Analytics Engineering is the foundation of Data Science and artificial intelligence. This approach represents a dynamic blend of data engineering and analytics, acting as a bridge between these two fields. Analytics engineers are responsible for a significant portion of the data lifecycle: from loading data sources and building data warehouses with corresponding pipelines to integration with business intelligence tools.
If you are an analyst or data scientist looking to master modern Data Engineering tools, or a beginner in the field of Analytics Engineering, this practical course is for you.
Course Objectives
By the end of this course, participants will have a thorough understanding of contemporary analytics engineering tools and techniques essential for transforming raw data into valuable insights.
What You Will Learn
- Create and develop a modern data warehouse using Snowflake.
- Automatically load data from multiple sources using connectors in Fivetran.
- Clean and transform data, mastering the basics of ELT (Extract, Load, Transform) using DBT and SQL.
- Configure and connect the business intelligence tool (Preset) to the data warehouse for effective data visualization and analysis.
Key Outcomes
- Build a full-fledged Data Engineering product - from handling "raw" data to creating insightful visualizations.
- Enhance your portfolio with a practical project that showcases the in-demand skills you’ll acquire, making you a competitive candidate in the market.
Target Audience
This course is tailored for:
- Data analysts eager to delve into data engineering tools and practices.
- Data scientists wanting to refine their analytics engineering skills.
- Beginners in the field of analytics engineering seeking a comprehensive introduction.
Prerequisites
Some prior experience with data analysis and SQL is recommended, but not required, to make the most of this course.
