Advanced Data Analytics

Apply Advanced Statistics: Master hypothesis testing, multivariate analysis, and dimensionality reduction.

Optimise Data Processing & SQL: Execute complex queries and tune database performance.

Develop Predictive Models: Build and tune machine learning models for data-driven insights.

Create Data Visualisations: Build interactive dashboards with Tableau and Power BI.

Enhance Decision-Making: Leverage A/B testing, forecasting, and process optimisation.

What we cover

• Module 1: Advanced Data Analytics Foundations Evolution of Data Analytics & Key Trends: Historical context from early record-keeping to modern AI-driven and cloud architectures. Comparative Disciplines: Data Science vs. Data Analytics vs. Business Intelligence. Decision Frameworks: OODA Loop, PDCA Cycle, and the CRISP-DM methodology. Data Pipelines & Integration: Ingestion, processing, storage, monitoring, and ETL vs. ELT architectures. Governance & Scalability: Data privacy regulations (GDPR, UK Data Protection Act), bias minimization, and distributed computing frameworks (Apache Spark, Presto).
• Module 2: Advanced Statistical Analysis & Hypothesis Testing Probability Distributions: Normal, Binomial, and Poisson distributions. Core Statistical Theorems: Central Limit Theorem (CLT) applications and Bayesian inference. Parametric & Non-Parametric Hypothesis Testing: Independent two-sample T-Tests, one-way ANOVA with Tukey post-hoc analysis, and Chi-Square contingency testing. Resampling Techniques: Bootstrapping and empirical confidence interval estimation.
• Module 3: Multivariate Analysis & Dimensionality Reduction Linear Dimensionality Reduction: Principal Component Analysis (PCA) workflow, covariance matrices, and eigenvalue decomposition. Supervised Feature Extraction: Linear Discriminant Analysis (LDA) for class separability. Latent Factor Discovery: Exploratory Factor Analysis vs. PCA. Non-Linear Manifold Learning: High-dimensional visualization using t-SNE and UMAP.
• Module 4: Advanced SQL for Data Analytics Analytical Window Functions: RANK(), DENSE_RANK(), ROW_NUMBER(), and moving aggregates. Complex Data Modeling: Common Table Expressions (CTEs) and Recursive CTEs for hierarchical hierarchies. Time-Series Operations: Trend analysis, LAG(), LEAD(), and rolling window aggregates. Performance Tuning & Execution: Reading execution plans, index strategies (Clustered, Non-Clustered, Filtered), and query optimization. Advanced Joins: Self-joins, CROSS JOIN, and outlier detection with Z-scores.
• Module 5: Data Cleaning, Transformation & Feature Engineering Missing Value Management: MCAR, MAR, and MNAR mechanisms; mean/median/mode, KNN, and predictive imputation. Outlier Detection: Z-score and Interquartile Range (IQR) methods; Winsorization and clipping. Feature Scaling: Standardization (Z-score) vs. Normalization (Min-Max). Categorical Encoding: One-Hot Encoding, Label Encoding, and Target (Mean) Encoding. Feature Engineering & Selection: Polynomial/interaction features, binning, filter methods, wrapper methods (RFE), and embedded regularization (LASSO). Class Imbalance & Transformations: Resampling (SMOTE, undersampling) and skewness correction (Log, Box-Cox).
• Module 6: Exploratory Data Analysis (EDA) with Python Data Profiling: Central tendency, dispersion, skewness, and kurtosis with Pandas. Visual Data Exploration: Histograms, KDE plots, boxplots, and scatterplots via Matplotlib and Seaborn. Correlation & Normality Testing: Pearson vs. Spearman correlation heatmaps; Shapiro-Wilk and Kolmogorov-Smirnov tests. End-to-End EDA Workflow: Univariate, bivariate, and multivariate analysis pipelines.
• Module 7: Predictive Analytics & Machine Learning Models Supervised Regression: Ordinary Least Squares (OLS), Ridge Regression (L2), and Lasso Regression (L1). Classification Algorithms: Logistic Regression, log-odds, Sigmoid activation, and Decision Trees (Gini Impurity vs. Information Gain/Entropy). Model Validation: Bias-Variance tradeoff, K-Fold cross-validation, and hyperparameter search. Evaluation Metrics: MAE, MSE, R2, Accuracy, Precision, Recall, F1-score, and ROC-AUC.
