Certificate Course Listing

 

Data-Driven Food, Energy, and Water Decision Making

The graduate certificate aims to prepare masters and doctoral students for multiple career paths in the food-energy-water nexus, such as research scientist, bioeconomy entrepreneur, agribusiness leader, policymaker, agriculture analytics specialist, or professor, by credentialing their understanding of the FEWS nexus and their skills in quantitative analysis of complex systems.

The graduate certificate consists of 13 credits across five courses – two required courses, plus one elective course within each of three focus areas. Coursework is designed to fit within the major program of study.

Certificate Curriculum

Knowledge Area Required Courses
Fundamental understanding of interactions in the FEWS nexus (select 1)

ABE 585x Biosystems for Sustainable Development (was ABE 690) (2 credits) 

OR

IE 690 Advanced Topics.  This is an independent study contact Dr. Ryan before registering smryan@iastate.edu (2 credits)

Communication GR ST 566 Communications in Science (1 credit)

Data visualization and analytics

Choose One elective from this category
 

A B E 504: Instrumentation for Agricultural and Biosystems Engineering

Interfacing techniques for computer-based data acquisition and control systems. Basic interfacing components include A/D and D/A conversion, signal filtering, multiplexing, and process control. Sensors and theory of operation applied to practical monitoring and control problems. Individual and group projects are required for graduate credit.

 

M E 592X: Data Analytics and Machine Learning for Cyber-Physical Systems Applications

In this course, several data analytics techniques and Machine Learning algorithms will be explored with a strong focus on various applications to cyber-physical systems. The students will have hands-on experience with various analytics tools and data-driven decision-making techniques applied to a diverse set of spatial, temporal and spatiotemporal data emanating from real-life cyber-physical systems such as robots, energy & power systems, design & manufacturing systems, self-driving cars and agricultural systems. Among various machine learning techniques, special emphasis will be given on deep learning, reinforcement learning and probabilistic graphical models. A key highlight of this course is that the assignments and class projects will be designed for individual students or groups based on their specific applications or data sets of interest.

 

E E 525X: Data Analytics in Elect. & Comp. Engineering

Introduction to a variety of data analytics techniques — particularly those relevant for electrical and computer engineers — from a foundational perspective. Topics to be covered include techniques for classification, visualization, and parameter estimation, with applications to signals, images, matrices, and graphs. Emphasis will be placed on rigorous analysis as well as the principled design of such techniques.

 

I E 583: Data Mining

Foundations of classification, data clustering, and association rule mining. Techniques for data mining, including probabilistic and statistical methods. Focus on tree-based methods for classification (simple trees, random forest and boosted trees), ensemble learning, optimization algorithms and deep learning with neural networks. Case studies from both manufacturing and service industries. A computing project in R is required.

 

 IE 592x: Analytics Projects for Improved Decision Making in the Service Sector

Provide practices in data analytics and decision modeling, while simultaneously honing communication and teaming skills, through the development and completion of an industrial design project supplied by a real-world company. 

 

STAT 585 Data Technologies for Statistical Analysis

Introduction to computational methods for data analysis. Accessing and managing data formats: flat files, databases, web technologies based on mark-up languages (SML, KML, HTML), netCDF. Elements of text processing: regular expressions for cleaning data. Working with massive data, handling missing data, scaled computing. Efficient programming, reproducible code.

 

I E 520X. Engineering Problem Solving with R.

 Statistical analysis and engineering problem solving using R programming language. Data manipulation. Exploratory data analysis. Statistical quality assurance. Basic statistical analysis. R Markdown. Simulation. Conditional expressions, loops, and functions. Matrices. High-level data visualizations. Data extraction from text. Optimization. Logistic regression. High-performance computing tools. The project required for graduate credits.

  Note: Cohorts 1, 2 & 3 can use STAT 587, 510, & 575, but are encouraged to explore other data visualizations & analytics to broaden their experience.
   

Complex systems modeling for decision support

Choose One elective from this category
 

A B E 580: Engineering Analysis of Biological Systems

Systems-level quantitative analysis of various biological systems, including applications in foods, feeds, biofuels, bioenergy, and other bio-based systems. Introduction to techno-economic analysis and life-cycle assessment of these systems at multiple production scales. Applying these tools to evaluate and improve cost and sustainability performance. Students enrolled in ABE 580 will be required to conduct additional learning activities.

 

CE 574X:  Integrated Assessment Modeling and Science-Policy Integration for Global Environmental Change. 

Overview of conceptual insights and quantitative analysis of global climate change with a focus on physical models and policy implications. State-of-the-art Integrated Assessment Models (IAMs) within a context of water sustainability with implications to the energy, food, and environmental sectors. Water resources related environmental management and problem-solving under global change. Climate modeling and impacts of climate change on water, energy, and food systems. Application of Integrated Assessment Models (IAMs) in understanding the water-energy-food nexus. National and international climate policy.

 

I E/E E/AER E 565: Systems Engineering & Analysis

Introduction to an organized multidisciplinary approach to designing and developing systems. Concepts, principles, and practice of systems engineering as applied to large integrated systems. Life cycle costing, scheduling, risk management, functional analysis, conceptual and detail design, test and evaluation, and systems engineering planning and organization. Not available for degrees in industrial engineering.

