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Master of Science in
Information Science

Courses

As a student in this program, you will have the opportunity to master emerging theories and technologies in enterprise information systems. You'll also research and study solutions to transform enterprise systems to meet the needs of ever-changing business operations.

To graduate, you must successfully complete a total of 33 course credits at the 400-, 500-, or 800-level, including:

  • at least 18 credits of required core courses
  • at least 12 credits of approved electives
  • three credits of INSC 594, an integrative research topics course, which includes a master's scholarly paper

Required Courses (18 credits)

  • 3
    credits

    Design and development of business processes that align business objectives with Information Technology (IT) systems.

  • 3
    credits

    This course will explore various emerging theories and technologies in information science, including at least cloud computing, mobile application, Internet of things, blockchain, machine learning, and artificial intelligence.

  • 3
    credits

    A web-centric look at the latest techniques and practices in computer security as they apply to the internet.

    • Prerequisite

      CSE 453 or IST 815

  • 3
    credits

    This course will investigate the evolution of information systems architecture.

  • 3
    credits

    Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

    • Prerequisite

      INSC 521, or approval of instructor or department

  • 3
    credits

    Advances in design, development, and deployment of control and management software for enterprise and production information systems.

Electives (select 12 credits)

  • 3
    credits

    Examination of large-scale data storage technologies including NoSQL database systems for loosely-structured data, and warehouses for dimensional data.

    • Prerequisite

      INSC 521

  • 3
    credits

    This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

    • Prerequisite

      STAT 500

  • 3
    credits

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    Application and interpretation of analytics for real-life decision making.

    • Prerequisite

      STAT 500

  • 3
    credits

    The course examines business intelligence in the era of big data. Emphasis is on the successful implementation of big data in large and small corporations that deliver extraordinary results.

  • 3
    credits

    Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

    • Prerequisite

      STAT 500 or equivalent

  • 3
    credits

    The requirements capture, design, and development of relational database applications; analysis of business requirements and development of appropriate database systems.

  • 3
    credits

    This course will employ several methods and measures from the area of social network analysis to study the interrelatedness of cyber-social and cyber-technical aspects of an organization or the society as a whole to detect and capture the dynamic patterns of group membership and structure. This type of analysis can be useful in uncovering potential threats and attacks and predicting criminal behavior and evolution of criminal networks.

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and 2-way ANOVA, Chi-square tests, diagnostics.

Culminating Experience (3 credits)

  • 3
    credits

    Supervised student activities on research projects identified on an individual or small-group basis, leading to M.S. scholarly papers.

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