Research and Data Analysis

Generalized Linear Models (GLM) and Generalized Additive Models(GAM) Training Course

Introduction

In the rapidly advancing world of data science and statistical modeling, understanding and applying Generalized Linear Models (GLM) and Generalized Additive Models (GAM) has become essential for professionals working with complex data. Generalized Linear Models (GLM) and Generalized Additive Models (GAM) Training Course provides a comprehensive introduction and deep-dive into the theory, application, and interpretation of GLMs and GAMs. Using real-world datasets, learners will build predictive models, assess performance, and generate actionable insights across diverse domains such as health analytics, marketing, environmental studies, and more.

Whether you're a data scientist, biostatistician, or business analyst, this course equips you with practical skills to model non-normal data, handle non-linear relationships, and extend linear regression models using flexible and powerful tools. With rich case studies, trending examples, and step-by-step guidance in R and Python, learners will gain both conceptual clarity and technical competence to build impactful models in today's data-driven environments.

Programme Curriculum

Generalized Linear Models (GLM) and Generalized Additive Models (GAM) Training Course

Introduction

Course Objectives

Understand the foundations of Generalized Linear Models and their components.
Define and apply link functions for different distribution families.
Use Poisson regression and logistic regression for count and binary data modeling.
Interpret model coefficients and assess goodness-of-fit metrics.
Handle overdispersion and zero-inflated models in real datasets.
Explore the flexibility of Generalized Additive Models (GAM) in modeling non-linearity.
Apply smooth terms and splines in GAMs using R (mgcv) and Python (pyGAM).
Conduct model diagnostics and validate assumptions with residual analysis.
Integrate cross-validation and penalized regression techniques.
Visualize complex model results using interactive data visualization tools.
Implement real-world projects using GLM and GAM in healthcare, marketing, and ecology.
Compare performance between linear, GLM, and GAM frameworks.
Build interpretable machine learning models with modern statistical software.

Target Audiences

Data Scientists
Biostatisticians
Epidemiologists
Marketing Analysts
Environmental Researchers
Actuarial Scientists
Public Health Professionals
Machine Learning Engineers

Course Duration: 5 days

Course Modules

Module 1: Introduction to GLM

Understanding Linear Regression Limitations
Introduction to GLM Components: Random, Systematic, and Link
Distribution Families: Gaussian, Binomial, Poisson
Role of Link Functions
Software Demonstration in R and Python
Case Study: Predicting admission rates using logistic regression

Module 2: Logistic Regression Modeling

Binary Outcomes and Logit Link
Odds Ratios and Model Coefficients
Model Fit Statistics (AIC, BIC)
ROC Curves and Confusion Matrix
Hands-on Coding in R & Python
Case Study: Customer churn prediction in telecom industry

Module 3: Poisson and Count Data Models

Poisson Distribution and Canonical Link
Overdispersion and Quasi-Poisson Models
Zero-Inflated Poisson (ZIP) Modeling
Application in Insurance and Epidemiology
Model Interpretation Techniques
Case Study: Modeling disease incidence rates

Module 4: Introduction to GAM

Why Use GAMs? Flexibility and Non-linearity
Spline Functions and Smooth Terms
Estimating Smoothing Parameters
Visualizing Smooth Functions
R (mgcv) and Python (pyGAM) Use
Case Study: Modeling pollutant impact on health

Module 5: GAM Model Building and Interpretation

GAM Formula Syntax and Model Building
Checking Smoothness & Selecting Knots
GAM Model Fit and Residuals
Interpretation of Non-linear Terms
Combining Linear and Smooth Predictors
Case Study: Website traffic modeling

Module 6: Advanced Topics in GLM/GAM

Penalized Regression and Ridge/Lasso in GLMs
Hierarchical GAMs and Mixed Models
Bootstrapping and Resampling Techniques
Comparing GLM/GAM vs ML Models
Ensemble Approaches using GAMs
Case Study: Loan default prediction using penalized GAM

Module 7: Model Diagnostics and Validation

Residual Analysis and Leverage
Cross-Validation and Model Tuning
Dealing with Collinearity and Outliers
Calibration Curves and Lift Charts
Automated Reporting Tools
Case Study: Healthcare outcome modeling

Module 8: Capstone Project and Presentation

Select Your Domain (Health, Marketing, Environment)
Model Selection and Data Cleaning
Implement GLM and GAM Frameworks
Visualize and Interpret Results
Peer Review and Instructor Feedback
Case Study: Full project from raw data to business decision

Training Methodology

Interactive lectures and expert-led demonstrations
Hands-on coding with R and Python in every module
Group discussions on real-world problems
Weekly quizzes and knowledge checkpoints
Capstone project with peer review and certification

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

GLM/GAM regression advanced statistics

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000