DATA SCIENCE FOR AGRICULTURAL AND APPLIED ECONOMICS

AAE 335

Introduction to data analysis for social scientists using spreadsheets software - with specific applications to economics, business and finance - including data management and manipulation; formulas and calculations; data visualization and presentation using charts and graphics; statistical and visual analysis of economic indicators using tables, functions, graphs and descriptive statistics; and optimization of functions with economic and financial data.

AAE 337

Introduction to principles data visualization with applications in agricultural and life sciences. Learn to effectively communicate quantitative data via visual graphics and critically evaluate graphical data in the news and popular media. Develop practical data visualization skills in Microsoft-based programs Excel and Power BI, including tables, maps, dashboards, charts and other graphics that display trends and relationships within and between quantitative variables.

AAE 670

The fundamental mathematics and statistics necessary for the study of quantitative methods in agricultural and applied economics (AAE). Topics include the mathematics of optimization and its role in basic welfare theory and consumer demand; linear and matrix algebra and their application in both modeling consumer behavior and the statistical analysis of models; and the fundamentals of statistical analysis relevant to econometric analysis, including probability theory, sampling distributions and statistical inference.

AAE 701

Application of data science methods to economic analyses. Integration of data acquisition, cleaning, analysis, and interpretation in managerial contexts. Emphasis on applications in the agri-food supply chain.

BSE 380

Agricultural and life scientists need to creatively apply physical, chemical, and biological principles to address technical, business, and environmental challenges. Many of these challenges involve numerical analyses, including the interpretation of large datasets. The fundamentals of computer coding using numerical software will be taught, using real-world data science challenges from the agricultural and life sciences.