Data Science, Online
The Online Data Science master’s program at Virginia University offers a flexible, rigorous curriculum tailored for working professionals who aim to gain expertise in data-driven decision-making. Delivered entirely online over 20 months, the part-time structure allows students to balance their career responsibilities while progressing through a comprehensive education in statistics, programming, machine learning, and data ethics. The curriculum is designed to equip learners with both theoretical understanding and practical proficiency in data analysis and visualization.
Students work with real-world datasets and tools widely used in the industry, including Python, SQL, and cloud-based platforms. The program emphasizes both the technical and strategic dimensions of data science, preparing graduates to extract actionable insights that support organizational goals. Faculty members with academic and industry credentials guide learners through project-based coursework, ensuring they are ready to tackle challenges in fields such as finance, healthcare, e-commerce, and public policy. The program culminates in a capstone project that reflects the student’s mastery of key data science concepts and tools.
Key information
Key Facts
- Program Title: Data Science, Online
- Degree Type: S. Master
- Duration: 20 months
- Mode of Study: Part-time, Online
- Application Deadline: 01 June 2026
- Location: University of Virginia, United States
- Field of Study: Data Science & Big Data
- Language of Instruction: English
Program Structure
Semester 1 – Foundations of Data Science
- Introduction to Data Science
- Programming with Python for Data Analysis
- Probability and Statistical Inference
- Data Ethics and Legal Frameworks
Semester 2 – Advanced Techniques and Applications
- Machine Learning Fundamentals
- Data Wrangling and Visualization
- Predictive Modeling and Algorithms
- Capstone Planning and Proposal
Semester 3 – Applied Data Science Practice
- Deep Learning and AI Concepts
- Big Data Systems and Cloud Computing
- Domain-Specific Applications (e.g., Health, Finance, Policy)
- Final Capstone Project and Portfolio Review
Semester 4 – Integration and Leadership
- Data Science Strategy and Communication
- Advanced Topics in Data Governance
- Team-Based Analytics Project
- Professional Development in Data Careers
Career Opportunities
Graduates of this program are equipped to pursue diverse roles such as data analyst, machine learning engineer, data architect, and business intelligence consultant in both the private and public sectors. Organizations in technology, finance, healthcare, and government are increasingly dependent on data-literate professionals who can turn complex data into actionable strategies. The program also supports career shifts for professionals from other industries by providing a solid foundation in data science and hands-on experience with contemporary tools. Whether working in startups or large enterprises, graduates bring value through their ability to bridge the gap between data and decision-making.
Why Choose This Program
Virginia University’s online format combines academic excellence, professional flexibility, and technical depth, making it a top choice for aspiring data scientists who cannot commit to full-time, on-campus study. The curriculum is designed with direct input from industry advisors to ensure that graduates gain relevant, job-ready skills. Students benefit from personalized mentorship, interactive learning environments, and access to a wide alumni network. The program’s part-time design allows professionals to apply what they learn immediately in their current roles, accelerating both learning and career advancement. This balance of flexibility, relevance, and rigor makes the program a compelling option for anyone aiming to thrive in the fast-evolving world of data.
Contact Information
For further information, please contact the admissions office at:
Phone: +1 434 924 0311
Email: gradadmissions@virginia.edu
Address: University of Virginia, Charlottesville, VA 22904, United States
