Curriculum Guidelines for Undergraduate Programs in Data Science

Curriculum Guidelines for Undergraduate Programs in Data Science

Table of Contents:
1. Introduction …………………………………………………………………………………….. 2
2. Background and Guiding Principles……………………………………………………………….. 3
  2.1. Data Science as Science …………………………………………………………………….. 4
  2.2. Interdisciplinary Nature of Data Science ……………………………………………………… 4
  2.3. Data at the Core ……………………………………………………………………………. 5
  2.4. Analytical (Computational and Statistical)Thinking …………………………………………… 5
  2.5. Mathematical Foundations…………………………………………………………………… 6
  2.6. Flexibility …………………………………………………………………………………… 6
3. Key Competencies and Features of a Data Science Major ………………………………………….. 6
  3.1. Analytical Thinking …………………………………………………………………………. 7
  3.2. Mathematical Foundations…………………………………………………………………… 8
  3.3. Model Building and Assessment ……………………………………………………………… 8
  3.4. Algorithms and Software Foundation …………………………………………………………. 9
  3.5. Data Curation ………………………………………………………………………………. 9
  3.6. Knowledge Transference …………………………………………………………………….. 9
4. Curricular Content for Data Science Majors……………………………………………………….. 10
  4.1. Overview of Course Sequence………………………………………………………………… 11
5. Additional Considerations ……………………………………………………………………….. 13
6. Transitioning to a Data Science Major Using Typical Existing Courses ……………………………… 15
  6.1. Courses in Mathematics……………………………………………………………………… 15
  6.2. Courses in Computer Science ………………………………………………………………… 16
  6.3. Courses in Statistics…………………………………………………………………………. 16
  6.4. Related Courses …………………………………………………………………………….. 16
7. Summary and Next Steps ………………………………………………………………………… 16
8. Appendix – Detailed Courses for a Proposed Data Science Major…………………………………… 18

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