Industrial Data Analytics training

Lesson 1 • Analytics Value Chain in Industry

Traces how raw industrial data transforms into actionable decisions. Connects descriptive, diagnostic, predictive, and prescriptive analytics tiers.

Lesson 2 • Industrial Data Landscape Overview

Maps the types and origins of data generated in industrial environments. Establishes vocabulary and context for all subsequent analytical work.

Lesson 3 • Roles and Stakeholders in Analytics Projects

Defines responsibilities across data engineers, analysts, and domain experts. Clarifies collaboration patterns essential for project success.

Lesson 1 • Data Integration Across Systems

Merges data from disparate industrial systems into unified analytical datasets. Addresses schema conflicts and synchronisation challenges.

Lesson 2 • Data Cleaning and Preprocessing

Applies techniques to detect and correct errors, outliers, and gaps in raw data. Directly prepares datasets for exploratory and statistical analysis.

Lesson 3 • Data Quality Dimensions

Defines accuracy, completeness, consistency, and timeliness as measurable quality attributes. Provides criteria for evaluating any industrial dataset.

Lesson 4 • Industrial Data Collection Methods

Covers automated and manual data acquisition techniques used on the plant floor. Grounds students in practical collection workflows before quality assessment.

Lesson 1 • Data Visualisation Techniques

Selects and constructs charts suited to industrial time-series and categorical data. Visualisation accelerates pattern recognition and stakeholder communication.

Lesson 2 • Descriptive Statistics for Process Data

Summarises central tendency, dispersion, and distribution shape for process variables. Provides the statistical baseline for all subsequent modelling.

Lesson 3 • Anomaly and Outlier Identification

Detects unusual observations using statistical and visual methods. Distinguishes genuine process faults from measurement errors.

Lesson 4 • Feature Engineering from Process Data

Transforms raw sensor readings into informative features for modelling. Bridges exploratory analysis and machine learning workflows.

Lesson 1 • Advanced SPC and Multivariate Monitoring

Extends SPC to correlated multi-variable processes using Hotelling T-squared and CUSUM charts. Addresses limitations of univariate charts in complex systems.

Lesson 2 • Fundamentals of Statistical Process Control

Introduces common-cause vs. special-cause variation and the logic of control limits. Establishes the theoretical basis for all control chart methods.

Lesson 3 • Process Capability Analysis

Calculates Cp, Cpk, Pp, and Ppk indices to quantify process performance against specifications. Connects monitoring results to customer and design requirements.

Lesson 4 • Attribute Control Charts

Applies p, np, c, and u charts to count and proportion defect data. Extends monitoring capability to discrete quality characteristics.

Lesson 1 • Regression Modelling Fundamentals

Develops linear and polynomial regression models for continuous process outputs. Provides the modelling baseline before ensemble and nonlinear methods.

Lesson 2 • Model Training and Validation Strategies

Implements cross-validation, train-test splits, and hyperparameter tuning for robust models. Prevents data leakage common in time-ordered industrial datasets.

Lesson 3 • Deploying Predictive Models in Operations

Integrates trained models into production workflows via APIs and edge deployment. Addresses model drift monitoring and retraining triggers.

Lesson 4 • Model Performance Metrics

Selects and interprets RMSE, MAE, F1, and AUC metrics appropriate to industrial objectives. Aligns model evaluation with operational cost and risk criteria.

Lesson 1 • Maintenance Strategy Fundamentals

Contrasts reactive, preventive, and predictive maintenance approaches by cost and risk. Positions data analytics as the enabler of condition-based decisions.

Lesson 2 • Remaining Useful Life Estimation

Builds regression and survival models to estimate time-to-failure for critical assets. Outputs actionable maintenance windows from continuous sensor streams.

Lesson 3 • Sensor Data for Equipment Health

Identifies vibration, temperature, current, and acoustic signals relevant to asset health. Guides sensor selection and placement for maximum diagnostic value.

Lesson 4 • Anomaly Detection for Fault Diagnosis

Applies unsupervised and semi-supervised methods to detect early equipment faults. Reduces false alarms while maintaining high fault detection sensitivity.

Lesson 1 • Design of Experiments Principles

Introduces factorial, fractional factorial, and response surface designs for process studies. Establishes experimental rigour that separates true effects from noise.

Lesson 2 • Analysis of Variance for Process Factors

Applies one-way and multi-way ANOVA to quantify factor significance in designed experiments. Connects statistical significance to practical process improvement.

Lesson 3 • Taguchi Methods and Robust Design

Uses orthogonal arrays and signal-to-noise ratios to minimise process sensitivity to noise. Delivers designs that perform consistently under real operating variation.

Lesson 4 • Optimisation Algorithms for Process Settings

Applies gradient-based and metaheuristic algorithms to find optimal process parameters. Extends beyond DOE to continuous and constrained optimisation problems.

Lesson 1 • Scaling Analytics Across the Enterprise

Designs centre-of-excellence models and platform strategies for organisation-wide analytics adoption. Addresses change management and capability-building at scale.

Lesson 2 • Digital Twin Integration with Analytics

Connects physics-based simulation models with live sensor data for real-time process insight. Enables what-if scenario analysis without disrupting physical operations.

Lesson 3 • Building the Analytics Business Case

Quantifies financial and operational benefits of analytics initiatives for executive approval. Structures ROI calculations, risk assessments, and milestone roadmaps.

Lesson 4 • Analytics Governance and Ethics

Establishes model accountability, bias auditing, and explainability standards for industrial AI. Ensures analytics outputs meet regulatory and organisational trust requirements.