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Python Data Science Workflows for Industrial Operations

30 Chapters
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01
Industrial operations data landscape
sensor networks, SCADA historians, MES, ERP, and data acquisition workflows in Python.
Industrialoperations
02
Python environment setup for industrial data science
conda, pip, Jupyter, VS Code, and reproducible project structures.
environmentindustrial
03
Time-series data ingestion from industrial historians
connecting to OSIsoft PI, InfluxDB, and SQL-based historians with Python clients.
Time-seriesingestion
04
Data cleaning and imputation for industrial sensor streams
handling missing values, outliers, and irregular sampling intervals.
cleaningimputation
05
Exploratory data analysis for operational data
resampling, rolling statistics, and visualization with pandas and Plotly.
Exploratoryanalysis
06
Feature engineering for industrial processes
lag features, windowed aggregates, and domain-specific derived variables.
industrialprocesses
07
SQL and database integration in Python workflows
querying production databases, writing results, and managing connections.
SQLdatabase
08
Building reusable data pipelines with pandas and NumPy for batch processing of operational datasets.
Building reusable data pipelines with pandas and NumPy for batch processing of operational datasets.
Buildingreusable
09
Workflow orchestration for industrial data science
Apache Airflow, Prefect, and scheduling ETL jobs.
Workfloworchestration
10
Statistical process control in Python
control charts, Western Electric rules, and automated anomaly flagging.
StatisticalElectric
11
Anomaly detection in multivariate sensor data
isolation forests, autoencoders, and Mahalanobis distance.
detectionmultivariate
12
Predictive maintenance modeling
remaining useful life estimation with survival analysis and gradient boosting.
Predictivemaintenance
13
Classification of equipment failures
imbalanced datasets, SMOTE, and evaluation metrics for maintenance tasks.
Classificationequipment
14
Regression modeling for process optimization
predicting yield, energy consumption, and quality outcomes.
Regressionmodeling
15
Time-series forecasting for industrial demand and production
ARIMA, Prophet, and gradient-boosted trees.
Time-seriesforecasting
16
Deep learning for sensor data
1D CNNs, LSTMs, and temporal convolutional networks for fault detection.
CNNsLSTMs
17
Model validation and backtesting for industrial time-series
walk-forward validation and drift-aware evaluation.
validationbacktesting
18
Deploying models to production
packaging with joblib, REST APIs with FastAPI, and edge inference considerations.
Deployingproduction
19
Monitoring model performance in operations
data drift detection, alerting, and retraining triggers.
Monitoringperformance
20
Optimization and scheduling with Python
linear programming, constraint satisfaction, and production planning.
Optimizationscheduling
21
Digital twin workflows
simulation integration, surrogate modeling, and real-time state estimation.
workflowssimulation
22
Computer vision for industrial inspection
defect detection with OpenCV and PyTorch on production lines.
Computerindustrial
23
Natural language processing for maintenance logs
text classification, entity extraction, and topic modeling.
languageprocessing
24
Root cause analysis with causal inference
DoWhy, propensity scores, and counterfactual reasoning in operations.
analysisinference
25
Interactive dashboards for operations
Streamlit, Dash, and Grafana integration for real-time monitoring.
Interactivedashboards
26
Data quality and governance in industrial settings
validation rules, lineage tracking, and metadata management.
governanceindustrial
27
Scaling Python workflows
Dask, Ray, and Spark for large-scale industrial data processing.
workflowslarge-scale
28
Edge computing and IoT integration
deploying Python models on edge devices and MQTT data ingestion.
computingIoT
29
Case study
end-to-end predictive maintenance pipeline from sensor data to maintenance scheduling.
end-to-endpredictive
30
Capstone project
designing and implementing a complete Python data science workflow for an industrial operations problem.
Capstonedesigning