AI-Enhanced Data Preparation
About This Course
This module covers data preparation for analytics, combining established data-handling techniques with generative AI assistance. Students learn to extract, cleanse, transform, enrich, and impute data using ETL processes, supported by a working understanding of relational databases, data modelling, SQL querying, and data warehouse architecture. Generative AI is introduced throughout as an assistant, generating SQL from natural language, recommending schema designs, detecting data quality issues, and automating metadata extraction. Students also work with a visual data pipeline platform that incorporates generative AI within the workflow, giving them hands-on experience with AI-assisted data preparation. The module also addresses data governance, data ethics, and data security principles as they apply to analytics practice.
What You'll Learn
• Understand relational databases and data warehouse architecture, and write SQL queries to retrieve and prepare data for analytics
• Build data preparation workflows using ETL processes including collection, cleansing, transformation, enrichment, and imputation
• Apply generative AI for SQL generation from natural language, data quality detection, and metadata extraction
• Work with visual data pipeline platforms that incorporate generative AI within the workflow
• Apply data governance, data ethics, and data security principles
Entry Requirements
Applicants should possess a relevant (Business, Engineering, or Technology) local Polytechnic Diploma, ITE Technical Diploma / Technical Engineer Diploma / Work-Study Technical Diploma or Degree.
Applicants who do not meet the entry requirements may be considered for admission based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may be required to go through an interview and/or entrance test.