AI and machine learning · Free online course
Industrial Data with Python
The plumbing nobody teaches: values out of PLCs and meters over Modbus and OPC UA from Python, published over MQTT, stored as time series, cleaned and gridded, forecast and scored for anomalies, and wired to a dashboard — every stage built against a simulator on your own laptop, so no plant is needed and nothing you break matters.
- 12 modules
- 10h 21m of video
- Intermediate
- English
- ₹0, free
₹0Free for good
Worth ₹5,999 by length and depth against classroom training. It has always been free; that is not a former price.
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Overview
About Industrial Data with Python
The plumbing nobody teaches: values out of PLCs and meters over Modbus and OPC UA from Python, published over MQTT, stored as time series, cleaned and gridded, forecast and scored for anomalies, and wired to a dashboard — every stage built against a simulator on your own laptop, so no plant is needed and nothing you break matters.
Industrial Data with Python is a free, self-paced online course from EDWartens for engineers, developers and students applying AI to real work. It has 12 modules and 10h 21m of video lessons by Fusion Automate, Rocket Systems, APMonitor.com and others, with written notes and worked problems, a practical project with a document pack and a 15-question final assessment (pass mark 60%). Learning is free with an account; an optional certificate with a public verification code is issued when you pass. Last updated 27 September 2026.
Who it is for: Engineers, developers and students applying AI to real work
This course includes
- 10h 21m of video lessons
- 12 modules with written notes and diagrams
- One final assessment: 15 questions, pass mark 60%
- Ask-an-engineer support from practising engineers
- Lifetime access, the course does not expire
- Optional verifiable certificate when you finish
- Step 1 of 3
Learn the 12 modules
10h 21m of video lessons, written notes and a practice task per module, at your own pace.
- Step 2 of 3
Pass the final assessment
15 questions drawn from every module, pass mark 60%, 3 attempts.
- Step 3 of 3
Take the certificate, if you want it
Optional, paid once, only if you choose it. The course stays free either way.
See the certificate and its price
A look inside
From the lessons
Where plant data lives, and where Python fits
by APMonitor.com
Modbus TCP from Python
by Fusion Automate
Modbus RTU and serial devices
by Fusion Automate
OPC UA from Python
by Fusion Automate
OPC UA security, and a Pi as a server
by Fusion Automate
MQTT: publish once, subscribe anywhere
by IT and Automation Academy
Lesson frames belong to the creators named in the Credits and are shown from YouTube.
What you learn
What you will be able to do
Read Modbus TCP and RTU devices and OPC UA servers from Python and run simulators of both; subscribe rather than poll; publish on well-designed MQTT topics with quality and timestamps; store in SQLite or InfluxDB with Grafana; clean and grid live process data honestly; forecast day-ahead load and beat the seasonal-naive baseline; detect anomaly episodes on process tags; and assemble the whole pipeline with Node-RED and a model served over HTTP, run as services with security and documentation.
- Read holding registers over Modbus TCP and RTU from Python and decode floats, offsets and scaling correctly
- Run an OPC UA server and client in Python, subscribe to changes and log value, quality and source timestamp
- Design MQTT topics and payloads and bridge OPC UA to a broker with retain and last will
- Store time series in SQLite or InfluxDB, downsample honestly and dashboard with Grafana
- Clean live process data: quality masks, grids, frozen sensors, coverage, joins by time
- Forecast day-ahead load against a seasonal-naive baseline and detect anomaly episodes on process tags
Syllabus
Course content, module by module
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.
