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Emphasizes developing graduates' numerical and analytical skills. Students will acquire statistical proficiency for examining and presenting both basic and intricate data collections from diverse origins, including disease indicators, genomic/transcriptomic information, and corporate datasets. This prepares graduates to excel in the competitive data science field across computing, business, and natural science applications.
Learning Outcomes
Utilize comprehensive data science understanding across various theoretical and applied big data scenarios.
Employ critical thinking to assess, interpret, and frame complex data science challenges.
Apply innovative thinking to foresee and address big data obstacles.
Leverage digital tools, information literacy, and numerical skills to gather, assess, and integrate pertinent information from multiple sources.
Demonstrate effective communication within statistical discussions spanning diverse data science contexts.
Conduct and share data science evaluations with cultural sensitivity and ethical consideration, including indigenous cultural awareness.
Collaborate effectively while showing leadership in applying social, sustainable, and ethical principles.
Display independent learning capabilities, responsibility, and discernment in managing one's educational and professional development.