Learning Outcomes for the B.S. in Data Science

Learning Outcomes for the B.S. in Data Science

Graduates of the Data Science undergraduate program will demonstrate expertise in the following core competencies essential to succeed as a data and information science professional:

Data Literacy and Foundations

1. Compare and explain the differences amongst data, information, knowledge, and wisdom.
2. Interpret how data plays a key role in various societal contexts, including knowledge creation,
   innovation, decision-making, and influencing societal values.
3. Analyze datasets and data technologies to evaluate data veracity, including bias in data collection,
   organization, representation, dissemination, and technological applications.
4. Utilize various data analysis techniques and methods to analyze and share insights across different
   contexts.

Data Analysis and Visualization

1. Apply data science concepts, techniques, and tools to support data analytics.  
2. Organize and analyze datasets using descriptive statistics and visualizations to interpret data and communicate findings.  
3. Design effective visualizations for various data types, analytical tasks, and contexts.  
4. Assess the purpose, benefits, and limitations of visualization as a data analysis methodology.
5. Identify and select appropriate data analysis methods and models to solve real-world problems, weighing their advantages and disadvantages.

Computational and Statistical Techniques

1. Apply inferential statistics, predictive analytics, and data mining to various informatics and data contexts.
2. Utilize supervised machine learning methods, including regression, classification, and support vector machines, to analyze datasets.  
3. Implement unsupervised machine learning techniques, such as clustering and principal components analysis, for data exploration and pattern discovery.  
4. Write programs to perform data analytics using languages such as R, Python, and SQL.
5. Evaluate statistical learning and modeling methods to solve real-world problems, considering their advantages and disadvantages.

Data Management and Information Science

1. Demonstrate the application of the data lifecycle, including data curation, stewardship, and longterm preservation.
2. Contextualize the characteristics of various genres of data utilized in a variety of disciplines, research communities, and government organizations.
3. Apply principles of consistency and uniformity to recognize the need for authorized terms for describing various types of data.  
4. Implement principles of data organization, including metadata, encoding standards, access control, version control, and data documentation.
5. Analyze critical issues associated with the storage, backup, and security of data.
6. Analyze and compare data policies and their potential outcomes. 

Ethics and Social Impact

1. Identify and analyze the relationship between data, community, and society.
2. Discuss the social, political, and ethical aspects of data creation, access, ownership, and communication.  
3. Develop arguments using ethical reasoning to suggest improvements to data-driven systems and practices.  
4. Assess how data-driven decisions influence human rights and social justice, particularly in terms of privacy, autonomy, and equality.  
5. Advocate for the ethical responsibilities of data scientists in mitigating biases and promoting fairness and social justice through their work.   
6. Ensure community engagement methods are used when working with community data throughout the data lifecycle.

Applied Experience and Communication

1. Design, conduct, and communicate research results.  
2. Apply tools and techniques of research and project management.  
3. Apply data science skills to interdisciplinary projects, demonstrating the ability to integrate domain knowledge with data science methodologies.  
4. Analyze the economic, industrial, legal, and business contexts of technology and data, preparing to navigate and address challenges as data professionals.
5. Develop and present data-driven and asset-based approaches to real-world problems, demonstrating effective communication skills to diverse stakeholders.