Data Mining Training Course

Lesson 1 • Data Quality and Challenges

Examines noise, missing values, outliers, and redundancy as barriers to accurate mining. Students learn to diagnose quality issues before modelling begins.

Lesson 2 • Ethical and Privacy Considerations

Addresses consent, bias, fairness, and responsible disclosure in data mining projects. Establishes an ethical lens applied throughout the entire course.

Lesson 3 • Types of Data and Attributes

Covers nominal, ordinal, interval, and ratio scales plus structured vs. unstructured data. Attribute type determines which algorithms are applicable.

Lesson 4 • What Is Data Mining

Defines data mining, its objectives, and its position within the broader knowledge discovery process. Grounds all subsequent techniques in a unified conceptual framework.

Lesson 1 • Data Cleaning Techniques

Covers imputation strategies, noise smoothing, and duplicate removal. Clean data is the prerequisite for reliable pattern discovery in later chapters.

Lesson 2 • Feature Engineering Fundamentals

Teaches construction of new features from raw attributes to expose hidden patterns. Strong features amplify the performance of every mining algorithm covered later.

Lesson 3 • Data Integration and Fusion

Explains schema matching, entity resolution, and conflict resolution when merging multiple sources. Integrated datasets enable richer, more generalisable models.

Lesson 4 • Data Reduction Methods

Introduces dimensionality reduction, numerosity reduction, and data compression. Smaller, representative datasets reduce computation without sacrificing insight.

Lesson 1 • Outlier and Anomaly Visualisation

Uses visual methods to surface anomalies before formal detection algorithms are applied. Early visual detection prevents outliers from distorting later models.

Lesson 2 • Univariate and Bivariate Analysis

Examines single-variable distributions and pairwise relationships using histograms, box plots, and scatter plots. Reveals marginal and joint patterns in the data.

Lesson 3 • Communicating EDA Findings

Teaches chart selection, annotation, and narrative structure for presenting exploratory results. Clear communication aligns stakeholders before modelling investment begins.

Lesson 4 • Multivariate Visualisation Techniques

Introduces parallel coordinates, heat maps, and dimensionality-reduced plots for high-dimensional data. Enables simultaneous inspection of many variables.

Lesson 1 • Classifier Evaluation Metrics

Covers accuracy, precision, recall, F1, ROC-AUC, and confusion matrices. Metric selection depends on class imbalance and the cost of each error type.

Lesson 2 • Support Vector Machines

Explains maximum-margin hyperplanes, kernel functions, and soft-margin SVMs. SVMs excel in high-dimensional spaces common in text and genomic data.

Lesson 3 • Decision Trees and Rule Induction

Covers ID3, C4.5, and CART algorithms, including splitting criteria and pruning. Decision trees provide interpretable models suitable for business stakeholders.

Lesson 4 • Classification Concepts and Workflow

Defines supervised learning, training/test splits, and the bias-variance tradeoff. Establishes the evaluation mindset used across all classification algorithms.

Lesson 1 • Regularised Regression Methods

Covers Ridge, Lasso, and Elastic Net to control overfitting and perform feature selection. Regularisation is essential when features outnumber observations.

Lesson 2 • Linear Regression for Prediction

Reviews ordinary least squares, assumptions, and diagnostic plots for linear models. Linear regression provides an interpretable baseline for numeric prediction tasks.

Lesson 3 • Regression Model Evaluation

Applies MAE, RMSE, R-squared, and residual analysis to compare regression models. Proper evaluation prevents deployment of models that fail on unseen data.

Lesson 4 • Nonlinear and Polynomial Regression

Extends linear models with polynomial terms, splines, and generalised additive models. Nonlinear methods capture curved relationships missed by linear approaches.

Lesson 1 • Cluster Validation and Interpretation

Applies silhouette score, Davies-Bouldin index, and domain knowledge to assess cluster quality. Validation prevents over-reliance on arbitrary groupings.

Lesson 2 • Hierarchical Clustering

Explains agglomerative and divisive approaches with linkage criteria and dendrograms. Hierarchical methods reveal multi-scale structure without specifying k in advance.

Lesson 3 • Partitioning Methods

Covers k-means, k-medoids, and k-modes algorithms with initialisation strategies. Partitioning methods scale well and suit large, globular-shaped clusters.

Lesson 4 • Density-Based Clustering

Introduces DBSCAN and OPTICS for discovering arbitrarily shaped clusters and noise points. Density methods handle irregular geometries that k-means cannot capture.

Lesson 1 • Apriori Algorithm

Explains the Apriori property, candidate generation, and pruning for efficient itemset discovery. Students implement Apriori on retail transaction datasets.

Lesson 2 • FP-Growth Algorithm

Introduces the FP-tree structure and conditional pattern base for faster mining without candidate generation. FP-Growth outperforms Apriori on dense datasets.

Lesson 3 • Frequent Itemset Concepts

Defines support, confidence, lift, and conviction as rule quality measures. These metrics determine which patterns are statistically and practically significant.

Lesson 4 • Applications of Association Mining

Applies association rules to market basket, web clickstream, and medical co-occurrence analysis. Domain context determines which rules translate into business value.

Lesson 1 • Model Monitoring and Maintenance

Teaches drift detection, retraining triggers, and performance dashboards for deployed models. Ongoing monitoring prevents silent model degradation in production.

Lesson 2 • Ensemble and Meta-Learning Methods

Covers boosting, stacking, and blending to combine weak learners into strong predictors. Meta-learning strategies maximise accuracy on competitive benchmarks.

Lesson 3 • Model Deployment and Serving

Covers serialisation, REST API serving, batch scoring, and containerisation for production models. Deployment bridges the gap between experimental notebooks and live systems.

Lesson 4 • Anomaly and Outlier Detection

Applies statistical, proximity, and isolation-based methods to detect rare events. Anomaly detection underpins fraud detection, fault monitoring, and security.