Turn data into insights.

Analyze data using SQL, Excel, Power BI, and Tableau. Learn to design dashboards and interpret metrics that matter.
Perfect for aspiring data analysts and BI professionals.

Predict the future with data.

Explore statistics, machine learning models, and Python-based libraries like Scikit-learn and Pandas.
Focus on applied problem-solving with real datasets.

Ensure compliance and data trust.

Learn about metadata management, data lineage, access control, and regulatory frameworks.
Crucial for enterprise-level data teams.

Maintain data integrity and security.

Explore data profiling, validation rules, anomaly detection, and security strategies.
Learn how to detect, prevent, and manage data issues.

Leverage cloud-native data architectures.

Learn to build scalable pipelines using AWS S3, Glue, Redshift, and EMR.
Ideal for modern data engineers aiming to master AWS-based workflows.

Smart analytics with Google Cloud.

Work with BigQuery, Dataflow, Cloud Composer and other tools to power cloud-native data processing.
Designed for scalable data transformation and analysis.

Enterprise-grade data with Microsoft Azure.

Build pipelines using Azure Data Factory, Synapse, and ADLS.
Suited for teams working on Microsoft cloud ecosystems.

Master orchestration with Apache Airflow.

Learn how to build, monitor, and manage DAGs, define scheduling logic, and ensure robust data workflows.
A must-have for pipeline automation.

Enterprise-grade workflow management.

Use Control-M to schedule, monitor, and automate critical data processes.
Best for enterprise teams requiring visibility and SLA tracking.

Automate your data deployments.

Learn CI/CD pipelines using tools like TeamCity and Octopus Deploy.
Build and release reliable data code at scale with minimal manual intervention.

Collaborate with confidence.

Use Git & GitHub to manage source code, branches, pull requests, and version control for ETL and scripting workflows.
A foundational skill for all data professionals.

End-to-end project lifecycle for data.

Understand every stage — from requirements to deployment & support.
Gain real-world insights into how data projects are designed, built, tested, and maintained.

Deliver smarter, faster with Agile.

Apply Agile & Scrum methods in data-driven environments.
Learn sprint planning, backlog grooming, retrospectives, and team collaboration frameworks.

Prepare data solutions for production.

Validate, package, and deploy data code with best practices for sustainability and rollback safety.
Great for building release-ready pipelines.