Top Masters Programs in Data Science

The Master of Science in Statistics and Data Science at Yale University is a comprehensive and rigorous program tailored for individuals aiming to delve deep into the realms of data science. This program is meticulously designed to encompass a wide array of methodologies central to the field, including advanced data analysis, effective data visualization techniques, and the adept use of a multitude of data science tools.

By integrating foundational principles from mathematics, statistics, and computer science, the curriculum is structured to not only impart theoretical knowledge but also to provide practical skills essential for solving complex problems in various industry settings. Students enrolled in this program will gain hands-on experience and develop a robust skill set that prepares them to address and navigate the complexities of real-world data science challenges.

The features of the program are:

• The program requires the completion of 8 to 12 courses, each carefully curated to cover essential aspects of statistics and data science. This range allows students to tailor their learning experience according to their interests and career goals.
• Designed as a full-time program, it spans 24 months, providing students ample time to immerse themselves in learning and apply their knowledge through practical projects and collaborations.
• Reflecting the program's focus on practical skills and industry readiness, there is no requirement for a thesis, allowing students to concentrate on coursework and hands-on projects.
• In line with the evolving nature of educational credentials, the program does not require GRE scores for admission, making it more accessible to a diverse pool of applicants.

The Data Science master's program, jointly led by the Computer Science and Statistics faculties and administered through the Institute for Applied Computational Science (IACS), trains students in the rapidly growing field of data science.

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains. The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. The program focuses on topics such as reproducible data analysis, collaborative problem-solving, visualization and communication, and security and ethical issues that arise in data science.

To earn the Master of Science in Data Science, students must complete 12 courses. This requires students to be on campus for at least 3 semesters (one and a half academic years). Some students will choose to extend their studies for a fourth semester to take additional courses or complete a master’s thesis research project.

Data hold enormous potential to advance discovery, decision making, and the continuous improvement of learning.

The program in education data science integrates the complex and rich disciplines of computer science and statistics with education and social science theory. It prepares students to apply data science skills and techniques to education research and practice.

This two-year, full-time residential course of study combines academic coursework with a mentored internship or research assistantship.

Stanford is known for building data science expertise across disciplines. The Graduate School of Education has been a pioneer in research methods, both quantitative and qualitative, and is ideally situated to generate innovations in research and applications to improve education for all learners. The master of science in education data science leverages these strengths to prepare leaders in the field.

The program draws students from a diverse set of backgrounds. What they share is a passion for improving equitable and effective learning for all. Ideal candidates will have a demonstrated interest in applying data science skills and techniques to education research and practice.

Education data science graduates are among the first in the world with cutting-edge training in both data science and education theory and practice. Their expertise is valuable to education research firms, school districts and systems, universities, state and federal governmental education agencies, ministries of education in other countries, education technology companies, and educational for-profit ventures.

Using data to improve education in humanitarian settings.

Mei Tan is a PhD student at the GSE.

The University of Chicago's Master's in Applied Data Science program offers both in-person and online options to accommodate diverse student needs. The in-person program, eligible for OPT STEM, is ideal for early career professionals and can be completed full-time in 12-18 months, with part-time options available. The online program provides flexibility with in-person Immersion Weekends twice a year, fostering networking opportunities. The curriculum consists of 12 courses, including core, elective, and Capstone Project courses, ensuring relevance in the dynamic data science field. Students can choose part- or full-time study schedules to fit their lifestyles and career goals. The program emphasizes interactive learning through synchronous online classes and leverages advanced technology for collaboration.

The benefits of this online Data Science program are:

• Combines online study with in-person immersion weekends
• Weekly synchronous classes for live interaction
• Comprehensive curriculum includes core, elective, and Capstone Project courses
• Curriculum reviewed annually to stay current in data science
• Part-time and full-time study options available
• The in-person program is OPT STEM-eligible

The Data Science program balances theory and applications so that you can advance your career long-term.

The rigorous curriculum focuses on the fundamentals of computer science, statistics, and applied mathematics, while incorporating real-world examples. With options to study online and on-site in state-of-the-art facilities at the Johns Hopkins Applied Physics Laboratory, students learn from practicing engineers and data scientists.

