Duke University

How this program compares

Duke University's Master of Engineering in ECE - Data Analytics & Machine Learning lists about $102.9K total tuition — about 140% above the $42.9K AI master's average (95th percentile in our data). It runs on a full-time schedule (on-campus).

Admission Snapshot

Typical admitted student: A bachelor's degree in electrical engineering, computer engineering, computer science, or a related STEM field with a minimum GPA of 3.0 is required, along with relevant coursework in programming, linear algebra, and probability. GRE scores are optional, and professional experience in engineering or data-related roles is preferred.

About This Program

This specialization within the Master of Engineering in Electrical and Computer Engineering focuses on the mathematical foundations and practical deployment of AI systems that scale in real-world environments. Coursework concentrates on Machine Learning and AI Engineering / Applied AI. Most students complete it in about 1.5 years.

Design and implement machine learning models for engineering applications.

At roughly $102.9K total, Duke University's Master of Engineering in ECE - Data Analytics & Machine Learning sits above the $42.9K average among AI master's programs we track — the 95th percentile on price. Applicants should weigh that premium against the program's outcomes and brand.

It is one of 31 AI-related programs we track in North Carolina, of which about 45% offer an online option. On price, it comes in higher than about 74% of the North Carolina programs in our data, where typical tuition runs near $69.0K.

Graduates frequently move into roles such as Data Scientist, with reported salaries around $130,000.

Career Outcomes

Graduates of Duke University's Master of Engineering in ECE - Data Analytics & Machine Learning most often target Data Scientist roles, where reported compensation runs around $130,000. Its focus on Machine Learning and AI Engineering / Applied AI maps directly to how employers screen for specialized skills rather than generic degrees. Federal projections for this occupational area point to roughly 23% growth this decade — verify the current figure on the BLS Occupational Outlook Handbook before you rely on it.

• 1. Machine Learning Engineer
• 2. Data Analytics Engineer
• 3. AI Systems Engineer
• 4. Embedded ML Specialist

What You'll Learn

• Design and implement machine learning models for engineering applications
• Analyze large datasets using data analytics techniques in ECE contexts
• Apply deep learning to signal processing and embedded systems
• Develop optimized algorithms for real-time data processing and AI inference

Curriculum Highlights

The program combines advanced ECE coursework in AI and data analysis with business fundamentals, product development, and a faculty- or industry-mentored applied project.

Top Employers

Top employers include tech giants like Google, Amazon, and NVIDIA, as well as engineering firms such as Intel, Qualcomm, and defense contractors like Raytheon.

Admissions

Admission to Duke University's Master of Engineering in ECE - Data Analytics & Machine Learning generally expects a bachelor's in ece or related field. The GRE is optional here — a growing norm among AI programs — so applicants can often lead with coursework, projects and recommendations instead. Deadlines, testing policies and funding change year to year, so confirm the current requirements on the official program page before applying.

Application Materials

• Statement of Purpose: Required
• Letters of Recommendation: 3
• Resume: Required
• Transcripts: Official transcripts required

Frequently Asked Questions