Data Science and Statistical Programming Applied to Agriculture
CRSS 8030-8030L
3 hours. 2 hours lecture and 2 hours lab per week.
Data Science and Statistical Programming Applied to Agriculture
Course Description
Students will be exposed to data analytical workflows in agriculture utilizing data science principles. Workflows include analysis of designed and observational data (analysis of variance, regression, machine learning). Tasks will be performed using data science tools for reproducibility like version control, R, open data, code automation, and interactive dashboards.
Athena Title
Data Sci Stat Prog App to Ag
Pre or Corequisite
STAT 6315 or STAT 6315E
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will learn and apply analytical workflows that involve importing data, processing, analyzing, assessing model fit, extracting model information (means and pairwise comparisons, regression coefficients), and producing publication-ready figures for different analysis including ANOVAs and regression.
Students will conduct analysis of variance workflows for the most commonly used agricultural designed studies (completely randomized design, randomized complete block design, split-plot design).
Students will conduct linear and non-linear regression workflows.
Students will learn and apply machine learning concepts (bias-variance trade-off, data split, hyper-parameter optimization, predictive metrics) and algorithms to agricultural observational data (soils, weather, yield).
Students will learn and use data science tools for reproducibility like version control, statistical programming, APIs to publicly available data sets, task automation, and creating online interactive dashboards.
Topical Outline
1. Intro to R and RStudio (R script, R markdown, quarto, RStudio Projects)
2. Version control with git and GitHub
3. R APIs to publicly available data (USDA NASS, weather, soil)
4. Data wrangling with dplyr, tidyr, pipe operator
5. Data visualization with ggplot2, gganimate
6. Experimental concepts of experimental unit, randomization, and replication
7. Experimental and treatment designs and ANOVAs (model fit, assumption checking, inference, plot)
a. Completely randomized design (CRD)
b. Randomized complete block design (RCBD)
c. Split-plot
8. Fixed vs. Random effect
9. Repeated measure analysis
10. Automating repetitive tasks through iteration and custom functions
11. Linear regression
12. Non-linear regression
13. Regression for finding optimum
14. Machine learning concepts
a. Bias-variance trade-off
b. Data split
c. Hyperparameter optimization
d. Predictive assessment
15. Machine learning models
a. K-means (unsupervised clustering)
b. Principal component analysis
c. Elastic net regression
d. Conditional inference tree
e. Random Forest
f. XGBoost
16. Dashboards
a. Creating a simple dashboard with shiny apps
b. Publishing a dashboard online
