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Master of Science in Applied Data Science with a specialization in Sports Analytics

Numbers are the name of the game

Statistics have always been part of sports. But the digital age has altered the playing field, as organizations seek out those with the skills to use statistics as a tool for success.

Combine sports marketing skills with the analysis and management of data when you earn a master’s in Applied Data Science with a specialization in Sports Analytics at IUPUI.

Succeed with a winning combination

Analytics is a crucial part of decision-making in amateur and professional athletics. Teams rely on those with the knowledge to interpret data and relate it to the world of athletics.

Indianapolis boasts 10 professional sports teams. The city is home to the National Collegiate Athletic Association (NCAA), the National Federation of State High School Associations, and is widely considered the Capitol of Amateur Sports. By teaming up, IU’s Schools of Physical Education and Tourism Management and Informatics and Computing draw on a unique mix of resources to offer an M.S. and B.S./M.S. in this exciting field.

A skill set tailored to sports

Sports organizations need analytics experts who can turn data about their customers and teams into revenue-generating strategies.

Students who earn a Master of Science in Applied Data Science with a specialization in Sports Analytics learn core competencies in data analysis, data management and infrastructure, and client–server application development, and ethical and professional management of informatics projects. Earn additional competencies in sports sales, the management of massive, high-throughput data stores, cloud computing, and the data life cycle.

Careers in Data Analytics

By earning this degree, you’ll have a combination of skills in sports sales, marketing, sabermetrics, and analytics that can lead to positions such as:

Plan of Study

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Learning Outcomes

Master of Science in Applied Data Science 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 tables, keys, relationships, and SQL commands to meet user and operational needs.
  • Diagram a relational database design with entity–relationship diagrams (ERDs) using crows’ foot notation to enforce referential integrity.
  • Evaluate tables for compliance to third normal form and perform normalization procedures on tables not in third normal form.
  • Write triggers to handle events and create views to enforce business rules within a relational database.
  • Perform database administration tasks.
  • Describe the data management activities associated with the data lifecycle.
  • Evaluate the social and ethical implications of data management practices, including their potential benefits and harms.
  • 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.