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
Eligibility
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.
| Code | Title | Hours | Counts towards |
|---|---|---|---|
| Required Courses | 6 | ||
| Statistics and Computing for Agricultural Data Science | |||
| Advanced Analytics to Agriculture, Food and Life Sciences Data | |||
| Track Requirements | 6 | ||
| Select one of the following tracks: | |||
| Total Hours | 12 | ||
Track A: Data Science Fundamentals
| Code | Title | Hours | Counts towards |
|---|---|---|---|
| Select 6 hours of the following courses: | |||
| BAE 555/455 | R Coding for Data Management and Analysis | 3 | |
| BAE 565 | Environmental and Agricultural Analytics and Modeling | 3 | |
| CSC 440 | Database Management Systems | 3 | |
| CSC/ST 442 | Introduction to Data Science | 3 | |
| CSC 505 | Design and Analysis Of Algorithms | 3 | |
| CSC 520 | Artificial Intelligence I | 3 | |
| CSC 530 | Computational Methods for Molecular Biology | 3 | |
| CSC 540 | Database Management Concepts and Systems | 3 | |
| CSC 541 | Advanced Data Structures | 3 | |
| ST 563 | Introduction to Statistical Learning | 3 | |
| ECE 488/588/PB 488/588 | Systems Biology Modeling of Plant Regulation | 3 | |
| ECE 542 | Neural Networks | 3 | |
Track B: Data Science Applications in Agriculture, Food, Life Science and Agricultural Economics
| Code | Title | Hours | Counts towards |
|---|---|---|---|
| Select 6 hours of the following courses: | |||
| AEHS 777 | Qualitative Research Methods in the Agricultural Education and Human Sciences | 3 | |
| AEC 510 | Machine Learning Approaches in Biological Sciences | 2 | |
| AEC/FW 726 | 3 | ||
| ANS/GN 713 | Quantitative Genetics and Breeding | 3 | |
| ANS/CS/FOR 726 | Advanced Topics In Quantitative Genetics and Breeding | 3 | |
| BAE 535 | Precision Agriculture Technology | 3 | |
| BAE 536 | GIS Applications in Precision Agriculture | 1 | |
| CS 714 | Crop Physiology: Plant Response to Environment | 3 | |
| CS/HS/GN 745 | Quantitative Genetics In Plant Breeding | 1 | |
| CS 755 | Applied Research Methods and Analysis for Plant Sciences | 3 | |
| ECG/ST 561 | Applied Econometrics I | 3 | |
| ECG 562 | Applied Econometrics II | 3 | |
| ECG 563 | Applied Microeconometrics | 3 | |
| ECG 590 | Special Economics Topics | 1-6 | |
| ECG/ST 750 | Introduction to Econometric Methods | 3 | |
| ECG/ST 751 | Econometric Methods | 3 | |
| ECG/ST 752 | Time Series Econometrics | 3 | |
| ECG/ST 753 | Microeconometrics | 3 | |
| ECG 766 | Computational Methods in Economics and Finance | 3 | |
| ECG 739 | Empirical Methods for Development Economics and Applied Microeconomics | 3 | |
| ENT/GES 506 | Principles of Genetic Pest Management | 3 | |
| GN 550/450 | Conservation Genetics | 3 | |
| GN/HS/ST 757 | Quantitative Genetics Theory and Methods | 3 | |
| PP/MB 715 | Applied Evolutionary Population Genetics | 3 | |
| SSC 540 | Geographic Information Systems (GIS) in Soil Science and Agriculture | 3 | |
| SSC 545 | Remote Sensing Applications in Soil Science and Agriculture | 3 | |