Master of Computational Data Science

16 Months On Campus Masters Program

Carnegie Mellon University

Program Overview

The Master of Computational Data Science (MCDS) at Carnegie Mellon University is designed for students who want to develop the technical skills needed to build, manage, and improve large-scale data systems. Offered through the Language Technologies Institute within the School of Computer Science, the programme combines computer science, machine learning, data analytics, and practical software development. Students can choose from three specialisations: Systems, Analytics, and Human-Centered Data Science. The degree typically takes 16 months to complete, with an extended 20-month option for students who want more time for coursework and research.

Curriculum Structure

The programme requires a minimum of 144 academic units and combines five core courses, a specialisation, electives, seminars, an internship, and a capstone project. Students choose their specialisation by the end of their first semester.

Core Courses

All students build a foundation in computational data science, machine learning, cloud computing, and interactive data systems through five core courses:

11-637: Foundations of Computational Data Science
15-619: Cloud Computing
10-601: Machine Learning
05-839: Interactive Data Science
11-631: Data Science Seminar

Specialisation Options

Students select one of three areas to develop deeper expertise.

1. Systems

This specialisation focuses on designing and building the infrastructure needed to store, manage, and process large volumes of data. Relevant courses include:

15-605: Operating System Design and Implementation
15-618: Parallel Computer Architecture and Programming
15-640: Distributed Systems
15-645: Database Systems
15-719: Advanced Cloud Computing
15-746: Storage Systems

2. Analytics

This specialisation develops skills in extracting insights from large datasets using machine learning, data mining, and analytical systems. Relevant courses include:

10-605: Machine Learning with Large Datasets
10-708: Probabilistic Graphical Models
11-642: Search Engines
11-663: Applied Machine Learning
11-711: Advanced Natural Language Processing
11-775: Large Scale Multimedia Analysis

3. Human-Centered Data Science

This specialisation explores how people interact with data-driven technologies and how research methods can improve the design of digital systems. Students study areas such as human-computer interaction, user research, and interactive data analysis. Relevant courses include:

05-610: User Centered Research and Evaluation
05-816: Applied Research Methods
05-839: Interactive Data Science
05-821: Mobile and Pervasive Computing
11-667: Large Language Models Methods and Applications

Electives and Seminar

Students complete an approved graduate-level elective from the School of Computer Science, along with the required data science seminar and capstone planning activities. These courses allow students to explore additional technical subjects or strengthen their chosen specialisation.

Internship and Capstone Project

The programme includes a summer internship that gives students an opportunity to apply their knowledge in a professional setting. Students then complete a group-oriented capstone project, which may involve university research or an industry-sponsored challenge. The project involves developing a practical data science solution and presenting the results.

Study Duration

Standard Option: 16 months, including fall and spring semesters, a summer internship, and a final fall semester.
Extended Option: 20 months, including an additional spring semester for further coursework or research.

Focus Areas

Computational Data Science
Machine Learning and Artificial Intelligence
Big Data Analytics
Cloud Computing and Distributed Systems
Database Management and Data Engineering
Human-Computer Interaction
Natural Language Processing
Search Engines and Information Retrieval
Large-Scale Data Processing
Data Systems Design and Development
Interactive Data Analysis and Visualisation

Learning Outcomes

Students learn how to design and develop systems that can handle large volumes of data efficiently. They gain experience in machine learning, data analytics, cloud computing, database technologies, and distributed systems, helping them understand both the technical infrastructure behind data platforms and the information generated by them.

The programme also develops problem-solving, teamwork, research, and project management skills. Through the internship and capstone project, students gain practical experience working on real-world data challenges and learn to turn complex technical ideas into effective solutions.

Professional Alignment (Accreditation)

The university does not specify a separate professional or industry accreditation for the Master of Computational Data Science programme. Its curriculum is designed around practical competencies in data engineering, machine learning, analytics, and large-scale computing.

Reputation (Employability Rankings)

Carnegie Mellon University highlights the strong industry relevance of the programme, with graduates pursuing careers as software engineers, data scientists, and project managers at technology, software services, and social media companies. The internship and industry-oriented capstone provide opportunities to gain practical experience and develop professional connections.

Experiential Learning (Research, Projects, Internships etc.)

At Carnegie Mellon University, the Master of Computational Data Science program is designed to provide students with hands-on experience that bridges the gap between theory and practice. Students engage in a variety of experiential learning opportunities that equip them with the practical skills needed to excel in the rapidly evolving field of data science. The program emphasizes collaboration, innovation, and real-world application, ensuring that graduates are well-prepared for their careers.

Here are some key aspects of the experiential learning opportunities available to students in this program:

- Collaborative Projects: Students work on team-based projects that simulate real-world data science challenges, allowing them to apply their skills in a collaborative environment.
- Internships: The program encourages internships with leading tech companies and organizations, providing students with invaluable industry experience and networking opportunities.
- Research Labs: Access to state-of-the-art research facilities, such as the Machine Learning Department and the Human-Computer Interaction Institute, where students can engage in cutting-edge research.
- Software and Tools: Students gain proficiency in essential data science tools and programming languages, including Python, R, TensorFlow, and Apache Spark, which are integral to their coursework and projects.
- Field Trips and Industry Visits: Opportunities to visit tech companies and attend industry conferences, giving students insights into the latest trends and practices in data science.
- Libraries and Resources: Access to extensive library resources, including the Carnegie Mellon University Libraries, which provide a wealth of academic materials and research support.
- Networking Events: Regular events and workshops that connect students with alumni and industry professionals, fostering relationships that can lead to job opportunities.

These experiential learning components are designed to enhance your educational journey and prepare you for a successful career in data science.

By choosing the Master of Computational Data Science at Carnegie Mellon, you’re not just earning a degree; you’re gaining the skills and experiences that will set you apart in the job market.

Progression & Future Opportunities

The Master of Computational Data Science at Carnegie Mellon University equips graduates with the skills and knowledge to excel in a rapidly evolving field. Alumni often find themselves in high-demand roles such as Data Scientist, Machine Learning Engineer, and Data Analyst. With a strong foundation in computational methods and data analysis, graduates are well-prepared to tackle complex challenges in various industries:

- University Services: The Career and Professional Development Center offers personalized career coaching, resume workshops, and networking events to connect students with potential employers. Additionally, the university hosts job fairs and provides access to exclusive job postings through its online portal.

- Employment Stats and Salary Figures: According to recent data, over 90% of graduates secure employment within six months of graduation, with starting salaries averaging around $100,000 per year, reflecting the high demand for skilled professionals in this field.

- University–Industry Partnerships: Carnegie Mellon has established partnerships with leading tech companies such as Google, Microsoft, and Amazon, providing students with internship opportunities and real-world projects that enhance their learning experience.

- Long-term Accreditation Value: The program is accredited by the Middle States Commission on Higher Education, ensuring that the education you receive meets rigorous academic standards and is recognized globally.

- Graduation Outcomes: Graduates of the program have gone on to work at prestigious organizations, contributing to innovative projects and research that shape the future of technology and data science.

Further Academic Progression: After completing the Master of Computational Data Science, students have the option to pursue a Ph.D. in related fields such as Machine Learning, Artificial Intelligence, or Human-Computer Interaction. This advanced study can lead to careers in academia, research, or high-level industry positions, further enhancing your expertise and career prospects.

Choosing to study at Carnegie Mellon not only opens doors to immediate job opportunities but also lays the groundwork for a successful and fulfilling career in data science.