Agriculture Data Science

Units: 3

This course provides students with foundational coding skills in R, an open-source statistical software environment, as well as instruction on best practices for tidying, managing, and analyzing environmental and agricultural data, including geospatial, tabular, and time series observations. As this is an introductory course, prior programming experience is not required or expected. Coding approaches taught in the course will be targeted towards developing skills needed for summarizing data, creating data visualizations, and applying simple statistical models for analysis of environmental and agricultural data.

Offered in Fall Only

Units: 3

This course provides students with a fundamental and practical understanding of data science and modeling approaches for environmental and agricultural systems analysis. The course is organized into three modules: [1] data retrieval, management, documentation, and visualization; [2] process-based modeling; and [3] data mining through statistical analysis and machine learning. Rather than develop a strong knowledge base in a specific methodology, students will gain broad and introductory understanding of a range of contemporary quantitative approaches and learn to think critically about the use of data analytics and models.

Offered in Spring Only

Units: 3

Introduction to database concepts. This course examines the logical organization of databases: the entity-relationship model; the relational data model and its languages. Functional dependencies and normal forms. Design, implementation, and optimization of query languages; security and integrity, concurrency control, transaction processing, and distributed database systems.

Offered in Fall Only

Units: 3

Overview of data structures, data lifecycle, statistical inference. Data management, queries, data cleaning, data wrangling. Classification and prediction methods to include linear regression, logistic regression, k-nearest neighbors, classification and regression trees. Association analysis. Clustering methods. Emphasis on analyzing data, use and development of software tools, and comparing methods.

Offered in Fall Only

Units: 3

Algorithm design techniques: use of data structures, divide and conquer, dynamic programming, greedy techniques, local and global search. Complexity and analysis of algorithms: asymptotic analysis, worst case and average case, recurrences, lower bounds, NP-completeness. Algorithms for classical problems including sorting, searching and graph problems [connectivity, shortest paths, minimum spanning trees].

Offered in Fall Spring Summer

Units: 3

Introduction to and overview of artificial intelligence. Study of AI programming language such as LISP or PROLOG. Elements of AI problem-solving technique. State spaces and search techniques. Logic, theorem proving and associative databases. Introduction to knowledge representation, expert systems and selected topics including natural language processing, vision and robotics.

Offered in Fall and Spring

Units: 3

This course provides an introduction to concepts and methods for extracting knowledge or other useful forms of information from data. This activity, also known under names including data mining, knowledge discovery, and exploratory data analysis, plays an important role in modern science, engineering, medicine, business, and government. Students will apply supervised and unsupervised automated learning methods to extract patterns, make predictions and identify groups from data. Students will also learn about the overall process of data collection and analysis that provides the setting for knowledge discovery, and concomitant issues of privacy and security. Examples and projects introduce the students to application areas including electronic commerce, information security, biology, and medicine. Students cannot get credit for both CSC 422 and CSC 522.

Offered in Fall and Spring

Units: 3

Computer algorithms supporting genomic research: DNA sequence comparison and assembly, hybridization mapping, phylogenetic reconstruction, genome rearrangement, protein folding and threading.

Offered in Fall Only

YEAR: Offered Alternate Odd Years

Units: 3

Advanced database concepts. Logical organization of databases: the entity-relationship model; the relational data model and its languages. Functional dependencies and normal forms. Design, implementation, and optimization of query languages; security and integrity, consurrency control, transaction processing, and distributed database systems.

Offered in Fall and Spring

Units: 3

Complex and specialized data structures relevant to design and development of effective and efficient software. Hardware characteristics of storage media. Primary file organizations. Hashing functions and collision resolution techniques. Low level and bit level structures including signatures, superimposed coding, disjoint coding and Bloom filters. Tree and related structures including AVL trees, B*trees, tries and dynamic hashing techniques.

