Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Data Quality - Ensuring Data Reliability in the Enterprise training are: Advanced mastery of SQL, Python or R for data processing, and basics in data warehousing. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Data Quality - Ensuring Data Reliability in the Enterprise training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Data Quality - Ensuring Data Reliabilit... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Data Quality - Ensuring Data Reliability in the Enterprise training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Data Quality - Ensuring Data Reliability in the Enterprise training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Data Quality - Ensuring Data Reliability in the Enterprise training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master advanced data quality techniques for reliable professional pipelines

Develop automated profiling and cleaning strategies in the enterprise

Implement scalable, certifiable data quality validation rules

Design real-time data quality monitoring systems

Optimize data governance to minimize business risks

Deploy open-source data quality tools in production environments

Training program

Module 1Data Quality Profiling: Evaluate and Diagnose Datasets (Great Expectations, Deequ, SQL profilers)

Immediate immersion in in-depth data quality analysis via automated profiling, hands-on with tools like Great Expectations to detect anomalies, duplicates, and inconsistencies on real enterprise datasets, practical exercises on your own imported data, generation of detailed visual reports with comprehensive metrics, development of an actionable initial diagnosis to prioritize corrective actions.

Module 2Data Quality Cleaning: Standardize and Enrich Data (Python Pandas, Talend, custom scripts)

Practical application of advanced data quality cleaning techniques using Python with Pandas and specialized libraries like Dedupe, processing large datasets to correct formats, missing values, and outliers, implementation of automated standardization rules on various business cases, iterative tests with cross-validation, production of cleaned datasets ready for analysis, support to integrate these pipelines into your existing workflows.

Module 3Data Quality Governance: Implement Rules and Monitoring (Collibra, Soda, data observability)

Design of a robust data quality governance framework with definition of parametric business rules, deployment of real-time monitoring via Soda and automated alerts on degradations, practical workshops to catalog metadata and lineages, simulation of data incidents to test resilience, creation of personalized dashboards tracking quality KPIs, expert advice to align governance with GDPR and ISO 8000 standards in the enterprise.

Module 4Data Quality Deployment: Scale in Production (Airflow, Kubernetes, CI/CD data pipelines)

Finalization through advanced orchestration of end-to-end data quality pipelines with Apache Airflow and Kubernetes integration for scalability, automation of quality tests in CI/CD via GitHub Actions, exercises on AWS/GCP cloud deployment of monitoring solutions, performance optimization for large volumes, collaborative code review on the red thread project, delivery of a complete playbook for immediate production rollout with measurable ROI.