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Industrial Data Science Solutions from Raw Data to Decisions

30 Chapters
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01
Industrial data science landscape
defining the raw data to decisions pipeline, key stakeholders, and value drivers in manufacturing, energy, and process industries.
Industriallandscape
02
Industrial data sources and acquisition
sensors, PLCs, SCADA, historians, MES, ERP, and IIoT gateways; sampling rates, timestamps, and data contracts.
Industrialacquisition
03
Data quality assessment for industrial time series
missing values, outliers, sensor drift, stuck signals, and noise characterization with engineering context.
assessmentindustrial
04
Exploratory data analysis for industrial datasets
multivariate visualization, correlation analysis, lag plots, and identifying operational regimes.
Exploratoryanalysis
05
Feature engineering from raw sensor signals
windowing, statistical aggregates, spectral features, and domain-specific indicators like vibration or pressure ratios.
windowingstatistical
06
Data preprocessing and cleaning pipelines
imputation strategies, denoising, resampling, synchronization, and handling irregular timestamps.
preprocessingcleaning
07
Dimensionality reduction and latent variable methods
PCA, PLS, and autoencoders for process monitoring and fault detection.
Dimensionalityreduction
08
Supervised learning for industrial regression tasks
predicting quality variables, energy consumption, and remaining useful life with tree ensembles and regularized linear models.
Supervisedindustrial
09
Supervised learning for industrial classification tasks
fault diagnosis, defect detection, and anomaly classification with imbalanced data.
Supervisedindustrial
10
Unsupervised learning for anomaly detection
isolation forests, one-class SVM, clustering, and reconstruction-based methods for unlabeled industrial data.
Unsuperviseddetection
11
Time series forecasting in industrial operations
demand forecasting, load prediction, and degradation trending with ARIMA, Prophet, and deep learning models.
forecastingindustrial
12
Deep learning for industrial sensor data
CNNs for vibration and acoustic signals, RNNs and LSTMs for sequence modeling, and temporal convolutional networks.
industrialCNNs
13
Model evaluation and validation for industrial applications
cross-validation with temporal splits, metrics for imbalanced and cost-sensitive problems, and uncertainty quantification.
evaluationvalidation
14
Deployment of industrial data science models
edge vs. cloud architectures, model serving, latency constraints, and integration with control systems.
Deploymentindustrial
15
MLOps for industrial analytics
versioning, monitoring, drift detection, retraining pipelines, and lifecycle management in regulated environments.
MLOpsindustrial
16
Predictive maintenance case study
end-to-end workflow from raw vibration and temperature data to remaining useful life estimation and maintenance scheduling.
Predictivemaintenance
17
Quality prediction and process optimization case study
soft sensors, inferential models, and closed-loop advisory systems in chemical or semiconductor manufacturing.
predictionoptimization
18
Energy management and sustainability analytics
load disaggregation, efficiency benchmarking, and carbon footprint estimation from operational data.
managementsustainability
19
Supply chain and logistics analytics
demand sensing, inventory optimization, and transportation network analysis using industrial data.
logisticsanalytics
20
Root cause analysis and troubleshooting with data
causal inference, fault trees, and contribution plots for diagnosing complex process deviations.
analysistroubleshooting
21
Human-in-the-loop and decision support systems
dashboards, alerting, explainable AI, and operator trust in industrial settings.
Human-in-the-loopdecision
22
Data governance, security, and privacy in industrial environments
IT/OT convergence, access control, data lineage, and compliance with standards like IEC 62443.
governancesecurity
23
Scalable data architectures for industrial data science
data lakes, lakehouses, time-series databases, and stream processing frameworks.
Scalablearchitectures
24
Edge computing and embedded analytics
deploying lightweight models on PLCs, edge devices, and gateways for real-time decision-making.
computingembedded
25
Simulation and digital twins for data-driven decisions
hybrid modeling, physics-informed machine learning, and what-if analysis.
Simulationdata-driven
26
Optimization and prescriptive analytics
linear programming, mixed-integer programming, and reinforcement learning for scheduling and control.
Optimizationprescriptive
27
Industrial case study
steel manufacturing – predicting mechanical properties and optimizing rolling parameters from raw process data.
Industrialmanufacturing
28
Industrial case study
oil and gas – anomaly detection and production forecasting from wellhead sensor data.
Industrialdetection
29
Industrial case study
pharmaceutical manufacturing – batch process monitoring and quality assurance with multivariate statistical methods.
Industrialpharmaceutical
30
Capstone project
designing an end-to-end industrial data science solution from raw data to actionable decisions, including problem framing, data pipeline, model development, and deployment plan.
Capstonedesigning