Data Analytics for Poultry and Swine Industries (Edition for NCSU students)

Full Program (Levels 1, 2 and 3)

The 5th edition starts on August 21st 2024 / Price: us$ 600

About this course

The swine and poultry industries generate enormous amount of information. This could be used to register historical series or as a data base to generate information to improve the results and plan future developments.

Analytical thinking sets the foundation for proper decision making. This process should consider the identification of the problem, the appropriate data collection, the statistical or analytical analysis, the interpretation and communication of the results, and the proper decision making. 

The course uses practical examples from the industry and real data shared by some of the main integrations of the world. Utilizes JMP, R and Tableau software with poultry and swine examples.  Opens the opportunity for customized data analytic projects.  It is a blended course, with self-paced interactive modules and virtual sessions with NC State professors and experts invited from different companies.  After completion and evaluation, the Course offers an Extension Certificate in Data Analytics for Poultry and Swine Industries.

The program includes the following topics divided in 3 levels:

Level 1:

- Overview of analytics and potential benefits
- Statistical thinking and problem solving I
- Identifying potential root causes
- Software for data visualization and analytics
- Data visualization and graphics
- Compiling and Collecting Data
- Exploratory data analysis
- Data preparation for analysis
- Decision making with data
- Hypothesis Testing for Continuous Data
- Sample Size and Power
- Statistical process control and quality control
- Correlation and regression to predict responses

Level 2:

- Design of Experiments
- Designing experiments to test factors
- Screening Experiments

Level 3:

- Introduction of predictive modelling
- Multiple Linear Regression Model
- Response Surface Regression
- Non-Linear Models
- Logistic Regression
- Generalized Regression
- Model Comparison and Selection
- Decision Trees
- Neural Networks & Clustering
- Text Mining
- Introduction to Machine Learning

Using this link, you can access a demo of the module on Statistical Thinking and Problem Solving

Director & Instructor: Dr. Edgar O. Oviedo Rondon.

Collaborators: Maria Camila Alfaro and Gustavo Adolfo Quintana.

Instructional Design: Federico Etcheverry


32 Modules


1800 mins.



Meet the instructors


Dr. Edgar O. Oviedo

Director & Instructor

Curriculum Vitae

Federico Etcheverry

Educational Design

Curriculum Vitae

Gustavo Quintana

Oviedo’s Lab Team

Curriculum Vitae

Maria Camila Alfaro

Oviedo’s Lab Team

Curriculum Vitae