Master of Science in Data Science

NA On Campus Masters Program

Brown University

Program Overview

The Master of Science in Data Science at Brown University brings together computer science, mathematics and statistics to help students build the skills needed for careers in the growing field of data science. The programme covers data engineering, statistical analysis, machine learning, deep learning and generative AI, giving students opportunities to work on practical projects and explore how data can be used to solve real-world problems. Students also learn to consider the ethical and social impact of data-driven technologies.

Curriculum Structure

The programme requires 9 courses, including four core courses, four restricted electives and one unrestricted elective. Students build a strong foundation in mathematics, statistics, data engineering and computational science before exploring advanced topics based on their interests. The flexible curriculum allows students to focus on areas such as machine learning, data analytics, computational biology and healthcare analytics.

Curriculum Progression

The university offers a flexible curriculum rather than a fixed year-by-year course sequence. Students choose their courses according to their academic interests and career goals.

Foundational Stage: Students begin with Hands-on Data Science (DATA 1030), where they learn how to collect data, explore datasets, build models and communicate their findings. Data Engineering (DATA 1050) introduces them to data processing, database systems and the technologies used to manage large amounts of information.

Advanced Stage: Students strengthen their mathematical and analytical skills through Math in Machine Learning (DATA 2010), covering important concepts such as linear algebra, calculus, probability and statistics. They can also explore subjects such as Statistical Learning (DATA 2020), Machine Learning: From Theory to Algorithms (DATA 2060) and Deep Learning (CSCI 1470 or CSCI 2470), gaining a deeper understanding of how intelligent systems learn from data and make predictions.

Final Stage – Practical Application: Students complete Data Science Practicum (DATA 2050), where they work on an in-depth data science project. Working in teams with Brown faculty members or external companies, students apply what they have learned in statistics, machine learning and computational science to address practical challenges and present their findings.

Focus Areas: Data Science, Data Engineering, Statistical Learning, Mathematical Foundations of Machine Learning, Machine Learning, Deep Learning, Generative AI, Agentic AI, Data Collection, Exploratory Data Analysis, Database Engineering, Computational Statistics, Data Visualisation, Responsible Data Science, Algorithmic Fairness, Healthcare Analytics, Computational Biology, Genomics, Predictive Modelling and Data Ethics.

Learning Outcomes: Graduates develop the ability to collect, organise and analyse data, identify meaningful patterns and build computational models to solve practical problems. They learn to apply statistical methods, machine learning and deep learning techniques while gaining experience in communicating their findings to both technical and non-technical audiences. The programme also helps students understand the ethical and social responsibilities involved in using data-driven technologies.

Professional Alignment (Accreditation): The programme is offered through Brown University's Data Science Institute, with academic connections to the Department of Computer Science and Division of Applied Mathematics. Students explore responsible data science, algorithmic fairness and the ethical considerations involved in developing and using data-driven technologies. The university does not specify a separate professional or industry accreditation for this master's degree.

Reputation (Employability Rankings): Brown University's programme prepares graduates for career opportunities in data science, analytics, machine learning and computational research. Students can develop specialised knowledge in areas such as computational biology, genomics, healthcare analytics and public-interest data science. The capstone project also gives students an opportunity to work with Brown faculty members and external companies on real-world challenges. The university does not publish a specific programme-level employability ranking or verified graduate employment percentage on the official programme page.

Programme Duration and Study Mode: The programme is delivered on campus and offers flexible completion options ranging from 12 to 24 months. The default duration is 21 months for students beginning in the fall and 24 months for students beginning in the spring. Fall entrants may choose 12, 16, 21 or 24 months, while spring entrants may choose 16, 21 or 24 months.

Experiential Learning (Research, Projects, Internships etc.)

At Brown University, the MS in Data Science program emphasizes experiential learning, ensuring that students not only grasp theoretical concepts but also apply them in real-world scenarios. The program is designed to equip you with practical skills through hands-on projects, collaborative work, and access to cutting-edge facilities and tools. Students engage in various group projects, internships, and research opportunities that enhance their learning experience and prepare them for careers in data science.

Here are some key aspects of experiential learning in the MS Data Science program at Brown:

- Collaborative Projects: Engage in team-based projects that simulate real-world data challenges, allowing you to work alongside peers and faculty.
- Internship Opportunities: Gain valuable industry experience through internships with leading companies, providing a platform to apply your skills in a professional setting.
- Access to Advanced Software: Utilize industry-standard tools and software such as Python, R, SQL, and Tableau, which are essential for data analysis and visualization.
- Research Laboratories: Work in dedicated research labs equipped with the latest technology, fostering an environment for innovation and exploration in data science.
- Data Science Institute: Collaborate with the Brown Data Science Initiative, which promotes interdisciplinary research and provides resources for students to engage in impactful projects.
- Field Trips and Workshops: Participate in field trips to tech companies and workshops led by industry experts, enhancing your understanding of the data science landscape.
- Library Resources: Access extensive library resources, including databases and journals, to support your research and coursework.

These experiential learning opportunities are designed to ensure that you graduate not only with a degree but also with the practical skills and experiences that employers are looking for.

Choosing Brown means choosing a program that prioritizes your hands-on learning and prepares you for a successful career in data science!

Progression & Future Opportunities

Pursuing an MS in Data Science at Brown University opens up a world of opportunities, with graduates finding success in various high-demand roles. Typical job positions include Data Scientist, Machine Learning Engineer, and Business Intelligence Analyst. The program equips students with the skills and knowledge necessary to thrive in these roles:

- University Services: Brown offers robust career services, including personalized career counseling, resume workshops, and networking events that connect students with industry leaders.
- Employment Stats and Salary Figures: Graduates from the Data Science program have an impressive employment rate, with many securing positions within six months of graduation. The average starting salary for data science roles can range from $90,000 to over $120,000, depending on the position and location.
- University–Industry Partnerships: Brown has established partnerships with leading tech companies and organizations, such as IBM and Microsoft, providing students with internship opportunities and real-world project experience.
- Long-term Accreditation Value: The MS in Data Science is accredited and recognized globally, ensuring that your degree holds value in the job market for years to come.
- Graduation Outcomes: Alumni of the program have gone on to work at prestigious companies, contributing to innovative projects and research, which speaks volumes about the quality of education and training received at Brown.

Further Academic Progression: After completing the MS in Data Science, students have the option to pursue a Ph.D. in Data Science or related fields, allowing them to delve deeper into research and academia. Additionally, graduates can explore certifications in specialized areas such as artificial intelligence or big data analytics, further enhancing their expertise and career prospects. This pathway not only solidifies their knowledge but also positions them as leaders in the evolving field of data science.