Introduction to Data Mining and Data Science
Build a foundation for understanding data mining, machine learning, prediction, model evaluation, and responsible interpretation.
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Free SPSS Modeler Lessons
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Data Mining Process, Methods, and Software
Learn how a project moves from problem definition and data understanding through preparation, modeling, evaluation, and reporting.
Start LessonDecision Tree Modeling in SPSS Modeler
See how a method-focused lesson connects a visual workflow with output interpretation, common mistakes, and sample report language.
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Sixteen Lessons From Foundations Through Advanced Methods
Lessons marked Free can be opened now. The remaining applied and advanced lessons are part of the planned Founder Plan.
1. Introduction to Data Mining and Data Science
Core definitions, project logic, responsible interpretation, and the relationship among AI, machine learning, and data mining.
Open Lesson2. Data Mining Process, Methods, and Software
CRISP-DM, SEMMA, supervised and unsupervised methods, measurement levels, and SPSS Modeler workflow planning.
Open Lesson3. Data Sampling and Partitioning
Sampling quality, training and testing partitions, stratification, leakage, and imbalanced outcomes.
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Distributions, outliers, associations, charts, and evidence used to make preparation decisions.
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Missing values, derivation, recoding, binning, normalization, filtering, and leakage prevention.
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Held-out evaluation, accuracy, class-level errors, ROC, lift, reliability, validity, and model comparison.
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Linear, logistic, and multinomial regression with coefficients, fit, residuals, assumptions, and reporting.
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Tree selection, splitting, pruning, costs, class-level evaluation, interpretation, and report language.
Open Sample9. Neural Networks
MLP and RBF networks, architecture, training, overfitting, predictor importance, and evaluation.
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Bagging, boosting, stacking, diversity, comparison, and performance-versus-interpretability tradeoffs.
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Methods, results, interpretation, limitations, recommendations, and evidence-based report structure.
Request Launch Notification12. Principal Component Analysis
Component retention, eigenvalues, explained variance, loadings, rotation, scree evidence, and interpretation.
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Hierarchical, k-means, and TwoStep clustering with solution quality, profiles, stability, and reporting.
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Bootstrap samples, random feature selection, out-of-bag evidence, tuning, importance, and evaluation.
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Sequential error correction, learning rate, tree depth, boosting rounds, early stopping, and comparison.
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Network structure, conditional probabilities, classification, Markov blankets, evaluation, and noncausal reporting.
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