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Physics
209.946.2220
Maria Cordero
Administrative Assistant
3601 Pacific Avenue
Stockton, CA 95211
mcordero@pacific.edu

Data Science Minor

Minor in Data Science 

Since 1990, the Digital Revolution has transformed the global culture. In the late 1980s, less than 1% of the world's technologically stored information was in digital format. By 2007, that number increased to 94%. While much of this content has been newly generated in native digital form, the ability to reproduce digital information (text, sound, images data, and now physical 3D objects) at low or no marginal cost, and to transfer it virtually anywhere on earth instantaneously, has reformed economics and commerce, communication, science and technology, teaching and learning, as well as most aspects of daily life. The next chapter in this revolution includes microcomputers that interact with us in our spoken human languages, cars that recognize the complex visual patterns required to navigate our roadways, and our jewelry activates remote actions with a simple gesture. 

Because the use of computers to recognize patterns is becoming more ubiquitous, coupled with the remarkable interdisciplinary nature and utility of these tools, we feel it is important to provide students, from a wide variety of majors, with a foundational underdtanding of the methods, applications, and pitfalls of this new arena. 

The interdiscipinary Minor in Data Science consists of 5 required courses: 

  • Two math courses - statistics and linear algebra 
  • Two computer programming courses - focused specifically on data analysis 
  • One applications course in the student's respective academic discipline or new departamental courses

Learning Outcomes

Students successfully completing the Minor in Data Science will have demonstrated competencies in:

  • Using basic programming concepts, techniques, and structures in software tools appropriate for data analysis.
  • Collecting, combining, and preparing data for modeling and analysis.
  • Performing exploratory data analysis and visualization. 
  • Applyung the competencies above to discipline-specific examples and case studies.