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Master of Science in Data Analytics

Lead Faculty: Dr. Jodi Reeves

The Master of Science in Data Analytics program is designed to provide students with a comprehensive foundation for applying statistical methods to solve real-world problems. One goal of this program is to prepare students for careers in data analytics with a broad knowledge of the application of statistical tools, techniques, and methods as well as the ability to conduct in-depth analysis, synthesis, and evaluation. Another goal is to prepare students for careers with analytical database knowledge, the ability to apply analytical database tools, techniques, and methods, and the ability to design, develop, implement, program, and maintain data marts and data warehouses.

To address the spectrum of issues in data analytics, this curriculum has been designed to include core courses in statistical topics as well as areas for advanced applications of data analytics in unique fields. Core topics include analytical and predictive modeling, data acquisition, data mining, data security and privacy, continuous and categorical data methods and applications, teamwork, and communication. Advanced topics include how to develop, implement, and maintain the hardware and software tools needed to make efficient and effective use of big data including databases, data marts, data warehouses, machine learning, and analytic programming. State-of-the-art analytical software will be used in all courses.

The culmination of this program is a three-month capstone project where real data from sponsoring organizations or publicly available data will be used to solve specialized problems in analytical database design, programming, implementation, or optimization.

Previous academic studies or industrial experience in such areas as statistics, computer programming, engineering or science are helpful prerequisites for this masters program. This degree is appropriate for both experienced professionals as well as recent college graduates.

Program Learning Outcomes

  1. Integrate components of data analytics to produce knowledge-based solutions for real-world challenges using public and private data sources.
  2. Evaluate data management methods and technologies used to improve integrated use of data.
  3. Construct data files using advanced statistical and data programming techniques to solve practical problems in data analytics.
  4. Design an analytic strategy to frame a potential issue and solution relevant to the community and stakeholders.
  5. Develop team skills to ethically research, develop, and evaluate analytic solutions to improve organizational performance.
  6. Design data marts.
  7. Analyze complex database queries for real-world analytical applications.
  8. Design medium to large data warehouses.
  9. Evaluate machine learning methods and strategies for advanced data mining.

Requirements

To obtain the Master of Science in Data Analytics, students must complete 54 graduate units. A total of 13.5 quarter units of graduate credit may be granted for equivalent graduate work completed at another regionally accredited institution, as it applies to this degree, and provided the units were not used in earning another advanced degree. Please refer to the graduate admissions requirements for specific information regarding application and evaluation.

Core Requirements (12 courses; 54 quarter units)
Core Requisite(s):

Program Information