Undergraduate Education
The College of Agriculture provides numerous opportunities specifically for undergraduate students to develop expertise in data science and digital agriculture. By engaging with these programs, you can position yourself at the forefront of the industry and play a pivotal role in shaping the future of agriculture.
As an undergraduate in the College of Agriculture, you can find multiple opportunities in Digital Ag to gain knowledge and skills in the area of data science and digital agriculture, including data-focused courses within all 11 of our academic departments.
Majors Offered
- ABE 20500 Computations for Engineering Systems
- ABE 30100 Numerical and Computational Modeling in Biological Engineering
- ABE 31400 Design of Electronic Systems
- ABE 46000 Sensors and Process Control
- AGEC 20201 Introduction To Data Analytics For Agricultural Business
- AGEC 30500 Agricultural Prices
- AGEC 32100 Principles of Commodity Marketing
- AGEC 35200 Quantitative Techniques for Firm Decision-Making
- AGEC 42100 Advanced Commodity Marketing
- AGEC 45100 Applied Econometrics
- AGRY 44400 Weather Analysis and Forecasting
- AGRY 48500 Precision Crop Management
- ANSC 31100 Animal Breeding
- ASM 10500 Ag Systems Computations and Communications
- AT 31900 Unmanned Aerial Systems Applications, Data, and Documentation
- BTNY 30200 Plant Ecology
- ENTM 24200 Introduction to Data Science
- ENTM 30100 Experimentation and Analysis
- ENTM 41000 Applied Insect Biology
- ENTM 41001 Insects of Urban Landscapes
- ENTM 41002 Insects of Agricultural Crops
- FNR 21000 Natural Resource Information Management
- FNR 34800 Wildlife Investigation Techniques
- FNR 35500 Quantitative Methods for Resource Management
- FNR 35700 Fundamental Remote Sensing
- FNR 35900 Spatial Ecology and GIS
- FNR 38400 Statistics for Natural Resources
- FS 44400 Statistical Process Control
- ILS 29500 Introduction to Data Management
- PHIL 20700 Ethics for Technology, Engineering, and Design
- PHIL 29000 Environmental Ethics
- PHIL 29300 Ethics of Data Science
- STAT 30100 Elementary Statistical Methods
- The Data Mine (TDM courses) can be very fitting toward the majors, minors, and certificates related to data driven agriculture. Check with your advisor for applicability and for more information, visit: The Data Mine
The new College of Agriculture Internship & Job Board is live! The fall semester is the time when recruiters are filling spring and summer internships.
Find Undergraduate Employment Opportunities at:
As an undergraduate in the College of Agriculture, you can find multiple opportunities in Digital Ag to gain knowledge and skills in the area of data science and digital agriculture, including data-focused courses within all 11 of our academic departments.
Majors Offered
- ABE 20500 Computations for Engineering Systems
- ABE 30100 Numerical and Computational Modeling in Biological Engineering
- ABE 31400 Design of Electronic Systems
- ABE 46000 Sensors and Process Control
- AGEC 20201 Introduction To Data Analytics For Agricultural Business
- AGEC 30500 Agricultural Prices
- AGEC 32100 Principles of Commodity Marketing
- AGEC 35200 Quantitative Techniques for Firm Decision-Making
- AGEC 42100 Advanced Commodity Marketing
- AGEC 45100 Applied Econometrics
- AGRY 44400 Weather Analysis and Forecasting
- AGRY 48500 Precision Crop Management
- ANSC 31100 Animal Breeding
- ASM 10500 Ag Systems Computations and Communications
- AT 31900 Unmanned Aerial Systems Applications, Data, and Documentation
- BTNY 30200 Plant Ecology
- ENTM 24200 Introduction to Data Science
- ENTM 30100 Experimentation and Analysis
- ENTM 41000 Applied Insect Biology
- ENTM 41001 Insects of Urban Landscapes
- ENTM 41002 Insects of Agricultural Crops
- FNR 21000 Natural Resource Information Management
- FNR 34800 Wildlife Investigation Techniques
- FNR 35500 Quantitative Methods for Resource Management
- FNR 35700 Fundamental Remote Sensing
- FNR 35900 Spatial Ecology and GIS
- FNR 38400 Statistics for Natural Resources
- FS 44400 Statistical Process Control
- ILS 29500 Introduction to Data Management
- PHIL 20700 Ethics for Technology, Engineering, and Design
- PHIL 29000 Environmental Ethics
- PHIL 29300 Ethics of Data Science
- STAT 30100 Elementary Statistical Methods
- The Data Mine (TDM courses) can be very fitting toward the majors, minors, and certificates related to data driven agriculture. Check with your advisor for applicability and for more information, visit: The Data Mine
The new College of Agriculture Internship & Job Board is live! The fall semester is the time when recruiters are filling spring and summer internships.
Find Undergraduate Employment Opportunities at:
UNDERGRADUATE MINORS AND CERTIFICATES
Data-Driven Agriculture Minor
Pursuing the Data-Driven Agriculture Minor allows you to gain valuable skillsets to leverage the complex data sources that are becoming increasingly important in all aspects of the agriculture industry.
Digital Natural Resources Minor
The Digital Natural Resources Minor will train students in a wide-array of cutting-edge methods that leverage emerging technologies to sustainably manage our natural resources. This minor emphasizes new approaches to data acquisition (UAS-, aerial- and satellite-based platforms), analysis and novel applications in natural resources.
UNDERGRADUATE CERTIFICATE IN APPLICATIONS IN DATA SCIENCE
The Applications in Data Science Certificate is accessible to all Purdue undergraduate students; although requirements are for 16 credits in distinct categories, many of those credits will fit naturally within the requirements for many majors.
Data and Information Systems
One particular program specifically targets digital agriculture and that is the Agricultural Systems Management: Data and Information Systems concentration of the Agricultural Systems Management major.
B.S. and Minor in Unmanned Aerial Systems
The Purdue Polytechnic offers a B.S. in Unmanned Aerial Systems as well as a Minor in Unmanned Aerial Systems.
Minor in Artificial Intelligence/Machine Learning
The Artificial Intelligence and Machine Learning minor gives students a grounding in the fundamental concepts underlying modern AI and Machine Learning approaches and systems. It covers both the mathematical background as well as programming, and allows students to branch out and draw on courses across the spectrum of AI and ML topics.