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This community within The Data Mine allows students to gain learning outcomes directly related to their major program of study within the College of Agriculture. Students from every department in the College of Agriculture are welcome to apply. Potential research examples include the use of GIS technologies for soil science and using remote sensing for precision agriculture. As a result of such projects, students will be able to work in fast-growing fields such as precision agriculture using remote sensing with open-source data analysis tools. Other examples of disciplines in which students will benefit from Data Science expertise include spatial applications in agronomy, animal sciences, forestry, entomology; genomic applications in biochemistry and botany; etc.

This year will feature a partnership with the Agriculture Dean's Scholars learning community.  Select members from Dean's Scholars, who recognize the importance of having data analytical skills in the agricultural industry, will also participate in The Data Mine. 


  • Any undergraduate student enrolled in the College of Agriculture or Exploratory Studies with an interest in the Agriculture and Data Sciences

Residential Component

  • Students from this learning community must reside in Hillenbrand Hall
  • Information on this residence hall can be found here
  • Students who are required to reside in a different residence hall (e.g. due to the Honors College or athletics participation) or who do not sign a housing contract may not participate in this learning community
  • Your roommate in most cases will be a member of the learning community
  • Completing a housing contract is a separate process from applying for a learning community


  • Fall and Spring semesters

Associated Classes

  • First year courses
    • (First year Dean's Scholars only) Fall: AGR 29000-H01 – Dean’s Scholars Seminar
    • (First year Dean's Scholars only) Spring: AGR 29400-H01 – Dean’s Scholars Directed Reading Seminar
    • Fall and Spring: STAT 19000 1 credit per semester -- The Data Mine I and II
  • Classes and schedule tailored specifically to address the interests of students
  • Placement in classes with students of similar interests. *Examples* could include (other examples could be possible too, depending on student interest and professional goals):
    • AGEC 35200 – Quantitative Techniques For Firm Decision Making
    • AGRY 56500 – Soils and Landscapes
    • ASM 10400 Introduction To Agricultural Systems, paired with ASM 10500 Agricultural Systems Computations and Communication
    • BCHM 49500 - 001 – R For Molecular Biosciences
    • BCHM 49500 - 002 – Computational Genomics
    • ENTM 30100 – Experimentation And Analysis
  • One required pair of seminars:
    • Fall and Spring: STAT 19000 1 credit per semester -- The Data Mine I and II

Events and Activities Included:

  • Weekly dinners with LC participants
  • Tour of Purdue’s computational facilities
  • Social gatherings with LC members
  • Seminars by visiting speakers including practicing actuaries and data scientists
  • Meals with campus and community leaders
  • Game / recreation nights
  • Career and graduate school panels

Information above is subject to change. If you are placed in the LC, the associated courses will be on your schedule prior to you registering for the rest of your courses.

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