Online MS in Computer Science & Artificial Intelligence
The Online Master of Science in Computer Science & Artificial Intelligence (OMSCS) offers a rigorous, project-based curriculum that blends foundational systems engineering with modern artificial intelligence and data-driven computing. The program provides both flexibility and depth—allowing students to build the technical expertise and problem-solving mindset needed to design, implement, and manage scalable, intelligent systems in real-world contexts.
The course of study progresses through two parallel sequences: AI and Systems. Students begin with preparatory bootcamps that refresh essential programming, mathematics, and theoretical concepts before moving into four terms of integrated coursework. Each term pairs one AI module and one Systems module, ensuring that students simultaneously strengthen their understanding of computing infrastructure and the intelligent applications built upon it. Through hands-on projects, case studies, and collaborative assignments, students gain experience with contemporary tools and frameworks used in industry.
The program’s combination of academic rigor and practical relevance has made it a destination for students seeking impactful, applied learning. It reflects the pace and innovation of today’s technology landscape—where students don’t just study AI and systems but actively design and deploy practical solutions to real professional challenges. The flexible online format, enhanced by interactive modules and mentorship from expert faculty, empowers students worldwide to advance their education while continuing their careers.
Graduates pursue a wide range of career paths, including machine learning engineer, cloud architect, data systems engineer, software development manager, and AI product lead. The program’s interdisciplinary focus also prepares students for technical leadership roles that require fluency across data, infrastructure, and intelligent systems.
The program emphasizes applied learning, and the assignments and the projects within the different program modules will ensure a well-rounded education on emerging AI topics and technologies.
Learning Outcomes
By the end of the program, students will be able to:
- Apply foundational concepts in algorithms, data structures, and systems architecture to analyze and solve complex computing problems.
- Design and implement scalable, secure, and efficient software systems using modern programming languages, cloud infrastructures, and distributed technologies.
- Develop and evaluate machine learning and artificial intelligence models that address real-world challenges in diverse domains.
- Integrate principles of data management, networking, and computational efficiency to optimize end-to-end system performance.
- Critically assess the ethical, social, and professional implications of AI and computing technologies, and formulate responsible and sustainable design practices.
- Collaborate effectively in teams, communicating technical ideas, trade-offs, and project outcomes to diverse audiences.
- Plan, execute, and present a capstone project that demonstrates mastery in applying advanced computing knowledge.
Requirements
The Online Master of Science in Computer Science & Artificial Intelligence requires the successful completion of 30 units (10 modules, 3 units each). Most students complete the program in four terms of part-time study, following two short pre-term bootcamps. The flexible online structure allows students to balance professional and academic commitments while maintaining steady progress toward degree completion. The students can start the program either in the fall or in the spring terms. The program is project-based and not exam-based. 30 units is the minimum for a master’s program and is consistent with other online programs both at BU and in the broader market.
Preparatory Bootcamps (noncredit, required for students to refresh their background)
- Bootcamp 1: Mathematical and Theoretical Foundations–Covers linear algebra, probability, algorithms, and discrete structures relevant to the program.
- Bootcamp 2: Programming Warm-Up–Introduces Python, version control (Git/GitHub), and basic data manipulation.
Core Curriculum (30 units total)
Students complete eight modular courses organized into two parallel tracks—Artificial Intelligence (AI) and Systems—with one module from each track taken each term. In addition to these courses, students will also attend two modules (each spanning two terms), one with AI leaders from industry and another completing a capstone project, in order to enhance their project portfolio.
AI Curriculum
- AI Module 1 (CAS CX 641) (3 units)–Introduction to AI
Prerequisite: Programming proficiency (covered in Bootcamp 2) - AI Module 2 (CAS CX 642) (3 units)–Applied Machine Learning
Prerequisite: CAS CX 641 - AI Module 3 (CAS CX 643) (3 units)–Generative Models
Prerequisite: CAS CX 642 - AI Module 4 (CAS CX 644) (3 units)–Systems Deployment and Responsible Innovation
Prerequisite: CAS CX 643
Systems Curriculum
- Systems Module 1 (CAS CX 651) (3 units)–Foundations of Programming and Systems
Prerequisite: Programming proficiency (covered in Bootcamp 2) - Systems Module 2 (CAS CX 652) (3 units)–Cloud Computing
Prerequisite: CAS CX 651 - Systems Module 3 (CAS CX 653) (3 units)–Scalable Data Analytics Systems
Prerequisite: CAS CX 652 - Systems Module 4 (CAS CX 654) (3 units)–GPU Computing for Data and Cloud Applications
Prerequisite: CAS CX 653
Two (2) 2-term long 3-unit modules
- AI in Industry Series (CX 698) (3 units): Terms 1 and 2: Series of industry speakers that bring their experience of data analysis and machine learning in the classroom (fall and spring)
Prerequisite: No prerequisite - Capstone Project (CX 699) (3 units): Terms 3 and 4: Capstone project: Build your own LLM from scratch (fall and spring)
Prerequisite: CAS CX 641, CX 642, CX 651, CX 652
There is no formal internship requirement
There is no international language requirement. All classes will be given in English.
Additional Information
The students can enter the program both in the fall and in the spring term.
Admissions Information
This program is designed for working professionals who design, build, or lead technology—from software developers and data specialists to domain experts in engineering or applied sciences—who want to deepen their computer science foundation and apply AI to real-world innovation. It targets people who may have an undergraduate degree in computer science or tangential areas (e.g., engineering, physics, math, statistics, etc.) but have careers in development, management, etc., who want to update and refresh their technical skills in light of current advances in underlying technologies. These are people who have had prior exposure to coding. Background in AI/ML is not necessary, but it will definitely be useful.
