Covered end-to-end data engineering including workflow orchestration (Mage), data warehousing (BigQuery), batch processing (Spark), streaming (Kafka), infrastructure as code (Terraform), and analytics engineering. Built scalable data pipelines for reliable transformation, storage, and access. 

Demonstrated expertise in SQL for complex data extraction, Excel for advanced modeling, and Power BI for creating interactive, insight-driven dashboards. 

Practiced writing SQL queries to retrieve, filter, aggregate, and join data, while learning how databases are designed, normalized, and optimized for reliable data storage and access. 

Strengthened professional skills essential for technical careers, including problem-solving, effective communication, time management, collaboration, and ethical work practices. 

Built a solid foundation in machine learning concepts, focusing on how data is transformed into actionable models. Covered supervised and unsupervised learning, basic model evaluation, and the practical mindset behind feature selection and prediction rather than just theory.