Agriculture Data Science
The Graduate Certificate in Agriculture Data Science is an interdisciplinary graduate certificate program that applies the power of data science to agriculture, food and life science systems. Housed in NC State’s College of Agriculture and Life Science (CALS), this certificate program brings together faculty and coursework across 15 departments in CALS and the Colleges of Engineering (COE), Sciences (COS) and Natural Resources (CNR).
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 learn data collection, management and analysis methods and how to apply them to practical agriculture, food and life science questions. They will also have the opportunity to develop additional skills in data mining and artificial intelligence using real-world environments.
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 are also eligible to complete the certificate.
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.
Academic Requirements for Participants
A minimum of twelve credits must be completed; six credits from foundation courses and six from one of two tracks: Track A - Data science fundamentals. Track B - Data science applications in agriculture, food, life science and agricultural economics.
Foundation Courses (students must complete both 3-credit foundation courses):
ST 525: Statistical Methods and Computing for Data Science
BAE 542: Advanced Analytics to Agriculture, Food and Life Sciences Data
Plan of Study
A minimum of twelve credits must be completed; six credits from foundation courses and six from one of two tracks depending on your interests and background.
Career Prospects
The Graduate Certificate in Agriculture Data Science combines data management and analysis techniques with computer science and statistical training. Certificate graduates possess the knowledge and practical expertise necessary to apply the processes of data mining and artificial intelligence to critical agriculture, food and life science issues.
This translates to marketable skills in the agricultural industry, where data analysis is used for forecasting, predictive modeling and operations advising. Agriculture and food science professionals use data science expertise to advance their careers, either in moving to managerial or advisory positions. With this advanced proficiency in data use and management, graduates find employment as data analysts, advisors, researchers and managers in the life sciences and/or specifically in agriculture.