BSc in Business Analytics - Marketing in Newark United States | University of Delaware

As information technology rapidly evolves, the amount of data produced has grown exponentially in recent times. This data originates from diverse channels including digital transactions, mobile apps, sensor networks, video surveillance, and social platforms, often reaching massive scales measured in terabytes or petabytes. Organizations now have unprecedented opportunities to extract valuable business intelligence through systematic data collection, processing, and analysis.

Program Learning Objectives 1. Utilize analytical techniques to enhance decision-making and data storytelling. 2. Design and implement database systems aligned with business needs while mastering query languages. 3. Comprehend the complete machine learning pipeline and fundamental supervised/unsupervised algorithms. 4. Participate in data acquisition, preprocessing, and governance processes. 5. Articulate practical applications of machine learning for solving business challenges. 6. Acquire adaptable skills for mastering emerging analytical tools. 7. Examine ethical frameworks and organizational behavior principles to inform future choices. 8. Select suitable probability models for representing real-world scenarios. 9. Employ Bayesian computational methods for evaluating potential data-generating models. 10. Present analytical findings through clear language and impactful visualizations. 11. Implement machine learning approaches for data examination. 12. Identify business scenarios suitable for management science methodologies. 13. Cultivate competencies in information collection, model development, and results interpretation. 14. Differentiate between analytical approaches (optimization, simulation, etc.) based on data characteristics and business requirements. 15. Evaluate challenges and propose actionable solutions using case studies and analytical software. 16. Investigate how analytics informs strategic business choices. 17. Develop expertise in processing unstructured data with machine learning. 18. Create and deploy causal analysis methods for key business decisions. 19. Address practical business challenges using analytical modeling techniques.