Duke University

How this program compares

Benchmark this program against our national recognition pages and use the key guides below to evaluate ROI, admissions difficulty, and outcomes.

Admission Snapshot

Typical admitted student: Applicants must hold a bachelor's degree from an accredited institution with a strong quantitative background; GRE scores are optional to strengthen the application.[1

About This Program

This two-year applied program prepares students to lead data-driven teams by combining rigorous technical training in statistics and computation with professional skills in communication, teamwork, and ethical leadership.

Career Outcomes

Lead as Data Scientist in Cross-Industry AI with machine learning expertise

• 1. Data Scientist
• 2. Machine Learning Engineer
• 3. Data Analyst
• 4. Business Intelligence Analyst

What You'll Learn

• Apply machine learning algorithms to real-world datasets.
• Design and analyze large-scale data systems.
• Integrate interdisciplinary methods for data-driven decision making.
• Communicate insights through visualization and ethical considerations.

Curriculum Highlights

The curriculum features a technical bootcamp, seven core courses covering data ethics and visualization, a mandatory summer internship, and a year-long capstone project with research faculty or partners.

Top Employers

Top employers include tech giants like Google, Amazon, Microsoft, and consulting firms such as McKinsey and Deloitte.

Admissions

Applicants must hold a bachelor's degree from an accredited institution with a strong quantitative background; GRE scores are optional to strengthen the application.[1

Application Materials

• Statement of Purpose: Required
• Letters of Recommendation: 3
• Resume: Required
• Transcripts: Official transcripts required[1]

Academic Requirements

• Degree Required: Bachelor's degree
• GRE/GMAT: Optional
• TOEFL/IELTS: Required for international students (TOEFL 90+ / IELTS 7+)[1]