Advanced Analytics Workshops Spring 2026
Take your data analytics skills to the next level with non-linear models for the plant sciences, as well as Bayesian modeling
Advanced Analytics Workshops for Spring 2026
In spring 2026, ID3A and the Department of Statistics offered two specialized advanced analytics workshops. Both series were available to K-State students, staff and faculty, as well as non-K-State participants.
Both series took place in 126 Leadership Studies Building.
The cost to attend each series was $20 for K-State students, $50 for K-State non-students and $500 for non-K-State participants.
These workshops were co-sponsored by ID3A and the Department of Statistics.
10 a.m. - noon
Instructors: Dr. Trevor Hefley and Claudio Dias da Silva, Jr.
Capacity: 25 participants
Non-linear models are widely used in plant sciences. Just like linear models, non-linear models can be fitted to data to enable statistical inference, which is required for scientific investigations and can aid when making management decisions. For example, logistic disease progression curves are a non-linear model used to understand plant disease epidemics, and statistical estimation and inference are needed to connect field data to management decisions (e.g., when to spray fungicide). “Non-Linear Models for the Plant Sciences” was designed to help researchers develop an understanding of non-linear models and how they fit data using common statistical techniques such as maximum likelihood estimation and Bayesian estimation. This workshop was co-hosted by the Departments of Statistics and Plant Pathology.
10 a.m. - noon
Instructor: Dr. Josefina Lacasa
Capacity: 40 participants
The use of Bayesian models for applied data analysis in biological sciences is increasing due to their flexibility to tailor said data analysis to a given problem. Due to its widespread use, understanding Bayesian statistics has become as important as classic methods such as t-tests, regression, and ANOVA. Data generated from designed experiments can benefit from using Bayesian models, because they enable researchers (i) to build bespoke statistical models tailored to a specific research question or application, and (ii) to obtain fully probabilistic and statistically valid inference not only on model components (e.g., slope parameters) but also on other indirect quantities of interest (e.g., probability yield is below a certain threshold), and (iii) to adapt the experiment design based on intermediate results and maximize the information gained from an experiment. In this workshop, we aimed to enable practitioners to understand the basics of Bayesian models, demystify standard Bayesian techniques such as Markov chain Monte Carlo, and provide real-world, hands-on agricultural data examples where Bayesian models enable new and essential insights from data generated by designed experiments. This workshop was co-hosted by the Department of Statistics.