- Modules
- 12
- Video lessons
- 34
- Of video
- 10h 21m
- Final questions
- 15
- 01
Where plant data lives, and where Python fits
2 lessons · 23m - 02
Modbus TCP from Python
4 lessons · 53m - 03
Modbus RTU and serial devices
3 lessons · 33m - 04
OPC UA from Python
7 lessons · 1h 4m - 05
OPC UA security, and a Pi as a server
4 lessons · 48m - 06
MQTT: publish once, subscribe anywhere
3 lessons · 21m - 07
Storing time series
3 lessons · 1h 50m - 08
Cleaning and resampling process data
1 lesson · 58m Final assessment · 15 questions, pass mark 60%
Project
The course project
Optional practice that ends in the kind of job the course prepares you for, with the documents that go with it.
About 8 hours
A simulated PLC to a dashboard: OPC UA → MQTT → store → clean → anomaly episodes
Run an OPC UA server in Python that simulates a compressor with an injected fault, subscribe to it, publish over MQTT, log to SQLite, clean and grid the data, detect anomaly episodes with a baseline and persistence, publish the episodes back, and show it all on a Node-RED or Grafana dashboard — the whole pipeline on one laptop.
Sample document pack, 8 documents, filled in for the scenario
- PlanPlan: bench proof of the compressor C3 data pipeline
- Tag listTag and topic list: C3 simulator and pipeline
- Network architectureArchitecture: bench pipeline and its phase-2 equivalent
- SDSSoftware design: simulator, bridge, logger and detector
- FATBench acceptance test record: C3 pipeline
- Change noticeChange notice: simulator cycles and detector after run 1
- ReportResults report: run 2, 05-Oct-2026
- ProcedureProcedure: start, check and stop the bench pipeline
Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.
About 5 hours
Day-ahead compressor-house load forecast that beats seasonal naive
A year of 15-minute kW data. Clean it, build hourly lag and calendar features that respect a 24-hour horizon, train gradient boosting, and compare it honestly with the seasonal-naive baseline on the last three months — MAE in kW, residual plot, and a recommendation for the day-ahead power purchase.
Sample document pack, 8 documents, filled in for the scenario
- PlanProject plan: day-ahead compressor-house load forecast
- URSRequirements: day-ahead forecast
- RegisterData-quality log: meter export 2025-26
- RegisterFeature register with horizon check
- ProcedureProcedure: build, test and issue the day-ahead forecast
- Test reportTest report: forecast evaluation on July-August 2026
- Change noticeChange notice: add the site shutdown calendar as a feature
- ReportRecommendation to the energy manager
Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.
Tools
Software, hardware and lessons
Software
Python on your own laptop (not only Colab): pymodbus, minimalmodbus, asyncua, paho-mqtt, Mosquitto, SQLite, optionally InfluxDB, Grafana and Node-RED. All free.
Hardware
None. A USB–RS485 adapter and any Modbus meter make the serial module real; a Raspberry Pi makes the OPC UA server a device.
Who it is for
Intermediate. Needs the Python for AI course; the Machine Learning course helps for the forecasting and anomaly modules. Any PLC or instrumentation background makes the protocols familiar.
Video lessons by
- Fusion Automate
- Rocket Systems
- APMonitor.com
- RealPars
- Chipsee
- Johannes 4GNU_Linux
- InfluxData
- Masters Of The Code
- IT and Automation Academy
- Dave Ebbelaar
- Rob Mulla
- freeCodeCamp.org
- Lianne and Justin
- AIEngineering
- NeuralNine
Independent creators, credited in full under Credits.
Software you need
What to download, where from, what it costs and how to install it. Every link goes to the maker's own site, never a mirror.
Run Python on your own laptop, not only in Colab: the course talks to real ports. minimalmodbus, asyncua and paho-mqtt install with pip the same way as pymodbus. SQLite comes with Python.
Required
- 01Free
Python
Python Software Foundation
- Runs on
- Windows (not Windows 7 or earlier), macOS and Linux. Windows builds for x64, 32-bit and Arm64.
- Account
- None needed
Python is free, open source software. You can use it for learning and for commercial work at no cost.
Steps
- 1.Open python.org/downloads and click the download button for your system.
- 2.On Windows, run the Python install manager (or the classic 64-bit installer) you downloaded.
