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 issues. The certificate is housed in the College of Agriculture and Life Science at North Carolina State University and brings together faculty and coursework from three colleges: Agriculture and Life Science (CALS), College of Engineering (COE), and College of Science (COS) and 15 departments from those colleges.

PROGRAM OVERVIEW

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 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 at NCSU will also be eligible to complete the certificate.

INTENT OF THE GRADUATE CERTIFICATE

In this program students will learn data collection, management and analysis methods and how to apply them to practical agriculture, food and life science questions in industry, governmental, non-governmental and academics settings. Where necessary, students will be able to develop additional skills in data mining and artificial intelligence using real-world agriculture, food and life science situations.

Academic Requirements

A minimum of twelve credits must be completed; six credits from foundation courses and six from one of two tracks depending on their interests and background.

Foundation Courses

ST 525 Statistical Methods and Computing for Data Science

Instructor of Record: Dr. Paul Savariappan. Offered in Fall. Online delivery available.

Prerequisite: ST 305 or ST 312 or ST 372 or ST 511.

BAE 542 Advanced Analytics to Agriculture, Food and Life Sciences Data

Instructor of Record: Dr. Dani Jones. Offered in Spring. Online delivery available.

Prerequisite: ST 525.

Track A - Data science fundamentals

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 will be interested in Track A.

Elective courses for Track A

(Please contact instructors for updated information on each course)

BAE 555 R Coding for Data Management and Analysis

Instructor of Record: Natalie Nelson. Offered in Fall. Online delivery available.

Prerequisite: Introductory statistics (ST 370 or ST 515).

BAE 565 Environmental and Agricultural Data Analytics and Modeling

Instructor of Record: Natalie Nelson. Offered in Spring. Online delivery available.

Prerequisite: Introductory statistics (e.g. ST 515) and experience coding in R (e.g. BAE 555).

CSC 440 Database Management Systems

Instructor of Record: Kemafor Ogan. Offered in Fall.

Prerequisite: CSC 316 or ECE 309.

CSC/ST 442 Introduction to Data Science

Instructor of Record: Rada Chirkova. Offered in Fall.

Prerequisite: [MA 305 or MA 405] and [ST 305 or ST 312 or ST 370 or ST 372 or ST 380] and [CSC 111 or CSC 112 or CSC 113 or CSC 114 or CSC 116 or ST 114 or ST 445].

CSC 505 Design and Analysis of Algorithms

Instructor of Record: Steffen Heber or Matthias Stallmann or Jamie Jennings. Offered in Fall, Spring, Summer. Online delivery available.

Prerequisite: CSC 316 and CSC 226.

CSC 520 Artificial Intelligence I

Instructor of Record: Bita Akram, Collin Lynch. Offered in Fall, Spring. Online delivery available.

Prerequisite: CSC 316 and either CSC 226 or LOG 201 or LOG 335 or background in symbolic logic.

CSC 522 Automated Learning and Data Analysis

Instructor of Record: Thomas Price or Min Chi. Offered in Fall, Spring. Online delivery available.

Prerequisite: CSC 226 or LOG 201, ST 370, MA 305.

CSC 530 Computational Methods for Molecular Biology (co-requisite with CSC 505)

Instructor of Record: Steffen Heber. Offered in Fall.

Prerequisite: CSC 316, Corequisite: CSC 505.

CSC 540 Database Management Concepts and Systems

Instructor of Record: Rada Chirkova or Kemafor Ogan. Offered in Fall, Spring.

Prerequisite: CSC 316.

CSC 541 Advanced Data Structures

Offered in Fall. Online delivery available.

Prerequisite: CSC 316 .

ECE/PB 588 Systems Biology Modeling of Plant Regulation

Instructor of Record: Cranos Williams and Ross Sozzani. Offered in Fall.

Prerequisite: None listed.

ECE 542 Neural Networks

Instructor of Record: Edgar Lobaton. Offered in Fall, Spring. Online delivery available.

Prerequisite: GIS 510 or GIS/MEA 582 or Permission of Instructor.

GIS 532 Geospatial Data Science and Analysis

Instructor of Record: Vaishnavi Thakar. Offered in Spring. Online delivery available.

Prerequisite: GIS 510.

GIS/MEA 584 Mapping and Analysis Using UAS

Instructor of Record: Helena Mitasova. Offered in Summer. Online delivery available.

Prerequisite: Programming experience (an object-oriented language such as Python), linear algebra (MA 405 or equivalent), and probability (ECE 514, equivalent or instructor permission).

ST 563 Introduction to Statistical Learning

Instructor of Record: Arnab Maity, Emily Griffith, or Rui Song. Offered in Fall, Spring, Summer.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 517 .

Track B - Data science applications in agriculture, food, life science and agricultural economics

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 issues will be interested in Track B.

Elective courses for Track B

(Please contact instructors for updated information on each course and requirements)

AEE 777 Qualitative Research Methods in the Agricultural & Life Sciences

Offered in Spring, offered alternate odd years.

