Introduction to Data Engineering and Analytics

• Key roles and data lifecycle basics.

Programming for Data

• Python basics: variables, loops, and libraries like Pandas.

• SQL basics: SELECT, INSERT, simple queries.

Data Management and Storage

• Basics of databases: RDBMS overview, structured/unstructured data.

Data Pipelines and ETL

• ETL basics and introduction to pipeline tools (e.g., Apache Airflow).

• Real-time vs. batch processing basics.

Relational and NoSQL Databases

• Advanced SQL: joins, subqueries.

• NoSQL basics: MongoDB introduction.

Data Visualization

• Creating basic visualizations with Tableau/Power BI.

Big Data and Distributed Systems

• Introduction to Hadoop and Apache Spark.

• Writing Spark jobs in PySpark.

Cloud Data Engineering

• Building pipelines using AWS (S3, Redshift), GCP (BigQuery).

• Real-world data pipelines in cloud environments.

Advanced Data Analytics

• Using machine learning for predictive analytics.

• Advanced data visualisations and dashboards

End-to-End Projects

• Build and deploy an end-to-end data pipeline (on-premise to cloud).

• Design and present a capstone project integrating data engineering and analytics.