- 3.If you use the classic installer, tick "Add python.exe to PATH" on the first screen, then click Install Now.
- 4.Open a new Command Prompt or Terminal and run: python --version (on Windows you can also run: py --version).
- 5.Install packages with pip, for example: python -m pip install requests
- pip comes with Python. Run it as python -m pip so it always matches the Python you are using.
- Python.org now recommends the Python install manager on Windows. If it offers to add its folder to PATH, say yes so the python command works everywhere.
- Make a virtual environment for each project: python -m venv .venv
Official download pagepython.orgAlternatives
- Anaconda Distribution: Python with 600+ data science packages and Jupyter already included.
- 02Free
PyModbus
pymodbus-dev (open source project), a Python or npm package
- Runs on
- Windows, macOS and Linux with Python 3.10 or later
- Account
- None needed
Free, open source under the BSD 3-Clause licence.
Steps
- 1.Install Python 3.10 or later and create a virtual environment.
- 2.Run: pip install "pymodbus==3.8.6" (the version the course code is written and tested for; 3.10 renamed slave= to device_id=)
- 3.For serial (RS-485) devices, run: pip install "pymodbus[serial]==3.8.6"
- 4.Follow the client examples in the documentation to read holding registers from a device or the built-in simulator.
- PyModbus includes a server simulator with a web interface, so you can practise without real hardware.
- Only write registers on lab equipment. Writes can move real machines.
Official download pagegithub.com - 03Free
Eclipse Mosquitto
Eclipse Foundation
- Runs on
- Windows (64-bit and 32-bit installers), macOS (Homebrew), Linux (Ubuntu PPA, Debian, Raspberry Pi, Snap)
- Account
- None needed
Free, open source under the Eclipse Public License and Eclipse Distribution License.
Steps
- 1.Open mosquitto.org/download.
- 2.On Windows, download the 64-bit installer and run it. On macOS run: brew install mosquitto. On Ubuntu run: sudo apt-add-repository ppa:mosquitto-dev/mosquitto-ppa, then sudo apt-get update and sudo apt-get install mosquitto mosquitto-clients.
- 3.Start the broker by running: mosquitto -v
- 4.In a second terminal, subscribe with: mosquitto_sub -t test, and in a third publish with: mosquitto_pub -t test -m hello
- By default Mosquitto 2 only accepts connections from the same computer. To allow other devices, add a listener and authentication settings to mosquitto.conf.
- Use a lab network only. Do not expose an open broker to the internet.
Official download pagemosquitto.org
Optional
Useful, not needed to finish the course.
- 04Free
InfluxDB (open source)
InfluxData
- Runs on
- InfluxDB 3 Core: Linux, macOS, Docker. InfluxDB OSS 2.x: Windows, macOS, Linux, Docker, Raspberry Pi (64-bit).
- Account
- None needed
The open source editions are free. InfluxDB 3 Core is under the MIT and Apache 2 licences and is what InfluxData recommends for new users. InfluxDB OSS 2.x is still offered and includes a web UI.
Steps
- 1.Open influxdata.com/downloads and choose InfluxDB 3 Core or InfluxDB OSS 2.x (use the one your course names).
- 2.Pick your platform and follow the install commands shown (Docker is simplest).
- 3.For OSS 2.x, start the server with: influxd
- 4.Open http://localhost:8086 and set up your first user, organisation and bucket, then save the API token it shows.
- The API token is shown once. Store it safely.
- Grafana can read from InfluxDB for dashboards.
Official download pageinfluxdata.com - 05Free
Grafana
Grafana Labs
- Runs on
- Windows, macOS, Linux (Debian/Ubuntu, RHEL/Fedora, SUSE, ARM64) and Docker
- Account
- None needed
Grafana OSS is free and open source under AGPLv3. The Grafana Enterprise download is also free to use without a licence key and is functionally identical until you buy an Enterprise licence.