 

I E 564: Decision Analysis in System Design

Application of normative decision theory to problems with uncertainty and/or multiple objectives. The first decision framework will be a single-objective decision problem with uncertainty that takes into account a decision maker’s attitude towards risk. The second decision framework will be a multi-criteria decision problem in which a decision maker has multiple objectives. Topics include utility theory, value of information, sensitivity analysis, value-focused thinking, cost-effectiveness analysis, influence diagrams, and behavioral decision making. Examples will be drawn from business, systems engineering and design, and government.

 

I E/AERE 568: Large-Scale Complex Engineered Systems

Introduction to the theoretical foundation and methods associated with the design for large-scale complex engineered systems, including objective function formation, design reliability, value-driven design, product robustness, utility theory, economic factors for the formation of a value function and complexity science as a means of detecting unintended consequences in the product behavior.

 

M E 525: Optimization Methods for Complex Design

Optimization involves finding the ‘best’ according to specified criteria. Review of a range of optimization methods from traditional nonlinear to modern evolutionary methods such as Genetic algorithms. Examination of how these methods can be used to solve a wide variety of design problems across disciplines, including mechanical systems design, biomedical device design, biomedical imaging, and interaction with digital medical data. Students will gain knowledge of numerical optimization algorithms and sufficient understanding of the strengths and weaknesses of these algorithms to apply them appropriately in engineering design. Experience includes code writing and off-the-shelf routines. Numerous case studies of real-world situations in which problems were modeled and solved using advanced optimization techniques.

 

AGRON 525: Crop and Soil Modeling

Understanding basic crop physiology and soil processes through the use of mathematical and statistical approaches. Structure of crop models, dynamics, and relationship among components such as leaf-level photosynthesis, canopy architecture, root dynamics, and soil carbon and nitrogen pools

 

AGRON 504: Global Change

Recent changes in global biogeochemical cycles and climate; models of future changes in the climate system; impacts of global change on agriculture, water resources, and human health; ethical issues of global environmental change.

 

ABE 537 Watershed Modeling and Policy

A project-based course on watershed-scale models for improving water quality. Legislative and judicial basis of the Total Maximum Daily Load (TMDL) program; approaches to TMDL development; principles and techniques for implementation; stakeholder engagement strategies. Hands-on experiences with GIS-interfaced models, data sources, calibration/validation, statistical assessment of model results, and simulation using multiple tools. In addition to other assignments, graduate students will present case studies of TMDLs using different modeling tools.

 

ME 585 Fundamentals of Predictive Plant Phenomics

Principles of engineering, data analysis, and plant sciences and their interplay applied to predictive plant phenomics. Transport phenomena, sensor design, image analysis, graph models, network data analysis, fundamentals of genomics, and phenomics. Multidisciplinary laboratory exercises.

 

ME 531: Advanced Energy Systems and Analysis

Introduction to energy systems including economic and thermodynamic principles. Various production systems will be analyzed. Application to transportation and building systems will be emphasized. Sustainability, climate change, and other current energy system topics.

Economics, policy, or sociology of FEWS

Choose One elective from this category
 

ECON 560: Agricultural, Food, and Trade Policy

Description and analysis of economic problems of U.S. agriculture. Explanation and economic analysis of government policies and programs to develop agriculture, conserve agricultural resources, address consumer food concerns, stabilize farm prices, and raise farm incomes. The influence of macropolicy, world economy, international trade, and bioenergy on U.S. agriculture.

 

ECON 580: Interm. Environment. & Resource Economics

Theories of natural resource utilization and allocation. Externalities, public goods, and environmental quality. Renewable energy, biofuels, land use change and life cycle analysis of carbon, and sustainability and resource conservation. Methodologies for analyzing natural resource and environmental problems and evaluating resource policies.

 

ECON 581: Advanced Environmental Economics Interrelationships of natural resource use and the environment.

Applied welfare and benefit-cost analyses. Externalities and pollution abatement. Nonmarket valuation of resources. Property rights. Legal and social constraints. Policy approaches.

 

POL S 515: Biorenewables Law and Policy

Evaluation of the biorenewables field as it relates to the areas of law and policy. Primary emphasis on the following topics: concerns that motivated the development and expansion of the biorenewables field, a history of the interactions between biorenewable pathways. U.S. law and policy and controversies that have arisen from these interactions and their effects.

 

M E 510: Econ. & Policy of Engineering Energy Systems

Economics and policy for U.S. energy systems, with an emphasis on connections to engineering. Topics include: economic analysis of conventional energy commodity markets and technologies, deregulated electricity markets, and emerging energy technologies; demand forecasting; economic and environmental policy in energy; integrated assessment; and semester-specific contemporary issues. Economics majors may not apply this course towards graduation.

 

NREM 570: Advanced Decision-making in Natural Resource Allocation

Analytical approach to economic aspects of forest resource management problems. Theory and application of economic decision-making criteria to traditional and modern forest resource management issues. Current problems in the allocation of forest resources.

 

NREM 585: Natural Resource Policy

Development, theory, and practice of natural resource policy. Integrative approach with topical policy studies in North American wildlife, forestry, and water. Policy formation, the role of science, introduction to federal law compliance.

 

SOC 544: Sociology of Food and Agricultural Systems

Social organization of food and fiber production, processing, and distribution systems. Sociological comparison of conventional and alternative production systems; gender roles in agriculture and food systems; local, national and global food systems; perspectives on food and agricultural research and policy.

 

SOC 549: Sociology of the Environment

Social causes and social consequences of environmental problems. Interrelationship between social inequality and environmental inequality. Social construction and social experience of the environment. Contemporary developments in the social theory of the environment. International and domestic implications.