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:聽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:聽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聽
- Delivery Method:聽On Campus, Distance
- Entrance Exam: None
- Interview Required: None
Application Deadlines
Please visit page for more information.
Plan Requirements
Certificates are distributed as "Graduate Certificate in Agriculture Data Science" without track specifications.
| Code | Title | Hours |
|---|---|---|
| Required Courses | 6 | |
| Statistics and Computing for Agricultural Data Science | ||
| SAS 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 |
|---|---|---|
| Select 6 hours of the following courses: | ||
| BAE 555/455 | R Coding for Data Management and Analysis | 3 |
| 叠础贰听565 | Environmental and Agricultural Analytics and Modeling | 3 |
| 颁厂颁听440 | Database Management Systems | 3 |
| CSC/ST 442 | Introduction to Data Science | 3 |
| 颁厂颁听505 | Design and Analysis Of Algorithms | 3 |
| 颁厂颁听520 | Artificial Intelligence I | 3 |
| 颁厂颁听530 | Computational Methods for Molecular Biology | 3 |
| 颁厂颁听540 | Database Management Concepts and Systems | 3 |
| 颁厂颁听541 | Advanced Data Structures | 3 |
| 厂罢听563 | Introduction to Statistical Learning | 3 |
| ECE 488/588/PB聽488/588 | Systems Biology Modeling of Plant Regulation | 3 |
| 贰颁贰听542 | Neural Networks and Deep Learning | 3 |
Track B: Data Science Applications in Agriculture, Food, Life Science and Agricultural Economics
| Code | Title | Hours |
|---|---|---|
| Select 6 hours of the following courses: | ||
| 础贰贬厂听777 | Qualitative Research Methods in the Agricultural Education and Human Sciences | 3 |
| 础贰颁听510 | Machine Learning Approaches in Biological Sciences | 2 |
| ANS/GN 713 | Quantitative Genetics and Breeding | 3 |
| ANS/CS/FOR 726 | Advanced Topics In Quantitative Genetics and Breeding | 3 |
| 叠础贰听535 | Precision Agriculture Technology | 3 |
| 叠础贰听536 | GIS Applications in Precision Agriculture | 1 |
| 颁厂听714 | Crop Physiology: Plant Response to Environment | 3 |
| CS/HS/GN 745 | Quantitative Genetics In Plant Breeding | 1 |
| 颁厂听755 | Applied Research Methods and Analysis for Plant Sciences | 3 |
| ECG/ST 561 | Applied Econometrics I | 3 |
| 贰颁骋听562 | Applied Econometrics II | 3 |
| 贰颁骋听563 | Applied Microeconometrics | 3 |
| 贰颁骋听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 |
| 贰颁骋听766 | Computational Methods in Economics and Finance | 3 |
| 贰颁骋听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 |
| 厂厂颁听540 | Geographic Information Systems (GIS) in Soil Science and Agriculture | 3 |