Learning Outcomes

Students in the Data Science Program are expected to achieve the following learning outcomes. These learning outcomes are used both in evaluating students and the undergraduate program. By the time they graduate, majors are expected to:

1. Frame questions of interest from various disciplines in quantitative terms and identify what data types might help address them.

2. Demonstrate familiarity with critical statistical, mathematical, and computational concepts and use them appropriately to solve quantitative problems.

3. Appraise the significant ethical questions that arise in collecting, analyzing, and using data for decision-making based on quantitative reasoning.

4. Convey quantitative analysis and technical results to a broad audience, effectively communicating the uncertainty associated with their conclusions and ensuring the reproducibility of results.

Additionally, students pursuing a Bachelor of Arts in Data Science have learning outcomes that are specific to their subplan.

Students in the Data Science BA with the Social Systems subplan will be expected to:

5. Integrate traditional theoretical approaches from the social sciences and engineering with modern computational tools to frame, understand, and analyze social-scientific problems. Specifically, students will be able to:

   A. Develop analytical approaches to address unstructured and multifaceted problems.

   B. Design and employ experimental and quasi-experimental approaches to understand causal effects in the social sciences.

   C. Implement and apply statistical analyses and machine learning algorithms using modern software engineering principles.

   D. Draw correct inferences from data with an appreciation for the assumptions and limits of quantitative methods.

6. Understand frontier research and practice at the intersection of data and social science concerning critical social, political, and economic issues.

Students in the Data Science BA with the Artistic and Cultural Analysis subplan will be expected to:

5. Bring the methods and principles of data science to bear on the study of cultural and aesthetic objects and systems in ways that retain the rigor of scientific investigation while remaining sensitive to the unique attributes of these domains.

6. Apply methods from humanistic inquiry and/or artistic practice to the study of data science, including, but not limited to assessing cultural biases, navigating fundamental uncertainties and unknowns within culture and the arts, and remaining attentive to the role that cultural artefacts play in society. 

7. Develop models for understanding arts and culture that do not reduce the complexity of the objects under investigation, whether that complexity lies in a fragmented historical record, across multilingual corpora, or in the interface between creator and community. 

8. Conduct research and/or undertake artistic practice at the frontiers of humanistic inquiry and data science.