University Catalog 2024-2025

Agriculture Data Science (Certificate)

All areas of agriculture, food, and life science have seen an explosion in data collection, ranging from plant breeders collecting phenotypic information to drones imaging fields to companies accumulating sales information. Professionals in industry, governmental, non-governmental and academics need post-baccalaureate training on how to properly collect, manage and analyze the data and then make appropriate decisions using the data.

Students will be able to take their training in this certificate in many different directions depending on their educational and employment needs. In data mining and predictive modeling, our students look for useful patterns in large data sets that would allow them to understand the past and better predict the future. In artificial intelligence and the related processes of machine learning and deep learning, our students will go several steps further, creating machines and algorithms that not only analyze and understand data, but also take the next logical steps dictated by the data.

This program will combine SAS data management and analysis techniques with computer science and statistical training to allow students to apply the processes of data mining and artificial intelligence to critical agriculture, food and life science issues. This certificate is intended for those students who have completed a BS degree in agriculture, food or life science and need additional training to be able to manage and use data in their fields. This certificate is also intended for those students who have completed a BS degree in computer science, mathematics or statistics and need additional training in how to apply data science techniques to agriculture, food and life science data issues. Students currently enrolled in a graduate program will also be eligible to complete the certificate.

More Information

Agriculture Data Science Certificate Website

Distance Website


To qualify for admission to the Graduate Certificate in Agriculture Data Science, students must have completed a BS degree in the sciences or engineering, including agriculture, biology, computer science, economics, food, genetics, life sciences, mathematics, and statistics.

Students will select one of two tracks depending on their interests and background:

  • Track A: Students who have completed a BS degree in agriculture, food or life science and need additional training to be able to manage and use big data in their fields.
  • Track B: Students who have completed a BS degree in computer science, statistics or in engineering other than biological/agricultural/biosystems engineering and need additional training in how to apply data science techniques to agriculture, food and life science data-driven decisions.

Students selecting Track A should have appropriate work experience or course prerequisites from their prior degree. Students selecting Track B should have prior experience with a high level programming language or the appropriate course prerequisites from their previous degree. Considering the number of courses that can be taken for this certificate, it is possible that students may not have all of the appropriate prerequisites for one or more of the courses. In this case, students should select other courses or contact the instructor to determine if the course(s) would be appropriate for them.

Students must have a 3.0 grade point average in their BS degree at the time of application.

Applicant Information 

  • Delivery Method: On Campus, Distance
  • Entrance Exam: None
  • Interview Required: None

Application Deadlines

Please visit The Graduate School Application Deadlines page for more information.

Plan Requirements

Certificates are distributed as "Graduate Certificate in Agriculture Data Science" without track specifications.

Required Courses6
Statistics and Computing for Agricultural Data Science
Advanced Analytics to Agriculture, Food and Life Sciences Data
Track Requirements6
Select one of the following tracks:
Total Hours12

Track A: Data Science Fundamentals

Select 6 hours of the following courses:
BAE 555/455R Coding for Data Management and Analysis3
BAE 565Environmental and Agricultural Analytics and Modeling3
CSC 440Database Management Systems3
CSC/ST 442Introduction to Data Science3
CSC 505Design and Analysis Of Algorithms3
CSC 520Artificial Intelligence I3
CSC 530Computational Methods for Molecular Biology3
CSC 540Database Management Concepts and Systems3
CSC 541Advanced Data Structures3
ST 563Introduction to Statistical Learning3
ECE 488/588/PB 488/588Systems Biology Modeling of Plant Regulation3
ECE 542Neural Networks3

Track B: Data Science Applications in Agriculture, Food, Life Science and Agricultural Economics

Select 6 hours of the following courses:
AEHS 777Qualitative Research Methods in the Agricultural Education and Human Sciences3
AEC 510Machine Learning Approaches in Biological Sciences2
AEC/FW 7263
ANS/GN 713Quantitative Genetics and Breeding3
ANS/CS/FOR 726Advanced Topics In Quantitative Genetics and Breeding3
BAE 535Precision Agriculture Technology3
BAE 536GIS Applications in Precision Agriculture1
CS 714Crop Physiology: Plant Response to Environment3
CS/HS/GN 745Quantitative Genetics In Plant Breeding1
CS 755Applied Research Methods and Analysis for Plant Sciences3
ECG/ST 561Applied Econometrics I3
ECG 562Applied Econometrics II3
ECG 563Applied Microeconometrics3
ECG 590Special Economics Topics1-6
ECG/ST 750Introduction to Econometric Methods3
ECG/ST 751Econometric Methods3
ECG/ST 752Time Series Econometrics3
ECG/ST 753Microeconometrics3
ECG 766Computational Methods in Economics and Finance3
ECG 739Empirical Methods for Development Economics and Applied Microeconomics3
ENT/GES 506Principles of Genetic Pest Management3
GN 550/450Conservation Genetics3
GN/HS/ST 757Quantitative Genetics Theory and Methods3
PP/MB 715Applied Evolutionary Population Genetics3
SSC 540Geographic Information Systems (GIS) in Soil Science and Agriculture3
SSC 545Remote Sensing Applications in Soil Science and Agriculture3