Learning Outcomes for the B.S. in Applied Data and Information Science
Graduates of the Applied Data and Information Science undergraduate program will demonstrate expertise in the following core competencies essential to succeed as a data and information science professional:
- Distinguish between data, information, and knowledge.
- Recognize that data can have value and play a key role in society by providing opportunities to expand knowledge, to innovate, and to influence.
- Analyze datasets in context to determine data veracity including bias in data collection or representation.
- Assess values with respect to the use of data technologies.
- 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.
- 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.
- Identify, assess, and select appropriately among data analytics methods and models for solving real-world problems, weighing their advantages and disadvantages.
- Understand data science concepts, techniques, and tools to support big data analytics.
- Analyze datasets with the following supervised learning methods: for functional approximation, multiple linear regression, splines, and local regression; for classification, logistic regression, linear discriminant analysis, decision trees, bagging, random forests, and boosting, and support vector machines.
- Analyze datasets with the following unsupervised learning methods: for dimensionality reduction, principal components analysis; for grouping, k-means clustering and hierarchical 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 and select appropriately among statistical learning methods for a particular real-world problem; analyze each method with respect to a given dataset or research question in terms of modeling accuracy and the bias-variance tradeoff; perform model assessment and selection; identify overfitting and underfitting; perform model selection and regularization; explain the relative advantages and disadvantages of each statistical learning method for the real-world problem.
- Write programs to perform data analytics on large, complex datasets in R.
- Analyze data from case studies in informatics related fields.
- Demonstrate an understanding of the data lifecycle, including data curation, stewardship, and long-term preservation.
- Apply the principles of consistency and uniformity to recognize the need for authorized terms for describing various types of data.
- Understand the principles of data organization including file name conventions, version control, and data documentation.
- Understand the characteristics of various data types generated and used by a variety of disciplines, subdisciplines, research communities, and government organizations.
- Understand critical issues associated with the storage, backup, and security of data.
- Analyze data policies to compare possible outcomes.
- Understand the relation between data, ethics, and society.
- Identify and understand the social, political, ethical, and legal aspects of data creation, access, ownership, service, and communication.
- Develop substantive arguments using ethical reasoning to suggest improvements to data-driven systems and practices.
- Differentiate between surveillance systems that promote and inhibit values.
- Design, conduct, and write up results of research.
- Understand tools and techniques of project management.
- Understand legal and business aspects of technology and media.