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NC State Extension

Data Analytics for Poultry and Swine Industries

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Available online


We continue to work with local and state health officials, as well as NC State and N.C. A&T State Universities, to monitor the evolving coronavirus (COVID-19) pandemic.

The second version of this Course of Data Analytics for Poultry and Swine Industries is available in a virtual interactive format and self-paced since April 4th, 2022. The next course will start on August 15, 2022.

Please know that these changes are intended to enhance learning opportunities and at the same time protect the health and safety of our community while striving to provide the resources and services you need. We will continue to closely monitor the situation and act consistently with local, state, and national recommendations.

Additional resources can be found at the links below:

We thank you for your patience and understanding at this time, and we are committed to continuing to serve and growing our community. Together, we will see it through, one day at a time.

About the workshop

This workshop started on April 4th is virtual, interactive, self-paced, and includes webinars for Q&A and meeting experts in Animal and Poultry Data Analytics around the world. The next course will start on August 15th, 2022.

Participants who complete the series will earn an Extension certificate in Data Analytics.


To provide information and training relevant to data analytics for poultry and swine industry employees.

Workshop 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 this training program

  • 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


  • Identify data
  • Plan to collect data

II. Data preparation for analysis –

Data Tables and Databases Essentials

  • Common Data Quality Issues
  • Dealing with a large amount of data
  • Identifying Issues in the Data Table
  • Identifying Issues One Variable at a Time

III. Exploratory data analysis

  • 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

II. Hypothesis Testing for Continuous Data

  • 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

III. Sample Size and Power – Dr. Edgar Oviedo

  • 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

*Advanced Analytics Program at NC State  (Introductory Video, 15 min)

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

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

Phone: +1 919.515.5391
Fax: +1 919.515.7070