Data Mining Specialist Course

Lesson 1 • The KDD Process Framework

Walks through each stage of the Knowledge Discovery in Databases process from raw data to actionable insight. Connects each stage to practical deliverables.

Lesson 2 • Data Types and Structures

Covers structured, semi-structured, and unstructured data formats and their implications for algorithm selection. Prepares students to handle diverse real-world datasets.

Lesson 3 • Ethical and Quality Considerations

Introduces bias, privacy, and data quality as constraints that shape every mining project. Establishes responsible practice norms applied in all subsequent chapters.

Lesson 4 • Core Data Mining Task Types

Surveys classification, regression, clustering, association, and anomaly detection as the five primary task families. Students map business problems to the correct task type.

Lesson 1 • Descriptive Statistics for Mining

Applies measures of central tendency, spread, and shape to characterize each feature. Outputs feed directly into preprocessing and feature engineering decisions.

Lesson 2 • Visual Exploratory Analysis

Uses histograms, scatter plots, box plots, and heatmaps to reveal distributions and relationships. Visualization findings are documented as part of the EDA report.

Lesson 3 • Missing Data Analysis

Identifies missing-at-random vs. not-at-random patterns and quantifies their impact on analysis. Students choose appropriate handling strategies based on missingness type.

Lesson 4 • Formulating Mining Hypotheses

Translates business questions into testable data mining hypotheses with measurable success criteria. Bridges EDA findings to the preprocessing and modeling stages.

Lesson 1 • Feature Scaling and Normalization

Covers min-max scaling, standardization, and robust scaling and explains which algorithms require each. Students transform features and verify scale consistency.

Lesson 2 • Outlier Detection and Treatment

Applies Z-score, IQR, and isolation-based methods to identify outliers and decides on removal, capping, or retention. Connects to anomaly detection tasks introduced in Chapter 1.

Lesson 3 • Encoding Categorical Variables

Applies label, ordinal, one-hot, and target encoding to convert categories into numeric representations. Addresses high-cardinality challenges common in real datasets.

Lesson 4 • Data Integration and Reduction

Merges multi-source datasets and applies dimensionality reduction to remove redundancy without losing signal. Prepares compact, clean feature sets for modeling.

Lesson 1 • Support Vector Machines

Teaches margin maximization, kernel tricks, and soft-margin SVMs for linear and nonlinear problems. Students tune kernel parameters and interpret support vectors.

Lesson 2 • Decision Trees and Rule Learners

Explains splitting criteria, tree growth, and pruning for interpretable classification models. Establishes the tree structure that underpins ensemble methods covered later.

Lesson 3 • Ensemble Classification Methods

Implements bagging, random forests, boosting, and stacking to improve accuracy and robustness. Connects to decision tree foundations from the first section of this chapter.

Lesson 4 • Probabilistic and Linear Classifiers

Covers Naive Bayes and logistic regression as fast, interpretable baselines for classification tasks. Students apply both to benchmark datasets and compare outputs.

Lesson 1 • Time-Series Regression Techniques

Adapts regression to temporal data using lag features, rolling statistics, and ARIMA-family models. Addresses autocorrelation and seasonality as unique regression challenges.

Lesson 2 • Regularized Regression Methods

Applies Ridge, Lasso, and Elastic Net to control overfitting and perform implicit feature selection. Students tune regularization strength using cross-validated grid search.

Lesson 3 • Nonlinear and Tree-Based Regression

Extends prediction to nonlinear relationships using polynomial features, regression trees, and gradient boosting regressors. Compares flexibility vs. interpretability trade-offs.

Lesson 4 • Regression Model Evaluation

Uses MAE, RMSE, MAPE, and R-squared to assess predictive accuracy and business relevance. Students select metrics based on error cost asymmetry in the target domain.

Lesson 1 • Cluster Validation and Profiling

Applies internal and external validity indices and builds descriptive profiles for each cluster. Translates statistical clusters into business-meaningful segment descriptions.

Lesson 2 • Dimensionality Reduction for Clustering

Uses PCA, t-SNE, and UMAP to reduce high-dimensional data before or alongside clustering. Improves cluster separation and enables 2D visualization of results.

Lesson 3 • Partitioning Clustering Methods

Implements k-means and k-medoids, covering centroid initialization, convergence, and sensitivity to k. Students use elbow and silhouette methods to choose optimal k.

Lesson 4 • Density-Based Clustering

Applies DBSCAN and HDBSCAN to find arbitrarily shaped clusters and label noise points. Handles datasets where partitioning methods fail due to irregular cluster shapes.

Lesson 1 • Sequential Pattern Mining

Extends association mining to ordered sequences using GSP and PrefixSpan algorithms. Applies to customer journey, clickstream, and event log analysis.

Lesson 2 • FP-Growth and Efficient Mining

Builds FP-trees to mine frequent patterns without candidate generation, dramatically reducing memory use. Compares performance against Apriori on large transactional datasets.

Lesson 3 • Rule Filtering and Deployment

Applies interestingness measures beyond lift to filter redundant and trivial rules for practical use. Students embed rules into recommendation and merchandising workflows.

Lesson 4 • Apriori Algorithm

Implements the Apriori candidate-generation approach and analyzes its computational complexity. Students tune minimum support and confidence to balance coverage and precision.

Lesson 1 • Model Deployment and Monitoring

Packages trained models as REST APIs, schedules batch scoring, and monitors for data and concept drift. Ensures models remain accurate and reliable in production environments.

Lesson 2 • Model Interpretability and Explainability

Uses SHAP values, LIME, and partial dependence plots to explain black-box model predictions. Addresses stakeholder trust and regulatory transparency requirements.

Lesson 3 • Anomaly Detection Methods

Applies statistical, proximity-based, and model-based anomaly detectors to fraud, fault, and intrusion datasets. Extends the anomaly task type introduced in Chapter 1 to full implementation.

Lesson 4 • Text Mining and NLP Basics

Converts unstructured text into numeric features using bag-of-words, TF-IDF, and word embeddings. Applies classification and clustering algorithms to text corpora.