Prerequisite: None listed.

AEC 510 Machine Learning in Biological Sciences (2 credit hours)

Instructor of Record: Benjamin Reading. Offered in Fall.

Prerequisite: None listed.

ANS/CS/FOR 726 Advanced Topics in Quantitative Genetics and Breeding

Instructor of Record: Fikret Isik and Christian Maltecca. Offered in Spring.

Prerequisite: ST 511, Corequisite: ST 512 .

BAE 535 Precision Agriculture Technology

Instructor of Record: Gary Roberson. Offered in Spring. Online delivery available.

Prerequisite: None listed.

BAE 536 GIS Applications in Precision Agriculture (1 credit hour)

Instructor of Record: Gary Roberson. Offered in Spring. Online delivery available.

Prerequisite: GIS 410 or GIS 510 or BAE 435 or BAE 535.

CS 714 Crop Physiology- Plant Response to Environment

Instructor of Record: Randy Wells. Offered in Fall.

Prerequisite: [PB 321 or PB 421] and CH 223 or CH 227.

CS/HS/GN 745 Quantitative Genetics in Plant Breeding (1 credit hour)

Offered in Spring.

Prerequisite: CS[GN, HS] 541, ST 712, course in quantitative genetics recommended.

CS 755 Applied Research Methods and Analysis for Plant Sciences

Instructor of Record: Grady Miller. Offered in Fall.

Prerequisite: ST 511.

ECG/ST 561 Applied Econometrics I

Instructor of Record: Xiaoyong Zheng. Offered in Fall.

Prerequisite: None listed.

ECG 562 Applied Econometrics II

Instructor of Record: Ilze Kalnina. Offered in Spring.

Prerequisite: ECG 561.

ECG 563 Applied Microeconometrics

Instructor of Record: Roger Von Haefen and Harrison Fell. Offered in Fall.

Prerequisite: None listed.

ECG 590 Big Data Econometrics (Special Economics Topics - 1-6 credit hours )

Instructor of Record: Zheng Li. Offered in Fall. Online delivery available.

Prerequisite: None listed.

ECG/ST 750 Introduction to Econometric Methods

Instructor of Record: Denis Pelletier. Offered in Spring.

Prerequisite: ST 421; Corequisite: ST 422.

ECG/ST 751 Econometric Methods

Instructor of Record: Denis Pelletier. Offered in Fall.

Prerequisite: ST 421, ST 422.

ECG/ST 752 Time Series Econometrics

Instructor of Record: Ilze Kalnina. Offered in Spring.

Prerequisite: ECG[ST] 751.

ECG/ST 753 Microeconometrics

Instructor of Record: Zheng Li. Offered in Spring.

Prerequisite: ECG 751.

ECG 766 Computational Methods in Economics and Finance

Instructor of Record: Paul Fackler (RETIRING). Offered in Fall.

Prerequisite: [MA 305 or MA 405] and MA 341 and EC 301 and EC 302 and [CSC 112 or 114] or equivalents. .

ECG 739 Empirical Methods for Development Economics and Applied Microeconomics

Instructor of Record: Raymond Guiteras. Offered in Spring.

Prerequisite: ECG 751 and ECG 753.

ENT/GES 506 Principles of Genetic Pest Management

Instructor of Record: Maxwell Scott, Marce Lorenzen, and Fred Gould. Offered in Fall, offered in even years.

Prerequisite: None listed.

GN 550 Conservation Genetics

Instructor of Record: Martha Burford. Offered in Spring.

Prerequisite: None listed.

GN 713 Quantitative Genetics and Breeding

Offered in Fall.

Prerequisite: None listed.

GN/HS/ST 757 Quantitative Genetics Theory and Methods

Instructor of Record: Zhaobang Zeng. Offered in Fall.

Prerequisite: ST 511.

PP/MB 715 Applied Evolutionary Analysis of Population Genetic Data

Instructor of Record: Ignazio Carbone. Offered in Fall.

Prerequisite: None listed.

SSC 540 Geographic Information Systems (GIS) in Soil Science and Agriculture

Instructor of Record: Robert Austin. Offered in Spring.

Prerequisite: SSC 200.

SSC 545 Remote Sensing Applications in Soil Science and Agriculture

Offered in Spring. Offered alternative even years.

Prerequisite: SSC 200, PY 212.

Certificate Cost

Pricing follows the normal graduate course structure: https://studentservices.ncsu.edu/your-money/tuition-and-fees/graduate-students/. For current students, they might be able to fold one to most of the courses into their graduate program. For external students, they would follow the normal graduate course tuition rate.

Apply

To apply, complete the application at grad.ncsu.edu/apply and contact Dr. Dani Jones (dsjones5@ncsu.edu) for specific questions about the certificate. Please include an updated CV in your email. Students currently enrolled in a graduate program at NCSU do not need to apply for the certificate.