• Module 8: Ensemble Learning & Model Optimization Bagging Methods: Bootstrap Aggregation and Random Forest architectures. Boosting Frameworks: Sequential error correction, XGBoost, LightGBM, and CatBoost. Model Blending & Stacking: Multi-model meta-learning architectures. Hyperparameter Optimization: Grid Search vs. Bayesian Optimization. Model Robustness: Stratified K-Fold and Leave-One-Out Cross-Validation (LOOCV).
• Module 9: Unsupervised Learning & Clustering Techniques Centroid & Density Clustering: K-Means (Elbow method, Silhouette score) and DBSCAN (Epsilon, MinPts). Hierarchical Clustering: Agglomerative vs. Divisive approaches and dendrogram analysis. Anomaly Detection: Isolation Forests for multidimensional outlier detection. Market Basket Analysis: Association rule mining using Apriori and FP-Growth algorithms.
• Module 10: Time Series Analysis & Forecasting Core Components: Trend, seasonality, cyclical patterns, and stationarity. Classical Forecasting: Differencing, ARIMA (p,d,q), SARIMA (P,D,Q,m), and Exponential Smoothing. Machine Learning & Deep Learning Approaches: Tree-based time-series forecasting (XGBoost) and Long Short-Term Memory (LSTM) neural networks. Specialized Tools & Metrics: Facebook Prophet, lag features, rolling statistics, MAE, MSE, RMSE, and MAPE.
• Module 11: Advanced Data Visualisation Techniques Aesthetic Visualization: Customizing Matplotlib and Seaborn styles, annotations, and subplots. Interactive Visualizations: Plotly dynamic dashboards, filtering, and drill-downs. Specialized Visual Displays: Heatmaps, Sankey diagrams, area charts, bubble charts, violin plots, and sunburst charts. Visual Storytelling: Communication structures and dashboard design standards.
• Module 12: Explainable AI & Model Interpretability Interpretability Frameworks: Global vs. Local model interpretability. Feature Attribution: Gini importance, Permutation Importance, and SHAP (Shapley Additive Explanations) values. Local Explanation Methods: Partial Dependence Plots (PDP) and Local Interpretable Model-Agnostic Explanations (LIME). Algorithmic Fairness: Disparate impact analysis, reweighing mitigation, and AI governance frameworks.
• Module 13: Business Intelligence with Tableau Data Preparation & Ingestion: Live vs. Extract connections; handling nulls, data types, and joins. Visualization Building: Dimensions vs. Measures; bar charts, trend lines, heatmaps, and treemaps. Interactive Dashboards: Action filters, URL actions, and drill-down interfaces. Calculations & Blending: Cross-source data blending, table calculations, and Level of Detail (LOD) expressions (FIXED, INCLUDE, EXCLUDE).
• Module 14: Data-Driven Decision Making & Optimization Techniques A/B Testing & Experimentation: Hypothesis testing workflows, sample sizes, and parameter-driven dashboard testing. Decision Tree Modeling: Segmenting strategic operational choices. Process Optimization: Process mining, demand forecasting, workforce scheduling, and supply chain logistics. Industry Case Studies: Retail/e-commerce, healthcare operations, and financial risk mitigation.
• Module 15: Data Governance & Quality Management Data Quality Dimensions: Accuracy, completeness, consistency, timeliness, and relevance. Data Lineage & Traceability: Auditing data transformations and pipelines. Tableau Prep Builder: Visual data cleaning, type casting, missing value handling, and aggregation deduplication. Compliance & Security: GDPR and HIPAA enforcement, data masking, encryption, role-based access control, and automated validation monitoring.

Every session is customized to your organization's needs. The final agenda is confirmed with your facilitator before delivery.