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 the critical statistical, mathematical, and computational concepts and use them appropriately.
   

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 in the Bachelor of Science in Data Science major are expected to:

5. Critically evaluate modeling, inferential and computational approaches for solving the problem. Specifically, they will be able to:  

(5a.) Justify different approaches based on mathematical and statistical reasoning.

(5b.) Implement the chosen strategy with computationally efficient algorithms and following modern software engineering principles.

(5c.) Test it empirically.

(5d.) Assess its limits.

6. Contribute creatively to the theoretical and/or applied frontiers of at least one of the following disciplines: statistical analysis, optimization, computation, and mathematical modeling.