Data Science Learning Outcomes

Students who successfully complete the requirements for the Data Science Major progress from core concepts in introductory data sciences and statistics courses to advanced topics such as statistical learning and artificial intelligence.

This progression addresses the eight learning outcomes below which are embedded and assessed in the courses offered in this program. Furthermore, students are expected to meet the following benchmarks during their studies as Data Science Majors:

• acquire competence in foundational data science courses;
• explore at least one advanced topic in data science of their own choosing;
• successfully complete a capstone project in DS 410 Seminar in Data Science;
• maintain a 2.0 GPA or higher in their courses.

Learning Outcomes

Data Science graduates:

1. perform statistical analysis of data;
2. cultivate skills in using computational tools for data analysis;
3. apply statistical and computational tools to real-world problems;
4. learn about the importance of proper data management;
5. acquire skills in documenting their work that allows for reproducibility of results;
6. learn how to assess the ethical considerations of a data science project;
7. apply a variety of technological tools, such as statistical software and computer programming languages, for the purpose of simulation and statistical data analysis;
8. effectively communicate data science concepts and reasoning in written reports and oral presentations using both technical and non-technical language.