Science and Engineering program train scientists to analyze big data and multisource datasets to solve these grand challenges, by bringing together the talents of faculty across several departments including the Departments of Mechanical and Aerospace Engineering, Chemical and Biological Engineering, Computer Science and Engineering, Math, Physics, and Chemistry. A Master's degree in a related field is required for admission to the PhD program. Some related fields of study include engineering, mathematics, physics, chemistry, marketing, business, and pharmacy.