Penn’s Master of Science in Engineering (MSE) in Data Science prepares students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications.

The Data Science Program can typically be completed in one-and-a-half to two years. It blends leading-edge courses in core topics such as machine learning, big data analytics, and statistics, with a variety of electives and an opportunity to apply these techniques in a domain specialization – a depth area – of choice.

The depth area offers both preparatory coursework and a thesis or practicum in a data science application area. Potential areas of specialization include network science (the Warren Center for Network and Data Science), digital humanities (the Price Lab for Digital Humanities), biomedicine (the Institute for Biomedical Informatics), and public policy (the Penn Wharton Budget Model and the Annenberg Center for Public Policy) — as well as more traditional opportunities in Computer and Information Science and Electrical and Systems Engineering. For students interested in applying data analysis and modeling to other areas within engineering and the physical sciences, Penn offers a specialized and synergistic program in Scientific Computing.

Penn provides the perfect environment for data science enthusiasts, with its strong cross-disciplinary traditions. Biomedical informatics, communications and public policy, robotics, machine learning and artificial intelligence, and data privacy are of broad interest across campus. There are also several extracurricular student groups, including the Penn Data Science Group, which runs speaker panels, events with companies, data projects and kaggle teams . The Philadelphia area is host to multiple biomedical data and cloud startups.

Northwestern offers an online Master of Science in Data Science program, providing aspiring professionals with the technical analysis and leadership expertise required to succeed within the data-driven economic landscape of today. MSDS candidates gain hands-on experience with relational, document, and graph databases as well as pioneering open-source systems such as R, Python, Go, and TensorFlow/Keras for deep learning.

Those enrolled in the data science master's program can take advantage of two options for completing the degree - part-time and accelerated full-time. Within these formats, there are five specializations to select from: Analytics and Modeling, Analytics Management, Artificial Intelligence, Data Engineering, or Technology Entrepreneurship. Plus, those interested have access to a wide variety of elective courses such as financial machine learning, deep learning, analytics systems engineering, and business process analytics. The part-time option is 100% online with nonresidential study while the one-year accelerated version provides an expedited path to completion. All routes provide customizable curriculums designed to help you reach your professional ambitions.

Making the most of your time, a program can be completed in one year by taking three classes each quarter with options for day, night, and online courses. For those on an expedited pace, there are five specializations to choose from; however, only students enrolled part-time via the Internet may pursue the broad data science base course.

The Duke University Master in Interdisciplinary Data Science (MIDS) is home for creative problem-solvers who want to use data strategically to advance society. The university is cultivating a new type of quantitative thought leader who uses disruptive computational strategies to generate innovation and new insights.

MIDS combines rigorous computational and technical training with field knowledge and repeated practice in critical thinking, teamwork, communication, and collaborative leadership to generate data scientists who can add value to any field.

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 mission of the department of Biomedical Data Science at Geisel School of Medicine is to advance scholarship and education for the quantitative and computational analysis of biomedical and health data and to unite and promote Biomedical Informatics and Biostatistics as essential disciplines for the mentoring and academic development of faculty and the training and education of the next generation of data scientists.

Brown University's Data Science Initiative provides an opportunity for aspiring professionals to learn and grow in a master's program that is focused on preparing students for successful careers in Data Science and Big Data Management. Four departments have come together to make this possible, creating a rigorous education path that dives into machine learning, data mining, security & privacy, as well as visualizations and management of data. Students enrolled in this program will gain insight into ethical considerations surrounding data science applications while having hands-on experience with real-life problems encountered by today's leading experts. This graduate degree ensures graduates receive a foundational understanding of methods and algorithms for data science across topics such as mathematics, statistics, and computer science.

For those enrolled in the Data Science Master's Program, there is a set curriculum of nine credits. This includes eight compulsory courses and one elective. Typically, it takes 12 months to complete these requirements from September through August; however, students who are exceptionally capable may be able to finish their studies within nine months.

The program's course structure, including the capstone experience, will ensure that the students meet the goals of acquiring and integrating foundational knowledge for data science, applying this understanding in relation to specific problems, and appreciating the broader ramifications of data–driven approaches to human activity.