Offered in Spring Only

Units: 3

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

Offered in Summer

Units: 3

This course provides an introduction to the field of systems biology with a focus on mathematical modeling, gene regulatory network and metabolic pathway reconstruction in plants. Students will learn how to integrate biological data with mathematical, statistical, and computational approaches to gain new insights into structure and behavior of complex cellular systems. Students are expected to have a minimal background in calculus and basic biology. The course will build on these basic concepts and provide all students, regardless of background or home department, with the fundamental biology, mathematics, and computing knowledge needed to address systems biology problems.

Offered in Fall Only

Units: 3

This course provides an introduction to the field of systems biology with a focus on mathematical modeling, gene regulatory network and metabolic pathway reconstruction in plants. Students will learn how to integrate biological data with mathematical, statistical, and computational approaches to gain new insights into structure and behavior of complex cellular systems. Students are expected to have a minimal background in calculus and basic biology. The course will build on these basic concepts and provide all students, regardless of background or home department, with the fundamental biology, mathematics, and computing knowledge needed to address systems biology problems.

Offered in Fall Only

Units: 3

Techniques for the design of neural networks for machine learning. An introduction to deep learning. Emphasis on theoretical and practical aspects including implementations using state-of-the-art software libraries. Requirement: Programming experience [an object-oriented language such as Python], linear algebra [MA 405 or equivalent], and basic probability and statistics.

Offered in Spring Only

Units: 3

Qualitative research methods continue to gain popularity in the disciplines of agricultural & life sciences. It is becoming increasingly important for graduates to have a practical working knowledge of the development, implementation, and evaluation of these methodologies. Topics in the course will include but not be limited to: the foundation of qualitative research, data collection and analysis techniques, and review of qualitative research. Students are encouraged to have completed an introductory research methods course prior to enrolling. Introductory Research Methods course taken at the graduate level.

Offered in Fall Only

YEAR: Offered Alternate Odd Years

Units: 3

The main objective of this course is to expose upper division undergraduate students and graduate students to conservation genetic tools and applications. Students will learn the genetic and genomic theory and methods commonly used in conservation and management of species. In addition, the course will provide hands-on experience working on current conservation projects here at North Carolina State University. Working in groups, the students will collect, run, and analyze those data for a scientific paper. The final project for all students will be a conservation genetic grant proposal.

Offered in Spring Only

Units: 2

A wide range of high-throughput technologies are now being used to generate data to answer an ever-increasingly diverse set of questions about biological systems. The next great challenge is integrating data analysis in a systems biology approach that utilizes novel supervised machine learning methods, which accommodate heterogeneity of data, are robust to biological variation, and provide mechanistic insight. The course will not focus on detailed mathematical models, but instead on how these machine learning tools may be used to analyze biological data, in particular gene and protein expression.

Offered in Fall Only

Units: 3

Current methods for assessment and management of exploited fish populations, including sampling methods, data analysis and modeling. A required research paper or project.

Offered in Fall Only

YEAR: Offered Alternate Even Years

Units: 3

Quantitative and population genetic theory of breeding problems; partitioning of genetic variance, maternal effects, genotype by environment interaction and genetic correlation; selection indexes; design and analysis of selection experiments; marker-assisted selection.

Offered in Spring Only

Units: 3

Advanced topics in quantitative genetics pertinent to population improvement for quantitative and categorical traits with special applications to plant and animal breeding. DNA markers - phenotype associations. The theory and application of linear mixed models, BLUP and genomic selection using maximum likelihood and Bayesian approaches. Pedigree and construction of genomic relationships matrices from DNA markers and application in breeding.

Offered in Spring Only

Units: 3

Overview of technology available for implementation of a comprehensive precision agriculture program. Topics include computers, GPS, sensors, mechanized soil sampling, variable rate control system, yield monitors, and postharvest processing controls. Applications of precision agriculture in crop planning, tillage, planting, chemical applications, harvesting and postharvest processing. Credit may not be received for BAE 435 and BAE 535.

