Introduction

In today's data-driven world, the ability to distill large datasets into meaningful insights is a competitive edge. Factor Analysis and Principal Component Analysis Training Course are advanced multivariate statistical techniques widely used in machine learning, psychology, market research, social sciences, and finance. This comprehensive training course provides a deep dive into these dimensionality reduction methods, equipping professionals with the skills needed to analyze complex datasets, reduce data redundancy, and extract significant patterns to support strategic decision-making. With real-world case studies, data visualization strategies, and practical applications, participants will confidently apply FA and PCA to large datasets using tools like SPSS, R, Python, and Excel.

Designed for beginners to advanced-level learners, this hands-on course enhances your analytical and interpretive abilities. Participants will gain actionable insights into how latent variables, eigenvalues, rotated component matrices, and exploratory factor analysis shape high-impact decisions in data science, research, and applied analytics. Learn how to visualize factor loadings, perform scree plot analysis, and determine the number of components effectively. By the end of the training, learners will confidently apply FA and PCA for research analysis, data preprocessing, and machine learning model optimization.

Programme Curriculum

Factor Analysis and Principal Component Analysis Training Course

Introduction

Course Objectives

Understand the fundamental principles of Factor Analysis and Principal Component Analysis.
Identify differences between FA and PCA in data science applications.
Perform Exploratory and Confirmatory Factor Analysis using R and SPSS.
Interpret eigenvalues, factor loadings, and variance explained.
Apply dimensionality reduction techniques to real-world datasets.
Use scree plots, KMO tests, and Bartlett's test for suitability assessment.
Build predictive models using PCA-preprocessed data.
Conduct component rotation (Varimax, Oblimin) for meaningful interpretation.
Integrate FA and PCA in Python (with sklearn and statsmodels).
Visualize factor structures using heatmaps and biplots.
Use FA/PCA to improve feature selection in machine learning.
Apply FA and PCA in business intelligence and market segmentation.
Understand ethical considerations and limitations in multivariate analysis.

Target Audience

Data Analysts
Business Intelligence Professionals
Academic Researchers
Psychometricians
Market Researchers
Machine Learning Engineers
Financial Analysts
Graduate Students in Social Sciences and Data Science

Course Duration: 10 days

Course Modules

Module 1: Introduction to Multivariate Statistics

Overview of multivariate techniques
Importance of dimensionality reduction
Applications of FA and PCA
Introduction to datasets
Common software tools used
Case Study: Survey data overview using FA

Module 2: Fundamentals of Factor Analysis

What is Factor Analysis?
Assumptions and prerequisites
Types: Exploratory vs Confirmatory
Role of latent constructs
Validity and reliability in FA
Case Study: FA in psychological testing

Module 3: Principal Component Analysis Basics

Understanding components
Eigenvalues and eigenvectors
Total variance explained
Interpreting PCA outputs
Use of scree plots
Case Study: PCA in credit scoring analysis

Module 4: Data Preparation and Cleaning

Handling missing data
Standardization and normalization
Checking for outliers
Sampling adequacy (KMO Test)
Bartlett’s test of sphericity
Case Study: Preprocessing student performance data

Module 5: Performing FA in SPSS

Input and options in SPSS
Interpreting the output tables
Rotation techniques
Factor score generation
Visualization in SPSS
Case Study: SPSS FA on market segmentation

Module 6: Performing PCA in SPSS

Steps in PCA execution
Total variance and rotated components
Saving component scores
Generating biplots
Variable clustering
Case Study: PCA on employee satisfaction

Module 7: FA and PCA in Python

Using sklearn.decomposition
PCA with real data in Python
Heatmaps and biplots with matplotlib
Interpreting PCA loadings
Using FactorAnalyzer package
Case Study: Python PCA in e-commerce

Module 8: FA and PCA in R

FA in psych package
PCA using prcomp()
Scree plot and cumulative variance
Interpreting factor loadings
Output visualization using ggplot2
Case Study: R FA on clinical trials

Module 9: Scree Plot, KMO, and Bartlett’s Test

Importance of scree plot
Cutoff criteria for component retention
KMO measure explained
Bartlett’s sphericity test
Interpreting statistical outputs
Case Study: Applying tests on retail sales data

Module 10: Rotation and Interpretation

Orthogonal vs oblique rotation
Varimax and Oblimin techniques
Simplifying component structure
Interpreting rotated component matrix
Choosing best rotation method
Case Study: Rotated PCA for behavioral clustering

Module 11: PCA for Feature Selection

Dimensionality reduction for modeling
Variance thresholds
Avoiding multicollinearity
Feature importance visualization
Use in unsupervised learning
Case Study: PCA in customer churn prediction

Module 12: FA in Market Segmentation

Cluster vs factor segmentation
Consumer behavior modeling
Segment profiling using FA
Psychographic segmentation
FA-based personas
Case Study: FA on brand loyalty study

Module 13: Machine Learning with PCA

PCA in classification models
Logistic regression and PCA
PCA in clustering (K-means)
Impact on model accuracy
Limitations in supervised learning
Case Study: PCA for fraud detection

Module 14: Ethics and Pitfalls in FA/PCA

Overfitting risks
Misinterpretation of components
Data bias in PCA
Ethical reporting of results
Transparency in analysis
Case Study: PCA misuse in predictive hiring

Module 15: Final Project and Evaluation

Project dataset selection
Analysis plan design
Performing FA and PCA
Interpreting outcomes
Peer evaluation & feedback
Case Study: Comprehensive PCA/FA on HR analytics

Training Methodology

Interactive video lectures and practical demonstrations
Hands-on exercises with datasets using R, SPSS, and Python
Peer-reviewed case study assignments per module
Live sessions with Q&A and expert analysis
Capstone project with real-world application

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

Factor analysis PCA dimensionality reduction

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Nov 09 2026

09 Nov — 20 Nov 2026

💻 Online / Virtual session

Online $2,000