Steps
- 1.Open grafana.com/grafana/download and choose the edition (Enterprise is recommended by Grafana and is free to use; OSS is fine too).
- 2.Pick your platform and download the installer or package.
- 3.Install and start the Grafana service.
- 4.Open http://localhost:3000 and sign in with the default admin account (admin / admin), then set a new password.
- 5.Add a data source, such as InfluxDB, and build your first dashboard.
- Change the default admin password straight away.
Official download pagegrafana.com - 06Free
Node-RED
OpenJS Foundation (Node-RED project)
- Runs on
- Windows, macOS and Linux (needs Node.js 22 or later; Node.js 24 recommended), or Docker
- Account
- None needed
Free, open source under the Apache 2.0 licence.
Steps
- 1.Install Node.js LTS from nodejs.org.
- 2.On Windows run: npm install -g node-red (on macOS or Linux run: sudo npm install -g node-red).
- 3.Start it by running: node-red
- 4.Open http://localhost:1880 in your browser to use the editor.
- With Docker, run: docker run -it -p 1880:1880 --name mynodered nodered/node-red
- Leave the terminal window open while you use Node-RED.
Official download pagenodered.org - 07Free
Visual Studio Code
Microsoft
- Runs on
- Windows 64-bit (supported Windows client versions), macOS (latest and two previous releases), Linux (Ubuntu 20.04, Debian 10, RHEL 8, Fedora 36 or later)
- Account
- None needed
- Size
- Less than 200 MB download, under 500 MB installed
Free to download and use. Extensions from the Marketplace each have their own licence.
Steps
- 1.Open code.visualstudio.com/download.
- 2.Pick the installer for your system (on Windows, the User Installer is fine).
- 3.Run the installer. On Windows, tick "Add to PATH" if it is offered.
- 4.Open VS Code and install the extensions your course uses, for example the Python extension.
- Hardware needs are small: a 1.6 GHz processor and 1 GB of RAM.
- On Windows, the "Open with Code" options in the installer let you open folders from File Explorer.
Official download pagecode.visualstudio.com
Checked against each maker's own page on 27 September 2026. Trial lengths and editions change; the maker's page is the final word.
What you walk away with
Your certificate for Industrial Data with Python
Finish the course, pass the final, and this is the document with your name on it.
Verifiable by anyone
Adds to LinkedIn in one click
QR code on the certificate
Names what you can do
A permanent link
Earned, not attended
Learning is free. The certificate is optional.
Add it now and pay only when you have finished the course, or come back for it later. One-off, US$28.99, with a receipt.
Issued by Wartens Ltd (England and Wales, company no. 15262249) through the EDWartens training portal, as a Certificate of Completion for this self-paced course. Sold in India by Wartens Automation Private Limited. It is not a vendor certification, a university award or a CPD-accredited activity, and it does not certify competence on live equipment. Delivered electronically; see the refund policy.
FAQ
Common questions
Do I need a PLC or a meter?
No. Every module runs against a simulator you write in Python — a Modbus slave, an OPC UA server, a local MQTT broker. A USB–RS485 adapter and any meter make the serial module real if you have them.
Why not just use Node-RED?
Node-RED is excellent glue and has its own free course here. This course is for the parts Node-RED does not do well — decoding, cleaning, storing at scale, forecasting, anomaly detection — and for understanding what the glue is gluing. The last module wires the two together.
What are the projects in the Industrial Data with Python course?
A complete bench pipeline: an OPC UA simulator with an injected fault, a subscribing client publishing to MQTT, a logger to SQLite, cleaning and gridding, an anomaly detector with a baseline and persistence, episodes published back and a dashboard — with the detection lag measured against the injection time. And a day-ahead load forecast on a year of compressor data that must beat the seasonal-naive baseline, with the improvement converted to money saved.
Is the Industrial Data with Python course really free?
