Level: Beginner to Intermediate
Prerequisite: None
In today’s data-driven world, organizations are eager to leverage analytics and artificial intelligence (AI) to gain a competitive edge. The success of these initiatives hinges on one critical factor: high-quality data. Yet despite its importance, most organizations face significant challenges in acquiring, preparing, and maintaining data for analytics and AI. In fact, more than 80% of the time, effort, and cost in analytics and AI projects is typically spent on data engineering and data enablement.
This course provides participants with the essential data engineering strategies and techniques needed to source, structure, and manage quality data for successful analytics and AI outcomes.
You Will Learn
- Four different types of business data and the 12 key dimensions of data quality
- Foundational concepts in the data lifecycle, data engineering workflows, and data transformation for analytics and AI
- Essential components of data engineering, including data integration, data wrangling, and data enrichment
- Practical data wrangling techniques such as data imputation, numerical analysis, data type conversion, and sampling
- Advanced data enrichment methods, including feature engineering, synthetic data generation, SPD/TPD integration, and survey data analysis
- The critical role of data governance, including data validation, profiling, ethical considerations, and risk management
Geared To
This course is ideal for professionals who work with data or are involved in analytics and AI initiatives, including:
- Data analysts and scientists looking to strengthen their data engineering foundation for more effective modeling and analysis
- Business intelligence professionals aiming to improve the quality and reliability of the data behind dashboards and reports
- Data engineers who want to expand their skill set with practical techniques for data wrangling, enrichment, and governance
- IT and data/solutions architects responsible for designing systems that support high-quality data pipelines and analytics infrastructure
- AI/ML practitioners who need robust, well-structured data to build reliable and scalable models
- Project managers and product owners overseeing analytics and AI projects and looking to understand the challenges and resource needs around data preparation
- Anyone transitioning into a data-related role who wants a practical, end-to-end understanding of how quality data enables analytics and AI