Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
Describe your need, get a tailored program and a pre-filled quote in 3 min. A 100% free call with an advisor.
Very demanding course · Expertise required
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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.
Program
Dive into in-depth analysis of IoT data streams from sensors via MQTT, using tools like Great Expectations and Pandas to profile massive datasets, detect abnormal patterns with advanced ML algorithms, perform practical exercises on real cases of corrupted LoRa data, produce detailed quality reports as immediate deliverables to assess your enterprise's data maturity.
Build specialized IoT ETL pipelines with Apache NiFi and Spark to clean sensor data from LoRa networks, apply complex validation rules to MQTT metadata, test live on simulations of faulty sensors, integrate custom Python scripts for automatic enrichment, generate interactive dashboards with Grafana as concrete deliverables, strengthening the robustness of your professional connected systems.
Define comprehensive IoT data governance with Collibra adapted for MQTT and LoRa streams, implement advanced KPIs like completeness, accuracy, and freshness of sensor data, analyze case studies from leading IoT enterprises, develop custom quality scoring models via TensorFlow, export compliance certificates as deliverables, optimizing real-time decision-making processes for your professional projects.
Deploy streaming data quality monitoring systems with Kafka and Prometheus for IoT sensors, configure automated alerts for MQTT/LoRa degradations, simulate massive loads to test scalability, integrate AI for predicting future anomalies, practice on AWS IoT Core cloud environments, produce rapid intervention playbooks as actionable deliverables, ensuring high availability in enterprise production.
Integrate all learned techniques into a complete IoT capstone project involving MQTT, LoRa, and real sensors, audit end-to-end quality with tools like Monte Carlo, prepare an internal professional data quality competency certification, present your solution to an expert jury, receive a deliverable portfolio with source code and reports, validating your expertise for critical enterprise IoT deployments.
The Data Quality IoT Training - Optimizing the Reliability of Connected Data program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the Data Quality IoT Training - Optimizing the Reliability of Connected Data program, the following equipment is required:
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
Skills assessment methods
Assessment of skills acquired during the Data Quality IoT Training - Optimizing the Reliability of Connected Data program is performed through:
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

A school kit donated to a child for every training booked
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
30 minutes with a learning advisor. No commitment. No sales pitch dressed up as a demo.
IoT & embedded systemsIndustry-certified
IoT & embedded systemsIndustry-certified
IoT & embedded systemsIndustry-certified
Data governance & qualityIndustry-certified
Data governance & qualityIndustry-certified
Data governance & qualityIndustry-certified
IoT & embedded systemsIndustry-certified
IoT & embedded systemsIndustry-certified
IoT & embedded systemsIndustry-certified
Automation & workflowsIndustry-certified
Automation & workflowsIndustry-certified
Automation & workflowsIndustry-certified
Google CloudIndustry-certified
Google CloudIndustry-certified
Google CloudIndustry-certifiedAvailable on-site and remotely. Pick your city to see the local training center.
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