Apr 28, 2024  
2023-2024 Undergraduate Catalog 
    
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2023-2024 Undergraduate Catalog [ARCHIVED CATALOG]

Data Science in Engineering

Learn more about the department

For more information and to register visit the Office of Micro-Credentials

Admission Criteria

Current students wishing to pursue the Data Science in Engineering micro-credential, submit the School of Engineering and Applied Sciences Major/Minor Change Application.

Course Requirements

Data Science in Engineering Core (6 credits)

Data Science Internship

  • complete a 10 week, full time, or equivalent part time, (minimum 350 total hours) data science internship

Students may opt to, but are not required to, formally enroll in a for-credit internship (e.g., 496 course). If the student enrolls in an internship, they must receive a grade of ‘B’ or better for the 496 course. If they do not enroll in a for-credit internship, the Data Science in Engineering microcredential program director will use supervisor evaluations to evaluate a student’s proficiency, with the expectation that the student’s performance is at a ‘B’ level or better.

Capstone Activity

  • Complete a capstone activity

The capstone activity (CA) requires students to develop and produce a short video module (approximately 5 minutes long) or equivalent multimedia module, e.g., a Jupyter notebook or a web page, that demonstrates integrative learning across the microcredential program elements. The CA must describe one or more basic concepts in DS, and explain how the student used that concept in her or his real-world work during the internship. It must show appropriate use of data communications and visualization tools. Students will also be prompted to reflect on how one or more of the professional competencies (teamwork, entrepreneurship, critical thinking, communications, collaboration, creativity, and ethics) was important to their internship work.

Total Credits Required for Micro-Credential: 6

Academic Requirements

Learning Outcomes

Graduates of the Data Science Microcredential Program will have:

  • an understanding of the different forms of data,
  • an ability to collect, analyze, and interpret data,
  • an ability to effectively communicate data and the results from analyses in various formats to appropriate audiences,
  • an ability to apply data science concepts to solve practical problems with real-world relevance,
  • an ability to practice the ethical use of data,
  • an enhanced set of professional competencies (teamwork, entrepreneurship, critical thinking, communications, collaboration, creativity, and ethics).