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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:

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Plan of Study

Sports Analytics students complete 10 courses for a total of 30 credit hours.

Six courses are required:

  • INFO I501
  • LIS S511
  • HPER T591 or PSY 60000
  • INFO H515
  • INFO H516
  • INFO H517

Four additional courses are selected from the following list:

  • HPER K514
  • INFO H518
  • INFO H559
  • NEWM N510
  • TECM 500
  • TECM 519
  • TECM 531
  • TECM 562
  • TECM M582

Sample plans of study are provided below for reference.

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Learning Outcomes

Master of Science in Applied Data Science Core

Students will demonstrate competency in data analytics.

  1. Differentiate between research fields, theoretical concepts, epistemologies, and qualitative and quantitative methods.
  2. Analyze critically and speak publicly about field-specific scholarly research, projects executed in class, and data management issues.
  3. Design, implement, test, and debug extensible and modular programs involving control structures, variables, expressions, assignments, I/O, functions, parameter passing, data structures, regular expressions, and file handling.
  4. Create efficient, well-structured applications that other programmers can easily understand by applying software development methodologies.
  5. Analyze computational complexity in algorithm development.
  6. Investigate research questions and designs by loading, extracting, transforming, and analyzing data from various sources.
  7. Test hypotheses and evaluate reliability and validity.
  8. Implement histograms, classifiers, decision trees, sampling, linear regression, and projectiles in a scripting language.
  9. Decompose and simulate systems to process data using randomness.
  10. Employ supervised and unsupervised machine learning for functional approximation and categorization.
  11. Display, interpret, and explore data using descriptive statistics and graphs.
  12. Explore assumptions about the data, including normality, skew, and kurtosis.
  13. Use random variables and probability distributions.
  14. Determine whether and how to perform statistical inference.
  15. Perform parametric (e.g., t-test, ANOVA, ANCOVA, MANOVA) and nonparametric (e.g., chi-square) hypothesis testing and correlation.
  16. Fit linear regression models and interpret their parameters.
  17. Design and execute ethical research using quantitative and experimental methods.
  18. Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
  19. Apply inferential statistics, predictive analytics, and data mining to informatics-related fields.
  20. Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
  21. Identify, assess, and select appropriately among statistical learning methods and models for solving a particular real-world problem, weighing their advantages and disadvantages.
  22. Write programs to perform data analytics on large, complex datasets.

Students will demonstrate competency in data management, infrastructure, and the data science lifecycle.

  1. Design and implement relational databases using tables, keys, relationships, and SQL commands to meet user and operational needs.
  2. Diagram a relational database design with entity–relationship diagrams (ERDs) using crow’s foot notation to enforce referential integrity.
  3. Evaluate tables for compliance to third normal form and perform normalization procedures on noncompliant tables.
  4. Write triggers to handle events and enforce business rules and create views within a relational database.
  5. Demonstrate an understanding of the data lifecycle, including data curation, stewardship, preservation, and security.
  6. Evaluate the social and ethical implications of data management.

Students will demonstrate competency in client–server application development.

  1. Design and implement client–server applications that solve real-world problems.
  2. Create well-formed static and dynamic webpages using current versions of PHP, HTML, CSS, and JavaScript or their equivalents.
  3. Implement the model-view-controller software pattern in web and mobile user interfaces.
  4. Apply client-side and server-side programming skills including design, coding, implementation, and integration with relational databases.
  5. Extract data from JavaScript Object Notation (JSON) and Extensible Markup Language (XML) documents.
  6. Transmit objects between the browser and server by converting them into JSON.
  7. Evaluate a given web application based on different criteria such as structure, dynamics, security, embedded systems, and interactivity.
  8. Diagram the phases of the secure software development lifecycle.
  9. Demonstrate the techniques of defensive programming and secure coding.
  10. Design user-friendly web and mobile interfaces.

Students will demonstrate competency in the management of massive, high-throughput data stores, and cloud computing.

  1. Research the main concepts, models, technologies, and services of cloud computing, the reasons for the shift to this model, and its advantages and disadvantages.
  2. Examine the technical capabilities and commercial benefits of hardware virtualization.
  3. Analyze tradeoffs for data centers in performance, efficiency, cost, scalability, and flexibility.
  4. Evaluate the core challenges of cloud computing deployments, including public, private, and community clouds, with respect to privacy, security, and interoperability.
  5. Create cloud computing infrastructure models.
  6. Demonstrate and compare the use of cloud storage vendor offerings.
  7. Develop, install, and configure cloud-computing applications under software-as-a-service principles, employing cloud-computing frameworks and libraries.
  8. Apply the MapReduce programming model to data analytics in informatics-related domains.
  9. Enhance MapReduce performance by redesigning the system architecture (e.g., provisioning and cluster configurations).
  10. Overcome difficulties in managing very large datasets, both structured and unstructured, using nonrelational data storage and retrieval (NoSQL), parallel algorithms, and cloud computing.
  11. Apply the MapReduce programming model to data-driven discovery and scalable data processing for scientific applications.

Students will demonstrate competency in data visualization.

  1. Assess the purpose, benefits, and limitations of visualization as a human-centered data analysis methodology.
  2. Conceptualize and design effective visualizations for a variety of data types and analytical tasks.
  3. Implement interactive visualizations using modern web-based frameworks.
  4. Evaluate critically visualizations using perceptual principles and established design guidelines.
  5. Conduct independent research on a range of theoretical and applied topics in visualization and visual analytics.