Data Analytics for Poultry and Swine Industries – Level 3

Level III:

Predictive Modeling



Next edition: September 6th until October 9th


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.

The 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: Dr. Edgar O. Oviedo Rondon.

Oviedo’s Lab Team: Maria Camila Alfaro and Gustavo Adolfo Quintana.

Instructors: Natalie Nelson and Dr. Edgar O. Oviedo Rondon

Instructional Design: Federico Etcheverry.


Structure

13 Modules

Length

600 mins.

Level

Advanced

Meet the instructors

user.png

Dr. Edgar O. Oviedo

Director

Curriculum Vitae
user.png

Natalie Nelson

Instructor

Curriculum Vitae
user.png

Federico Etcheverry

Educational design

Curriculum Vitae
user.png

Gustavo Quintana

Oviedo’s Lab Team

Curriculum Vitae
user.png

Maria Camila Alfaro

Oviedo’s Lab Team

Curriculum Vitae

Methodology

    Starting on September 6th until October 9th

    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

    Technical support

    Final Exam for Certification


Price

USD240.-