Learning Outcomes for M.S. in Applied Data Science
Learning Outcomes for M.S. in Applied Data Science
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.
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 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.
Students will demonstrate competency in data management, infrastructure, and the data science lifecycle.
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 create views to enforce business rules within a relational database.
Formulate queries in relational algebra using selection, projection, restriction, Cartesian product, join, and set operators.
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 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.
Applied Data Science Specialization
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 neural networks for deep learning.
Solve problems in linear algebra, probability, optimization, and machine learning.
Evaluate, in the context of a case study, the advantages and disadvantages of deep learning neural network architectures and other approaches.
Implement deep learning models in Python using the PyTorch library and train them with real-world datasets.
Design convolution networks for handwriting and object classification from images or video.
Design recurrent neural networks with attention mechanisms for natural language classification, generation, and translation.
Evaluate the performance of different deep learning models (e.g., with respect to the bias-variance trade-off, overfitting and underfitting, estimation of test error).
Perform regularization, training optimization, and hyperparameter selection on deep models.
Analyze a deep learning model’s hardware node and GPU scalability in preparation for deployment.
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.