Data Analytics for Poultry and Swine Industries – Level 3
Next edition: January 2022
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.
In this course you will learn more about applications of regression like multiple linear regression, response surface regression, non-linear regression, logistic regression, and generalized regression. These forms are important to predict responses and formulate prescription of how a particular result can be obtained given a set of conditions. We will also learn the most important classification techniques: decision trees and neural networks. These techniques are used in statistics, data mining, machine learning and artificial intelligence to determine target values and threshold points, make decisions, classify groups, model multiple relations among variables hard to detect and describe with regular regression methods. Finally, text mining and machine learning are introduced to use other type of data that is already available in great amounts, develop more advanced tools of prediction, prescription, detection and control of anomalies.
The course uses practical examples from the industry and real data shared by some of the main integrations of the world. Utilizes JMP and 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 NCSU 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.
program includes the following topics:
- Software for Data Visualization and Analytics
- Data visualization and graphics
- 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
Director & Instructor: Dr. Edgar O. Oviedo Rondon.
Oviedo’s Lab Team: Maria Camila Alfaro and Gustavo Adolfo Quintana.
Instructional Design: Federico Etcheverry.
13 self-paced and on demand interactive modules
- Available online and offline (Anpro Campus app)
- Multiple supports: desktop or mobile platforms
- Multimedia content
4 Virtual sessions with NC State professors and invited experts.
Support by the Experts
Final Exam for Certification