Available online
Fall 2025/Spring 2026. October 6, 2025 - January 30, 2026.
The renovated version of the Course and Extension Certificate on Data Analytics for Poultry and Swine Industries is available again in its virtual, self-paced, and interactive format.
The next course will start on October 6, 2025.
About this Extension Certificate
Covers how to collect, manage, and analyze data to generate useful information to improve the results and plan future developments.It goes beyond descriptive and predictive statistics, setting the basis for mathematical modeling, big data analysis, automatic analysis, and machine learning applied to Poultry and Swine situations.Objective
To provide information and training relevant to data analytics for the poultry and swine industries.
Online Course Contents:
Induction to Analytical Thinking for Decision Making
Overview of analytics and potential benefits- From statistics to business intelligence and machine learning
- Analytics in the livestock and poultry business
- Description of contents of this workshop
- Other workshops - Material Online
- Certification Program
- Project collaborations
A. Statistical thinking and problem-solving I
- What is Statistical Thinking
- Overview of Problem-Solving
- Statistical Problem-Solving
- Types of Problems
- Defining the Problem: Goals and Key Process Indicators
- Defining the Process:
- Developing a SIPOC Map, Input/Output Process Map
- Top-Down and Deployment Flowcharts
Practical activity:Identifying Potential Root Causes
- Tools for Identifying Potential Causes
- Brainstorming, Multi-voting, Using Affinity Diagrams.
- Cause-and-Effect Diagrams, The Five Whys, Cause-and-Effect Matrices
Practice, Data visualization, and graphics I - Tableau:
- Types of Data
- Key Principles of Visualization
- Visualizing Continuous Data
- Describing Categorical Data
- Creating Visualizations with Tableau
- Visualization Distributions with Boxplots and Histograms
- Identifying Patterns and Relationships Across Variables
B. Statistical thinking and problem-solving
- Data Collection for Problem-Solving
- Types of Data
- Operational Definitions
- Data Collection Strategies
- Importing Data for Analysis
Exercises:
- Identify data
- Plan to collect data
- Common Data Quality Issues
- Dealing with a large amount of data
- Identifying Issues in the Data Table
- Identifying Issues One Variable at a Time
- Exploring Continuous Data: Enhanced Tools
- The Exploratory Data Analysis checklist
- Introduction to Descriptive Statistics
- Measures of Central Tendency and Location
- Measures of Spread — Range and Interquartile Range
- Measures of Spread — Variance and Standard Deviation
- Using Visualizations and Statistics for Exploratory Data Analysis
Practice, Data preparation, and exploratory data analysis:
- Summary of Exploratory Data Analysis Tools
- Restructuring Data for Analysis
- Combining Data
- Deriving New Variables
- Working with Dates
- Data visualization and graphics III in JMP
C. Decision-making with data
I. Estimation- Introduction to Statistical Inference
- What Is a Confidence Interval?
- Estimating a Mean
- Visualizing Sampling Variation
- Constructing Confidence Intervals
- Understanding the Confidence Level and Alpha Risk
- Prediction Intervals
- Tolerance Intervals
- Comparing Interval Estimates
- Conducting a One-Sample t-Test
- Understanding p-Values and t- Ratios
- Equivalence Testing
- Comparing Two Means
- Unequal Variances Tests
- Paired Observations
- One-Way ANOVA (Analysis of Variance)
- Multiple Comparisons
- Statistical Versus Practical Significance
Practice, Decision making with data
- Statistical Model
- ANOVA testing – Mean differences
- Multiple comparisons
- Introduction to Sample Size and Power
- Sample Size for a Confidence Interval for the Mean
- Outcomes of Statistical Tests
- Statistical Power
- Exploring Sample Size and Power
- Calculating the Sample Size for One-Sample t-Tests
- Calculating the Sample Size for Two-Sample t-Tests
D. Statistical process control and quality control
- Introduction to Control Charts
- Individual and Moving Range Charts
- Common Cause versus Special Cause Variation
- Testing for Special Causes
- X-bar and R, and X-bar and S Charts
- Rational Subgrouping
- 3-Way Control Charts
- Control Charts with Phases
E. Correlation and regression to predict responses
- What is Correlation?