Offered in Spring Only

YEAR: Offered Alternate Even Years

Units: 1

Exploration of geographic information systems [GIS] and its applications in precision agriculture. Topics will include file structure and formatting, interfacing with precision agriculture equipment, georeferencing maps, merging and clipping farm data, data field calculations, designing management zones, variable rate prescriptions, and basic data analysis.

Offered in Spring Only

Units: 3

Examines interactions between plants and the environment. Light environment, plant canopy development, photosynthesis, source-sink relations, growth regulation, water relations, and environmental stresses are addressed.

Offered in Fall Only

Units: 3

Advanced topics in quantitative genetics pertinent to population improvement for quantitative and categorical traits with special applications to plant and animal breeding. DNA markers - phenotype associations. The theory and application of linear mixed models, BLUP and genomic selection using maximum likelihood and Bayesian approaches. Pedigree and construction of genomic relationships matrices from DNA markers and application in breeding.

Offered in Fall Only

Units: 1

Theory and principles of plant quantitative genetics. Experimental approaches of relationships between type and source of genetic variability, concepts of inbreeding, estimations of genetic variance and selection theory.

Offered in Spring Only

YEAR: Offered Alternate Years

Units: 3

Students will gain understanding of the common principles of scientific method. They will gain knowledge and experience with planning for research, developing research objectives, methodology considerations, experimental design, statistical analyses, and presentation of data. Class will have a heavy focus on experimental methods in applied plant science research.

Offered in Fall Only

Units: 3

Introduction and application of econometrics methods for analyzing cross-sectional data in economics, and other social science disciplines, such as OLS, IV regressions, and simultaneous equations models. Students should have had a statistical methods course at the 300 level or above as well as Calculus I and II.

Offered in Fall Only

Units: 3

This course is a continuation of Applied Econometrics I [ECG 561]. After a review of probability and statistics, and simple and multiple regression models, we explore the following topics: regression using panel [longitudinal] data, instrumental variables regression, regression with a binary dependent variable, prediction with many regressors and ``Big Data'' methods, and time series regression. The emphasis is on recognizing the conditions in which it is appropriate to apply the various techniques, formulating a relevant model, estimating the model and interpreting the results. This course will also provide the students practical experience in applied econometrics using STATA.

Offered in Spring Only

Units: 3

This course will survey econometric methods for the analysis of panel and limited dependent variable data. Both the theoretical foundation and empirical application of methods will be covered. Topics include fixed and random effects, program evaluation, censored, truncated, discrete choice and count data models. Although not required, ECG 561, ST 511 or ST 512 is encouraged prior to taking this class.

Offered in Fall Only

Units: 1 - 6

Examination of current problems on a lecture-discussion basis. Course content varies as changing conditions require new approaches to deal with emerging problems.

Offered in Fall Spring Summer

Units: 3

Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares. Extensions to time series and panel data. Consideration of endogeneity and instrumental variables estimation. Limited dependent variable and sample selection models. Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets.

Offered in Spring Only

Units: 3

Introduction to important econometric methods of estimation such as Least Squares, instrumentatl Variables, Maximum Likelihood, and Generalized Method of Moments and their application to the estimation of linear models for cross-sectional ecomomic data. Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods.

Offered in Fall Only

Units: 3

The characteristics of macroeconomic and financial time series data. Discussion of stationarity and non-stationarity as they relate to economic time series. Linear models for stationary economic time series: autoregressive moving average [ARMA] models; vector autoregressive [VAR] models. Linear models for nonstationary data: deterministic and stochastic trends; cointegration. Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity [ARCH] models. Generalized Method of Moments estimation of nonlinear dynamic models.

Offered in Spring Only

Units: 3

The characteristics of microeconomic data. Limited dependent variable models for cross-sectional microeconomic data: logit/probit models; tobit models; methods for accounting for sample selection; count data models; duration analysis; non-parametricmethods. Panel data models: balanced and unbalanced panels; fixed and random effects; dynamic panel data models; limited dependent variables and panel data analysis.

