Brandeis Online
Data Engineering and Cloud Analytics Online Master's Certificate
Program Snapshot
- 100% online
- Part-time
- 4 courses (12 credits)
- Complete in 1 year or less
- Pathway to online MS in Applied Data Science and Decision Analytics and MS in Applied Leadership
- GRE not required
- Offered through Rabb School of Continuing Studies
Engineering data systems for modern analytics
Data systems now operate at unprecedented scale and speed, while facing new expectations around efficiency, sustainability, and governance. Organizations need professionals who can design cloud-native pipelines that support analytics and AI without sacrificing reliability, transparency, or resource responsibility. This certificate will prepare learners to architect and manage production data systems that enable forecasting, visualization, and evidence-based decision-making in modern cloud environments.
The curriculum integrates data engineering, forecasting, and analytics visualization within modern cloud platforms. In the Data Engineering and Cloud Analytics certificate, students gain hands-on experience designing cloud-native pipelines, developing predictive and operational models, and translating complex data into clear, actionable insights. Emphasis is placed on reproducibility, ethical practice, and energy-efficient computing, ensuring graduates can support scalable analytics while managing cost, performance, and environmental impact. Graduates are prepared for roles across data engineering, analytics engineering, business intelligence, and data systems architecture in a wide range of industries.
The Master's Certificate in Data Engineering and Cloud Analytics will allow you to:
- Design and Implement Data Systems: Architect cloud-native pipelines that enable sustainable analytics and AI deployment
- Develop Forecasting and Operational Models: Use predictive techniques to improve resource planning and decision-making
- Communicate Analytics Visually: Translate complex data into clear, actionable visual insights
- Ensure Reproducibility and Ethical Practice: Integrate ethical, transparent data methods throughout the analytic lifecycle
News and Stories
June 30, 2025
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