- Interpreting Correlation
- Simple Linear Regression
- Introduction to Regression Analysis
- The Simple Linear Regression Model
- The Method of Least Squares
Practice, Statistical process, correlation, and regression
- Correlations - Pairwise
- Visualizing the Method of Least Squares
- Regression Model Assumptions
- Interpreting Regression Results
- Fitting a Model with Curvature
Experimentation for problem-solving and innovation
A. Design of Experiments
- Defining the Problem and Objectives
- Identifying the Responses
- Terminology of DOE
- Identifying Factors and Factor Levels
- Identifying Restrictions and Constraints
- Preparing to Conduct the Experiment
B. Designing experiments to test factors
- One-factor at a time experiments
- Designing factorial experiments
- Designing full factorial experiments
- Analyzing a Replicated Full Factorial
- Analyzing an Unreplicated Full Factorial
C. Screening Experiments
- Determining important effects
- Fractional factorial designs
- Creating screening designs in the customer designer
- Optimizing Multiple Responses
- Simulating Data Using the Prediction Profiler
D. Response Surface Experiments (RSD)
- Introduction
- RSD two factors
- Analyzing RS experiments
E. Central Composite Design (CCD)
- Designing a CCD
- Analyzing a CCD
Predictive Modeling
A. Introduction of predictive modeling
- Overfitting and model validation
- Assessing model performance
B. Multiple Linear Regression Model
- Fitting a MLR model with validation
- Assessing Model Performance: Classification models
- Receiver Operating Characteristic (ROC) Curves
C. Non-Linear Models
- Growth models
- Nonlinear responses
D. Decision Trees
- Introduction
- Classification trees
- Creating a classification tree
- Using a classification tree for problem-solving
- Identifying important variables
- Using a regression tree with validation
- Using tress to identify important variables
E. Neural Networks
- Introduction
- Interpreting neural networks
- Fitting a neural network
- Fitting a neural network with two layers
- Fitting a neural network for prediction
- Fitting a neural network for classification
F. Generalized Regression
- Introduction
- Fitting models using maximum likelihood
- Fitting a Linear Model in Generalized Regression
- Variable selection in generalized regression
- Reducing a model using generalized regression
- Penalized Regression
- Fitting a penalized regression (lasso) model
G. Model Comparison and Selection
- Comparing predictive models
- Comparing and selecting predictive models
Register Online
To register and have access to this course and other NC State courses online, you must first open a free "Brickyard account" by following these steps :
1. Navigate to go.ncsu.edu/REPORTER, and select Create New Account.
2. You will be directed to a Brickyard Account page where you will be prompted to enter your email address for the account.
3. After entering your email address, select the Send Email Confirmation button.
4. A confirmation code will be sent to the email address used in the previous step. Open a new window or browser, retrieve the code, and then return to enter it in the Confirmation Code field.
5. Enter your First Name, Last Name, and Password
6. Click Create Brickyard Account.
* Detailed instructions for opening a free Brickyard account on this website: Creating a Brickyard Account
Once you have your Brickyard account active. You can use this link to go directly to this Course. You could add to Cart, or log in using your Brickyard Login. After answering the registration questions, you can proceed to pay with Debit/Credit Card, and you will be registered.
The course will be added to your Reporter account, and the contents will be available from October 6, 2025, to March 30, 2026. Every week after October 6, new modules will be available until January 26th, 2026. The detailed schedule is described in the course.
Cancellation Notice
If your plans change, you can cancel your registration. Please note the cancellation fees.| 3 weeks or more prior | No charge — all fees will be reimbursed after the course |
| Within 2 weeks of the course | Half enrollment fee |
| Within 1 week of the course | Full enrollment fee |
For additional information contact
Dr. Edgar O. Oviedo-Rondón, Professor and Extension Specialist NC State University Cooperative Extension Service Prestage Department of Poultry Science 229 Scott Hall Raleigh, NC 27695-7608
E-mail: ncsupoultrycourses@ncsu.edu
Phone: +1 919.515.5391
Fax: +1 919.515.7070
