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Develop the technical expertise to interpret and handle data from a business perspective with Suffolk's Master of Science in Business Analytics (MSBA) program.
Data literacy is crucial for sound business decision-making. Our MSBA curriculum equips you with practical training, analytical resources, and hands-on experience to excel in this rapidly growing field. Whether you're a tech specialist, software developer, or business professional seeking to enhance your analytical capabilities, our balanced focus on business intelligence and data strategy will propel your professional growth.
In our rigorous MSBA program, you'll examine analytics within real-world business scenarios and master techniques for handling and forecasting with complex data systems. Upon graduation, you'll be prepared to make strategic, evidence-based decisions across various sectors and effectively translate analytical insights for non-technical executives and stakeholders.
Pursuing your MSBA at Suffolk means you will:
Utilize analytical methodologies in marketing, operations, finance, accounting, and healthcare sectors, Learn from distinguished faculty combining academic excellence with practical industry experience, Master data storage and governance principles, understanding why proper data processing is fundamental for business intelligence, Develop leadership qualities with the communication and interpersonal skills necessary to connect technical teams with business stakeholders, Access a powerful professional community of alumni and regional business leaders.
Our industry-informed curriculum keeps the program at the forefront of innovation. Through hands-on learning, you'll acquire skills in data collection, organization, and preparation – the building blocks for analytics-based problem-solving and opportunity identification across diverse industries. You'll also gain proficiency in Python, Excel, and SAP systems, positioning yourself for valuable professional certifications.
Grasp essential concepts including financial evaluation, predictive modeling, investment analysis, and how to implement these approaches in financial data examination.