{"id":1027,"date":"2025-11-18T21:03:16","date_gmt":"2025-11-18T21:03:16","guid":{"rendered":"https:\/\/depts.eng.ufl.edu\/msem\/?page_id=1027"},"modified":"2026-02-25T15:46:09","modified_gmt":"2026-02-25T15:46:09","slug":"data-analytics","status":"publish","type":"page","link":"https:\/\/ise.ufl.edu\/msem\/academics\/data-analytics\/","title":{"rendered":"Data Analytics"},"content":{"rendered":"\n
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MSEM Data Analytics Specialization<\/h1>

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Technical Managers are Critical in Organizations<\/h3>\n\n\n\n

Technical data analytics managers must answer crucial questions such as:<\/p>\n\n\n\n

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Does the organization’s architecture for a critical data analytics model support the addition of a real-time data feed?<\/p>\n<\/blockquote>\n\n\n\n

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What is the effort and work hours needed to design and implement a new data analytics model for a business partner?<\/p>\n<\/blockquote>\n\n\n\n

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The company’s scientists have proposed a solution, but the system has significant runtime limitations; can this be worked around?<\/p>\n<\/blockquote>\n\n\n\n

Managing in technical industries has grown increasingly complex over time. Business concerns often have many layers and competing objectives. In reality, even the best organizations often solve critical business problems with intuition that may not necessarily be fact-based. The Masters in Engineering Management with an emphasis in Data Analytic provides the technical knowledge and training to solve critical business questions with a scientific foundation, and guide your organization with thoughtful, calculated insights. <\/p>\n\n\n\n

MSEM Data Analytics program outcomes<\/h3>\n\n\n\n
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Data Models: <\/em><\/strong>Provide guidance on modeling paradigms in advanced data analytics to support solution design, communicate technical concepts to senior leadership, and evaluate and manage internal and external teams for successful execution.<\/p>\n<\/div>\n\n\n\n

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\"People's<\/figure>\n\n\n\n

System implementation: <\/em><\/strong>Guide core strategic infrastructure and software decision-making in the data analytics spaces to support the organization in a rapidly evolving technology landscape. Understanding of data architecture and governance principles.<\/p>\n<\/div>\n\n\n\n

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Leadership: <\/em><\/strong>Manage projects and personnel in engineering and high tech spaces, including alignment with strategic initiatives, risk assessment, deadlines and deliverables, driving and determining KPIs, and ensuring ROI. Ethical and legal considerations in data analytics.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/section>\n\n\n\n

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MSEM Data Analytics 3 Course Specialization<\/h3>\n\n\n\n
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This three course series covers primary topics in data science and data engineering taught from the lens of a middle manager in Technology or Engineering. Students will be exposed to a broad range of topics in all areas of the data analytics ecosystem, with the end goal of advising and managing data analytics programs and projects. Learning items can be categorized as:<\/p>\n\n\n\n

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  1. Students will gain an understanding of data architecture<\/em><\/strong> concepts (cloud-based and on-premises) in order to advise executives on strategic system decisions and guide implementation teams on the architectural impacts to analytics initiatives. <\/li>\n\n\n\n
  2. Students will learn key data engineering<\/em><\/strong> concepts and tools, including ETL pipeline management, enabling them to assess technical feasibility, advise on critical data flow design, and bridge communication between leadership and technical teams.<\/li>\n\n\n\n
  3. Students will develop foundational knowledge in data science, machine learning, and AI <\/em><\/strong>methodologies, equipping them to evaluate their applicability to business challenges. Students should be able to advise technical teams and executives on the tractability of various modeling approaches and advise on the supporting infrastructure and talent required.<\/li>\n\n\n\n
  4. Students will build skills in visualizing and communicating<\/em><\/strong> complex analytics systems and model outputs. Presentations should be clear and relevant to all strategic stakeholders, both technical and executive.<\/li>\n<\/ol>\n\n\n\n
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