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Sports Analytics Master of Science

The School of Physical Education and Tourism Management and the School of Informatics and Computing have teamed up to offer an M.S. and B.S./M.S. in Sports Analytics. The Master of Science in Informatics with a specialization in Sports Analytics provides core competencies in data analysis, data management and infrastructure, client–server application development, and ethical and professional management of informatics projects as well as additional competencies in sports sales and marketing, the management of massive, high-throughput data stores, cloud computing, and the data lifecycle.

Careers in Data Analytics

Sports organizations are increasingly seeking employees able to turn data about their customers and teams into revenue generating strategies. This degree offers the necessary combination of skills in sports sales, marketing, and analytics. The degree leads to positions such as Data Scientist, Informatics Scientist, Data Analyst, Big Data Consultant, Business Intelligence Analyst, Business Technology Analyst, IT Consultant, Software Developer, Database Administrator, System Administrator, Web Administrator, Information Architect, and Information Manager.

Plan of Study

Electives include 500 or 600-level INFO courses or other advisor-approved graduate courses.

Learning Outcomes

Master of Science in Informatics Core

Students will demonstrate competency in data analytics.

  • Design and execute ethical research using quantitative and experimental methods.
  • Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
  • Apply inferential statistics, predictive analytics, and data mining to informatics-related fields.
  • Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
  • Identify, assess, and select appropriately among data analytics methods and models for solving a particular real-world problem, weighing their advantages and disadvantages.
  • Write programs to perform data analytics on large, complex datasets.

Students will demonstrate competency in data management and infrastructure.

  • Design and implement relational databases using commercial database management systems according to database concepts and theory.
  • Diagram a relational database design based on an identified scenario.
  • Produce database queries using SQL.
  • Perform database administration tasks.
  • Describe the data management activities associated with the data lifecycle.
  • Overcome difficulties in managing very large datasets, both structured and unstructured, using nonrelational data storage and retrieval (NoSQL), parallel algorithms, and cloud computing.
  • Apply the MapReduce programming model to data-driven discovery and scalable data processing for scientific applications.

Students will demonstrate competency in client–server application development.

  • Design and implement client–server applications that solve real-world problems.
  • Design, implement, test, and debug programs in object-oriented and scripting languages involving control constructs, variables, expressions, assignments, I/O, functions, parameter passing, data structures, and modularization.
  • Apply software development methodologies to create efficient, well-structured applications that other programmers can easily understand.
  • Design user-friendly web and mobile interfaces.
  • Implement the model-view-controller software pattern in web and mobile user interfaces.
  • Create well-formed static and dynamic webpages using current versions of HTML, CSS, and JavaScript or their equivalents.
  • Diagram the phases of the Secure Software Development Lifecycle.
  • Demonstrate the techniques of defensive programming and secure coding.

Students will demonstrate competency in the ethical and professional management of informatics projects.

  • Apply project management methods to overcome the complexities of informatics projects.
  • Plan informatics projects, setting their scope and assigning team members appropriately to roles.
  • Apply to informatics projects time management concepts, such as network diagrams, CPM, and PERT.
  • Apply cost management and budgeting principles.
  • Manage unanticipated changes in informatics projects.
  • Perform risk analysis by means of quantitative and qualitative methods.
  • Employ both “hard” and “soft” skills in leading a project team.
  • Use project management software effectively.
  • Apply communication, negotiation, and group decision-making abilities in team projects.
  • Demonstrate ethical and professional behavior in response to ethically challenging situations.