Welcome to the Master of Science in Data-Enabled Computational Engineering and Science program at Brown University, one of the prestigious institutions in the United States. This transformative program is designed for individuals keen on exploring the complex realm of data science, engineering, and computational methodologies. Emerging technologies demand a skilled workforce adept at leveraging extensive data for informed decision-making and innovative solutions. Our curriculum is meticulously crafted to provide students with the necessary theoretical background and practical skills to excel in a variety of sectors, including engineering, healthcare, finance, and technology.

The program spans 18 months, allowing students to delve deeply into critical areas such as Data Science, Machine Learning, and Computational Engineering. Students will engage in a rigorous curriculum that includes core courses in Applied Statistics, Database Systems, and Machine Learning. In addition to the foundational courses, students can select from a range of electives tailored to their interests and career aspirations. The program not only emphasizes technical proficiency but also fosters analytical thinking and problem-solving skills vital for addressing real-world challenges.

Students in the program will receive instruction from acclaimed faculty members who bring a wealth of expertise and research experience to the classroom. Our faculty are leaders in their fields, contributing to groundbreaking research and advancements in data science and engineering. They employ diverse teaching methodologies, including hands-on projects, collaborative group work, and case studies, to ensure a comprehensive learning experience. This interactive approach encourages students to apply theoretical concepts to practical situations, enhancing their understanding of complex data-driven problems.

Research opportunities abound at Brown University. Students will have access to cutting-edge laboratories and research centers dedicated to data science, computational engineering, and science applications. Collaborations with faculty on research projects provide an invaluable chance to engage with current industry trends and innovative technologies. Whether through individual research, group projects, or internships, students will have the opportunity to contribute significantly to the academic community while building a robust portfolio of experience that employers value.

The program is closely integrated with industry, offering students numerous internship possibilities and networking opportunities. Brown University has established strong connections with various leading organizations and companies across sectors, which facilitates internships that provide real-world experience. These connections are instrumental in enhancing students’ employability and preparing them for successful careers after graduation. The robust alumni network further aids in providing mentorship and potential job placements in top firms.

Upon graduation, students can expect to explore diverse career pathways, including roles such as Data Scientist, Machine Learning Engineer, Data Analyst, and Research Scientist. The average base salary for graduates is approximately $110,000, showcasing the lucrative nature of careers in this field. Graduates have found success in various industries, from finance to healthcare, securing positions in companies known for their innovative practices.

Alumni of the program have shared success stories that reflect the program’s impact on their careers. Many have moved on to influential roles in Fortune 500 companies, startups, and research institutions. These testimonials highlight the program's ability to equip students with the critical skills necessary to thrive in today’s data-driven world. The support from the faculty and the comprehensive curriculum have been pivotal in their career advancements.

Program Curriculum and Specific Courses

The Master of Science in Data-Enabled Computational Engineering and Science program offers a rich curriculum tailored to equip students with essential skills. Key components of the curriculum include:

• Core Courses - Students will engage in foundational courses such as Applied Statistics, Database Systems, and Practical Machine Learning.
• Electives - A selection of electives allows students to specialize in areas like Data Visualization, Advanced Machine Learning, or Cybersecurity.
• Capstone Project - A hands-on project that synthesizes knowledge gained throughout the program, allowing students to work on real-world problems.

Research Opportunities and Resources

Students in the program have access to extensive research resources, including state-of-the-art laboratories and collaborative research groups. Opportunities exist to work alongside faculty on impactful research projects, often leading to publications and presentations at conferences. This integration of research within the academic framework enhances learning and provides vital experience.

Industry Connections and Internship Possibilities

The program’s strong ties with various industries facilitate numerous internship opportunities. Students are encouraged to pursue internships that complement their studies, providing valuable experience and networking prospects. These internships often lead to job offers post-graduation, significantly enhancing students' career trajectories.

Career Pathways and Job Outcomes

Graduates of the program typically find themselves in high-demand positions across a multitude of sectors. Possible job roles include:

• Statistician
• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Software Engineer

Why Study Data-Enabled Computational Engineering and Science at Brown University

• Access to a robust curriculum that integrates theoretical knowledge with practical skills.
• Learn from esteemed faculty who are leaders in their respective fields.
• Opportunities for meaningful research and collaborative projects with industry leaders.
• Strong industry connections and high internship placement rates, leading to promising job prospects.
• A vibrant alumni network that can provide mentorship and career guidance.

To apply, candidates should possess a Bachelor's degree in a related field, submit academic transcripts, a resume, and letters of recommendation. The program also requires standardized test scores such as the GRE and English proficiency tests (IELTS or TOEFL) for non-native speakers. The application deadline for Fall intake is April 1st, 2024, ensuring ample time for prospective students to prepare their applications.