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Curriculum Guidelines for Undergraduate Programs in Data Science

This announcement was published by the American Statistical Association. 

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for un- dergraduate programs in data science. The group consisted of 25 un- dergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major 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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