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Ag Data Science Certificate

Certificate Overview

Agriculture, food and the life sciences have seen an explosion in data collection. Professionals in industry, agribusiness, government and academia need advanced training on how to properly collect, manage and analyze the data to help inform decisions and improve outcomes.

The graduate certificate in Agriculture Data Science is an interdisciplinary graduate certificate program that applies the power of data science to issues and opportunities in agriculture, food and the life sciences. 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, as well as the Colleges of Engineering (COE), Sciences (COS) and Natural Resources (CNR).

Bridging the Big Data Divide

The new and innovative Ag Data Science Certificate course is training students to meet the high demand for data science expertise in agriculture.

What You Can Expect

You will learn data collection, management and analysis methods and how to apply them to practical agriculture, food and life science questions. Our students also have the opportunity to develop additional skills in data mining and artificial intelligence using real-world environments.

Through data mining and predictive modeling, you will learn how to look for useful patterns in large data sets that will help you understand the past and better predict the future. In artificial intelligence and the related processes of machine learning and deep learning, you will create machines and algorithms that not only analyze and understand data but also take the next logical steps dictated by the data.

But most importantly, you will have the flexibility to put your training to work in meeting your educational or career goals.

$1.4 billion

The estimated value of the global agriculture analytics market by 2025, up $800 million in 2020.

Admission Requirements and Tracks

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.

Courses & Electives

Foundation Courses

ST 525 Statistical Methods and Computing for Data Science
Instructor of Record: Dr. Paul Savariappan. Offered in Fall. Online delivery is available.
Prerequisite: ST 305, or ST 312, or ST 372, or ST 511

BAE 542 SAS Advanced Analytics to Agriculture, Food and Life Sciences Data
Instructor of Record: Dr. Dani Jones. Offered in Spring. Online delivery is available.
Prerequisite: ST 525

Elective Courses

Students who have completed a B.S. degree in agriculture, food or life science, and need training in managing and using data in their fields will be interested in 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 is 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 is 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. Offered in Fall, Spring and 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 and Spring. Online delivery available.
Prerequisite: CSC 316 and either CSC 226, LOG 201, 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 and Spring. Online delivery available.
Prerequisite: CSC 226 or LOG 201, ST 370, MA 305

CSC 530 – Computational Methods for Molecular Biology
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 and Spring.
Prerequisite: CSC 316

CSC 541 – Advanced Data Structures
Offered in Spring. Online delivery is 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 and Spring. Online delivery is available.
Prerequisite: GIS 510, GIS/MEA 582 or permission of instructor

GIS 532 – Geospatial Data Science and Analysis
Instructor of Record: Vaishnavi Thakar. Offered in Spring. Online delivery is available.
Prerequisite: GIS 510

GIS/MEA 584 – Mapping and Analysis Using UAS
Instructor of Record: Helena Mitasova. Offered in Summer. Online delivery is 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 and Summer.
Prerequisite: ST 512, ST 514, ST 515 or ST 517

Students who have completed a B.S. degree in computer science, statistics or traditional engineering, and need training in how to apply data science techniques to agriculture, food and life science issues will be interested in 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 of alternate odd years.
Prerequisite: None listed

AEC 510 – Machine Learning in Biological Sciences
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 is available.
Prerequisite: None listed

BAE 536 – GIS Applications in Precision Agriculture
Instructor of Record: Gary Roberson. Offered in Spring. Online delivery is available.
Prerequisite: GIS 410, GIS 510, 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
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
Instructor of Record: Zheng Li. Offered in Fall. Online delivery is available.
Prerequisite: None listed

ECG/ST 750 – Introduction to Econometric Methods
Instructor of Record: Dennis Pelletier. Offered in Spring.
Prerequisite: ST 421
Corequisite: ST 422

ECG/ST 751 – Econometric Methods
Instructor of Record: Dennis 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
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 of 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
Offered in Fall.
Prerequisite: None listed

SSC 540 – Geographic Information Systems (GIS) in Soil Science and Agriculture
Offered in Spring.
Prerequisite: SSC 200

SSC 545 – Remote Sensing Applications in Soil Science and Agriculture
Offered in Spring of even years.
Prerequisite: SSC 200, PY 212

12 million

The predicted number of agricultural sensors to be installed around the world by 2023.

How to Apply

Certificate Cost

Pricing follows NC State’s normal graduate course structure. Current students might be able to fold most of the certificate courses into their existing graduate program.

How To Apply

If you are not a current NC State graduate student, complete the Graduate School application to apply.

Students currently enrolled in a graduate program at NC State may apply by completing the Graduate Certificate Interest Form.

  • “Admit Term” refers to the certificate admission semester
  • “Expected Graduation Term” refers to the semester the primary degree will be completed
  • Select 11ADSCTG Agriculture Data Science – CTG under “Academic Plan”
  • No subplan is needed if you do not wish to choose a Distance Education subplan

Send the completed form to Dr. Dani Jones (dsjones5@ncsu.edu) with your Track intention (A or B) and an updated CV.

For specific questions about the Ag Data Science Certificate, complete the Interest Form.

More than 93%

of agriculture and life science companies said they expect at least one or more future positions will either require or benefit from ag data science management training.

Md Mahfuz Islam

Md-Mahfuz-Islam headshot

M.S. Student, Soil Sciences

“Completing the CALS Ag Data Science Certificate was a pivotal milestone in my academic journey. Looking back just three years, I vividly remember having no experience with agricultural data modeling. The comprehensive data analytics knowledge and skills I gained, particularly in statistical analysis, geospatial data analysis, agricultural data modeling (processed- based and machine learning), and GIS for soil science, have not only deepened my grasp of agricultural data science but have also opened up exciting opportunities in my professional career. Presently, I am actively applying this newfound data analytics knowledge to my Ph.D. dissertation and manuscripts. This certificate will continue to be a guiding light in my path to success.”

