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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.

Additional information can be found at


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

Residential Component


  • Required. The location of learning community housing will be determined based on the incoming size and needs of the learning community. 
  • A signed housing contract is required to apply to this learning community. Once a housing contract is completed, you will indicate your learning community housing preference within the learning community application. Applications received by the priority deadline will be considered first. 
  • If you want to be placed with a preferred roommate and be admitted to a learning community with a required residential component, both you and your roommate must apply (and be accepted) to the same learning community. Students admitted to learning communities with a required residential component cannot be paired with students admitted to learning communities with an optional residential component
  • For specific question regarding learning communities, email 
  • Completing a housing contract is a separate process from applying to a learning community. If you have questions about housing, contact University Residences ( at


Full Academic Year

Associated Classes

Fall Semester

  • TDM 10100
  • TDM 11100

Spring Semester

  • TDM 10200

Biochemistry students take this course:

  • BCHM 42100 (R For Molecular Biosciences)

Additional information can be found at

Events and Activities Included:

  • Weekly dinners with Data Mine LC participants
  • Faculty and TA office hours in Hillenbrand
  • Seminars by visiting speakers, including practicing data scientists
  • Social gatherings with Data Mine LC members
  • Meals with campus and community leaders
  • Game / recreation nights
  • Career and graduate school panels
  • Hackathons / data competitions
  • Professional development activities
  • Tour of Purdue's computational facilities

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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