Faculty:

• Jared Burns, Ph.D.
• Christopher Diaz, Ph.D.
• Brad Messner, M.B.A.

Learning Objectives: Data Analytics

1. Utilize data-handling techniques from the fields of computer science, business, information technology, and statistics to analyze and maximize the value of data.
2. Identify the opportunities, needs, and constraints of data usage within an organization, including the delivery of insights from data, data clearing, and misrepresentation of data.
3. Professionally communicate in a clear, concise, and precise way to a variety of audiences.
4. Apply quantitative modeling, programming, and data analysis techniques to solve real world problems, communicate findings, and effectively present results using data visualization techniques and software.
5. Identify, analyze, and describe ethical issues related to intellectual property, data security, integrity, and privacy.
6. Apply Setonian ideals of ethics in everyday business and organizational activities and make well-reasoned business and data management decisions.
7. Demonstrate knowledge of statistical data analysis techniques utilized in decision making.
8. Design creative data analytics solutions for analysis and presentation, and apply principles of data science to solve real-world problems.
9. Use new and emerging tools and technologies to analyze big data with cloud-based data mining for exploration, discovering patterns, and answering business questions.
10. Apply algorithms and data mining to construct machine intelligence for e-commerce and other applications.
11. Demonstrate leadership, decision-making, management, and teamwork skills and apply organization theory.

All candidates for the Bachelor of Science degree in data analytics must complete the Liberal Arts Curriculum requirements, the capstone assessment requirement (SDT 300 ), and the required major courses.

A minimum of 120 credits is required.