Nick Garrity

Nick Garrity headshot

M.S. Student, Crop Sciences

“NCSU’s Ag Data Science Certificate program has been a transformative experience for me. It not only deepened my understanding of statistical modeling but also empowered me with the ability to harness the power of programming and data visualization in R and SAS. These skills are essential as the world pivots towards data-driven decision making, and I am confident that this program has paved the way for my future career success.”

Mariella Carbajal Carrasco

mariella carabajal

4th year Ph.D., Biological and Agricultural Engineering

“The Ag Data Science Certificate allowed me to boost the skills gained during my Ph.D. degree program and my career by having hands-on real big data, machine learning approaches and data visualization. One of the most rewarding experiences during the certificate was participating in the SAS Hackathon 2022, where we were finalists. I chose this certificate because it fit my interests and I am confident it will have a positive impact on my professional career by helping me create decision-support tools for optimizing management practices and productivity for the benefit of local food systems and the environment. The Ag Data Science certificate is valuable for professionals with some or no experience in data science as it includes coursework for all levels across various academic departments.”

Shelly Hunt

Shelly Hunt

Senior Associate Business Development Specialist, AgTech and Consumer Goods Division at SAS Inc.

“Because of the certificate, I have honed very specific skills which allow me to understand the complex worlds of both agriculture and data science. I am not an expert in either field, but I am a translator who can speak both languages and play a critical role in interdisciplinary projects. I help bridge the gap between agronomists and data engineers to further drive innovations in both fields. Now that I have graduated, I use the skills I gained from the Agricultural Data Science certificate every day in my role with the AgTech division of SAS Institute.”

Tasmin Hossain

Tasmin Hossain

Systems Modeling Postdoctoral Research Associate, Operations Research and Analysis Team at Idaho National Lab

“The Ag Data Science Certificate is a great tool to understand how to utilize agricultural datasets in real world problem solving. The offered courses include some of the most promising aspects of agriculture and data science such as statistical analysis, agricultural data analysis and modeling, machine learning algorithms, and GIS for precision agriculture. I truly believe that the certificate is a valuable addition towards my professional development as it has helped me hone my analytical and modeling skills.”

Enrique Pena

Enrique Pena

Ph.D. Student, Electrical and Computer Engineering

“The Ag Data Science Certificate provided me with the essential skills required to approach modern agricultural challenges through a data science lens. The coursework was unique to other classes because we used authentic data from real farms to address ongoing agricultural issues. The introduction to SAS Viya stands out as a significant gain in my training. We also got to participate in a SAS Hackathon which was a lot of fun. I highly recommend this certificate to individuals passionate about agriculture, regardless of their level of computational expertise, who wish to enhance or establish their data science skills in an increasingly digital farming world.”

Shana McDowell

Shana McDowell

Ph.D. Student, Biological and Agricultural Engineering

“Cultivating a successful career as a data scientist requires tilling the fields of knowledge with precision and expertise. The Agricultural Data Science Certificate acts as a fertile ground, nurturing the seeds of skills and insights that sprout into tremendous professional growth. Just as a bountiful harvest empowers farmers, this certification equips data scientists with specialized tools to harness the transformative potential of agricultural data. From plowing through complex datasets to sowing innovative solutions, it cultivates a profound understanding of the agricultural domain, enabling data scientists to reap rich rewards. With the Agricultural Data Science Certificate in hand, you become the architect of sustainable progress, revolutionizing the agricultural landscape while nurturing a flourishing career as a data scientist.”

Frequently Asked Questions

The certificate program is designed to bring together post-baccalaureate and graduate students with two general backgrounds: those with degrees in the agriculture, food or life sciences who want to use and manage data collected from the field; and those with degrees in computer science, math or statistics who want to apply their data science skills to agriculture or agriculture-related issues. If any of these backgrounds fit yours, this certificate is right for you.

The program’s requisite is that students take 12 credit courses towards the Certificate. Six of these credits are the two fundamental courses of 3 hours each: ST 525 Statistical Methods and Computing for Data Science and BAE 542 Advanced Analytics to Agriculture, Food and Life Sciences Data. The additional required six credits are selected based on your academic background and interest. You can find a list of Track A electives here and a list for Track B electives here

The program’s length depends on how many courses you are able to take per semester. Several professionals pursuing the certificate would take one course per semester. If these courses are all 3 credit hours each, the program would take about 2 years to complete. But, the certificate can also be finished in one year if a student takes two courses per semester. 

Students are accepted on a rolling basis but classes start in alignment with the academic semesters. You can find the NC State University’s academic calendar here. Are you currently an NCSU graduate student, a future graduate student or wish to just enroll for the Certificate? If the latter, please complete your application here. Otherwise, you can simply fill out the Graduate Certificate Form here and send Dr. Daniela Jones (dsjones5@ncsu.edu) the completed form with your track intention (A or B), your major, and your updated CV.

Pricing follows NC State’s normal graduate course structure. Current NC State students might be able to fold most of the certificate courses into their existing graduate program.

There are no current financial options specific to the Graduate Ag Data Science program. But, I invite you to look through the financial support options listed by the Graduate School in case any of those apply to you. 

International certificate students do have to take the TOEFL, unless they completed higher ed in a country where English is the language of instruction.