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Emphasizes developing graduates' numerical and analytical skills. Students will acquire statistical proficiency for examining and presenting both simple and complex data from diverse sources including disease indicators, genomic information, transcriptomic datasets, and business analytics. 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 practical big data scenarios.
Employ critical thinking to assess, interpret, and frame complex data science challenges.
Apply innovative thinking to predict obstacles and develop solutions for big data issues.
Leverage digital tools, information literacy, and numerical skills to gather, assess, and integrate pertinent information from multiple sources.
Demonstrate effective communication within statistical discussions across diverse data science contexts.
Conduct and share data science findings with cultural sensitivity and ethical consideration, including indigenous cultural awareness.
Collaborate effectively while showing leadership to apply sustainable and ethical principles.
Display independent learning, responsibility, and sound judgment in managing one's professional development.