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Master of Science in Applied Data Science with a Specialization in User Experience Design

Use big data to understand people better

Be where data science meets people. That’s quantitative user experience (UX) research. This specialization lets you get in the heads of those who use technology and learn more about their motivations, intentions, and experiences.

As a quantitative UX researcher, you’ll combine data science skills with those of the social scientist to solve problems and develop valuable insights.

Learn not only what users do, but why

What motivates people to use one application but not another? How do they perceive it, respond to it emotionally, and adapt it to their daily life? What features come together to create the next killer app?

By applying theories from psychology and human factors, you’ll mine patterns in data to discover the user’s intent. Quantitative UX research tells you not only how much time users spend navigating a website—but whether it’s because of need, enjoyment, or mounting frustration.

Researchers supplement the terabytes of activity logs typically mined by data scientists with experiments, surveys, focus groups, and controlled observation to better understand user motivation and behavior. You’ll rely on a strong grounding in math and statistics to perform A/B testing, data analysis, and other assessments.

A leader in data science

The IU School of Informatics and Computing is the first informatics school in the nation, and the first data science program in Indiana to offer both master’s and Ph.D. degrees. Taken together, the Indianapolis and Bloomington campuses have the second largest human–computer interaction program in the United States. Key ties to industry, employment, and research distinguish our school and its programs.

Students enrolled in SOIC at IUPUI learn methods of data mining, ways to transform large data sets into usable knowledge, and how to represent information visually. The Master of Science in Applied Data Science with a specialization in User Experience Design teaches students the latest methods of data management, analysis, and high-throughput data storage.

UX research and other data science careers

Learn to tell a story with data that informs our understanding of people to create better products. Companies working with data at scale are seeking the skills you’ll acquire, to mine big data and manage cloud storage systems. Students who earn their master’s degree can find jobs in many sectors, including:

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Plan of Study

By earning an M.S. in Applied Data Science with a specialization in UX design, you’ll understand client-server application development and the ethical and professional management of informatics projects. Through your classes under the direction of our highly regarded faculty, gain competency in the application of human-computer interaction (HCI) theory and user-centered practices to user experience research and design.

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Learning Outcomes

User Experience Design Specialization

Students will demonstrate competency in the application of HCI theory and a user-centered practices to interaction design.

  • Assess user needs and requirements.
  • Design and develop user design prototypes based on user assessments, while applying HCI principles and models.
  • Apply evaluation and usability testing methods to interactive products to validate design decisions using user testing and heuristic evaluation.
  • Categorize, design, and develop information in proper architectural structures.
  • Analyze test data and write a comprehensive report on the product development process of a redesigned interface, including the stages of pre-design, design, and post-design, testing, and data analysis.
  • Apply the research methods regarding qualitative and quantitative data.
  • Implement an HCI research proposal, including research questions, collecting the relevant literature, and methodology.

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. Apply software development methodologies to create efficient, well-structured applications that other programmers can easily understand.
  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. Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
  18. Explore, transform, and visualize large, complex datasets with graphs in R.
  19. Solve real-world problems by adapting and applying statistical learning methods to large, complex datasets.
  20. Identify, assess, and select among statistical learning methods and models for solving a particular real-world problem, weighing their advantages and disadvantages.
  21. Write programs to perform data analytics on large, complex datasets in R.
  22. Analyze datasets from case studies in informatics-related fields (e.g., digital media, human-computer interaction, health informatics, bioinformatics, and business intelligence).

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

  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.