Learning Outcomes for M.S. in Applied Data Science with a Specialization in UX Design
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.
- Differentiate between research fields, theoretical concepts, epistemologies, and qualitative and quantitative methods.
- Analyze critically and speak publicly about field-specific scholarly research, projects executed in class, and data management issues.
- 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.
- Apply software development methodologies to create efficient, well-structured applications that other programmers can easily understand.
- Analyze computational complexity in algorithm development.
- Investigate research questions and designs by loading, extracting, transforming, and analyzing data from various sources.
- Test hypotheses and evaluate reliability and validity.
- Implement histograms, classifiers, decision trees, sampling, linear regression, and projectiles in a scripting language.
- Decompose and simulate systems to process data using randomness.
- Employ supervised and unsupervised machine learning for functional approximation and categorization.
- Display, interpret, and explore data using descriptive statistics and graphs.
- Explore assumptions about the data, including normality, skew, and kurtosis.
- Use random variables and probability distributions.
- Determine whether and how to perform statistical inference.
- Perform parametric (e.g., t-test, ANOVA, ANCOVA, MANOVA) and nonparametric (e.g., chi-square) hypothesis testing and correlation.
- Fit linear regression models and interpret their parameters.
- Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
- Explore, transform, and visualize large, complex datasets with graphs in R.
- Solve real-world problems by adapting and applying statistical learning methods to large, complex datasets.
- Identify, assess, and select among statistical learning 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 in R.
- 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.
- 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 crow’s foot notation to enforce referential integrity.
- Evaluate tables for compliance to third normal form and perform normalization procedures on noncompliant tables.
- Write triggers to handle events and enforce business rules and create views within a relational database.
- Demonstrate an understanding of the data lifecycle, including data curation, stewardship, preservation, and security.
- Evaluate the social and ethical implications of data management.
Students will demonstrate competency in client–server application development.
- Design and implement client–server applications that solve real-world problems.
- Implement the model-view-controller software pattern in web and mobile user interfaces.
- Apply client-side and server-side programming skills including design, coding, implementation, and integration with relational databases.
- Transmit objects between the browser and server by converting them into JSON.
- Evaluate a given web application based on different criteria such as structure, dynamics, security, embedded systems, and interactivity.
- Diagram the phases of the secure software development lifecycle.
- Demonstrate the techniques of defensive programming and secure coding.
- Design user-friendly web and mobile interfaces.
Students will demonstrate competency in the management of massive, high-throughput data stores, and cloud computing.
- Research the main concepts, models, technologies, and services of cloud computing, the reasons for the shift to this model, and its advantages and disadvantages.
- Examine the technical capabilities and commercial benefits of hardware virtualization.
- Analyze tradeoffs for data centers in performance, efficiency, cost, scalability, and flexibility.
- Evaluate the core challenges of cloud computing deployments, including public, private, and community clouds, with respect to privacy, security, and interoperability.
- Create cloud computing infrastructure models.
- Demonstrate and compare the use of cloud storage vendor offerings.
- Develop, install, and configure cloud-computing applications under software-as-a-service principles, employing cloud-computing frameworks and libraries.
- Apply the MapReduce programming model to data analytics in informatics-related domains.
- Enhance MapReduce performance by redesigning the system architecture (e.g., provisioning and cluster configurations).
- 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 data visualization.
- Assess the purpose, benefits, and limitations of visualization as a human-centered data analysis methodology.
- Conceptualize and design effective visualizations for a variety of data types and analytical tasks.
- Implement interactive visualizations using modern web-based frameworks.
- Evaluate critically visualizations using perceptual principles and established design guidelines.
- Conduct independent research on a range of theoretical and applied topics in visualization and visual analytics.