Offered in Spring Only

Units: 3

Fundamental methods for forumlating and solving economic models numerically will be developed. Emphasis on defining the mathematical structure of problems and practical computer methods for obtaining model solutions. Major topics include solution of systems of equations, complementarity relationships and optimization. Finite and infinite dimensional problems will be addressed, the latter through the use of finite dimensional approximation techniques. Particular emphasis placed on solving dynamic asset pricing, optimization and equilibrium problems. MS in Financial Mathematics Program required.

Offered in Fall Only

Units: 3

This course will provide an in-depth study of the application of the core tools of causal inference and microeconometrics to answer questions in development microeconomics. The class will largely consist of two activities: [1] close reading and guided discussion of seminal and recent papers and [2] the analysis of real data to estimate causal relationships. While the particular applications we study will come largely from development economics, the course is intended to be useful to students in diverse areas of applied micro.

Offered in Spring Only

YEAR: Offered Alternate Odd Years

Units: 3

Introduction to the biological aspects of genetic pest management [GPM]. Genetic techniques for GPM, including historical uses [such as the sterile insect technique] and approaches that are currently in development. Practical issues relating to the deployment of GPM, including ecological and economic considerations.

Offered in Fall Only

YEAR: Offered in Even Years

Units: 3

The essence of quantitative genetics is to study multiple genes and their relationship to phenotypes. How to study and interpret the relationship between phenotypes and whole genome genotypes in a cohesive framework is the focus of this course. We discuss how to use genomic tools to map quantitative trait loci, how to study epistasis, how to study genetic correlations and genotype-by-environment interactions. We put special emphasis in using genomic data to study and interpret general biological problems, such as adaptation and heterosis. The course is targeted for advanced graduate students interested in using genomic information to study a variety of problems in quantitative genetics.

Offered in Fall Only

YEAR: Offered Alternate Even Years

Units: 3

This course will introduce students to nonparametric and model-based methods for making inferences on population processes [i.e., mutation, migration, drift, recombination, and selection]. The goal is to provide a conceptual overview of these methods in lectures and hands-on training on how to analyze and interpret sample data sets in guided computer lab sessions. The course will leverage the tools and resources implemented in the DeCIFR toolkit [https://decifr.cifr.ncsu.edu/]. DeCIFR is a comprehensive suite of biodiversity informatics pipelines and visualization tools to discover, evaluate, and describe taxa at multiple spatial and phylogenetic scales. Students will apply these tools to estimate population parameters in different organisms with a focus on eukaryotic microbes, viruses, and bacteria.

Offered in Fall Only

Units: 3

Geographic information systems [GIS], global positioning system [GPS], and remote sensing to manage spatially variable soils, vegetation, other natural resources. Develop: function understanding of GIS principles, working knowledge of ArcGIS, problem-solving/critical-thinking necessary to use GIS to characterize and manage soils, agriculture, natural resources. Introduction to GIS; Maps/Cartography; Vectore/Raster Data Models; Georeferencing/Coordinate Systems; Spatial Data Sources; GPS/GPS skillls/ Remote Sensing; Statistics/Interpolation; Precision Agriculture; Computer Aided Design and GIS; Creating Analyzing 3-D Surfaces. Credit not given for both SSC 440 and SSC 540.

Offered in Fall Only

Units: 3

Overview of remote sensing including history, evolution, vocabulary, and physical principles, i.e., electromagnetic radiation and its interaction with matter. Distant and proximate remote sensing techniques [aerial photography, satellite imaging, radar, lidar, etc.], hardware, and platforms and their application in the characterization and management of soils and crops. Development of strategies for incorporating remote sensing into soil and agronomic research, and of practical skills for processing, analysis, display, and discussion of remote sensing data with applications in soil science and agriculture.

YEAR: Offered Alternate Even Years