Yes. Every module, practice task, project and assessment. You create an account so your progress is saved and the assessments can be marked. The certificate is the only paid item, and only if you want it.
What certificate does the Industrial Data with Python course give?
An EDWartens Certificate of Completion, issued when you have finished the modules and passed the final assessment (the project is optional practice), with a verification code anyone can check. It is not a vendor credential and is never described as one.
Who made the video lessons in the Industrial Data with Python course?
The creators named in the Credits block at the foot of this page, on their own YouTube channels. EDWartens did not make the videos and the creators are not affiliated with EDWartens. What EDWartens wrote is the study plan, the notes, the practice tasks, the projects and the assessments.
How long does the Industrial Data with Python course take?
Plan on about 16 hours in all, including the video lessons, notes and practice tasks. It is self-paced, so you work through the twelve modules at your own speed.
Is the Industrial Data with Python course free in India, and what does the certificate cost?
Yes. Learning costs ₹0 in India: every module, the written notes, the practice tasks and the final assessment, with no card and no trial period. The only paid item is the optional EDWartens Certificate of Completion, ₹599 including GST for this intermediate course, paid in rupees through Razorpay, and only if you want it after passing the final assessment.
All course factsHide course facts
- Price
- ₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹5,999.
- Who it is for
- Engineers, developers and students applying AI to real work
- Format
- 12 self-paced modules, 10h 21m of video, written notes, a practice task per module and one final assessment.
- Level
- Intermediate. Intermediate. Needs the Python for AI course; the Machine Learning course helps for the forecasting and anomaly modules. Any PLC or instrumentation background makes the protocols familiar.
- Brand
- Vendor-neutral
- Software
- Python on your own laptop (not only Colab): pymodbus, minimalmodbus, asyncua, paho-mqtt, Mosquitto, SQLite, optionally InfluxDB, Grafana and Node-RED. All free.
- Hardware
- None. A USB–RS485 adapter and any Modbus meter make the serial module real; a Raspberry Pi makes the OPC UA server a device.
- Certificate
- Optional EDWartens Certificate of Completion, ₹599 inclusive of GST for learners in India, verifiable by code. Not a vendor credential.
- Video lessons by
- Fusion Automate, Rocket Systems, APMonitor.com, RealPars, Chipsee, Johannes 4GNU_Linux, InfluxData, Masters Of The Code, IT and Automation Academy, Dave Ebbelaar, Rob Mulla, freeCodeCamp.org, Lianne and Justin, AIEngineering, NeuralNine (independent creators, credited below)
- Language
- English
- Last updated
- 27 September 2026
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Credits
Who made the video lessons
The video lessons in this course were created by the people below, not by EDWartens. Every lesson streams from its creator's own YouTube channel; EDWartens neither hosts nor sells that footage, and the creators are not affiliated with EDWartens and do not endorse this course. What EDWartens wrote is the study plan, the notes, the practice tasks and the assessments.
- Fusion Automatethe OPC UA and Python series, the Modbus TCP and RTU tutorials and the OPC UA to MQTT converters
- Rocket Systemsthe OPC UA series with Raspberry Pi, security and a dashboard
- APMonitor.comdata transfer with Modbus, OPC and SQL
- RealParsthe OPC UA and Node-RED explainers
- Chipseereading Modbus data with Python
- Johannes 4GNU_Linuxa Modbus TCP server with pyModbusTCP
- InfluxDatatime-series basics and querying InfluxDB with Grafana
- Masters Of The CodeInfluxDB 2, Grafana and Python monitoring
- IT and Automation Academythe MQTT explainer
- Dave Ebbelaardetecting outliers in sensor data
- Rob Mullatime-series forecasting with XGBoost
- freeCodeCamp.orgtime-series forecasting in Python
- Lianne and JustinARIMA models in Python
- AIEngineeringanomaly detection with Isolation Forest on time series
- NeuralNineanomaly detection for time-series data
If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.