Enterprise Data Wrangling with SQL and Python

Modern enterprise decisions rely on clean, well-structured data, but raw business data is often messy, fragmented, and stored across disconnected databases. This text-only course teaches you how to bridge the gap using SQL and Python to extract, clean, and prepare data for analysis. You will transition from manual data cleaning to building robust, automated data preparation workflows. By combining the querying power of SQL with the programming flexibility of Python, you will learn to tackle real-world data challenges, ensure data quality, and handle enterprise-scale datasets with confidence. What you'll learn: - Understand foundational data wrangling concepts, enterprise database structures, and data quality principles. - Extract and filter complex datasets from relational databases using essential SQL queries. - Clean and transform messy data using Python and modern dataframe libraries. - Merge and integrate disparate data sources to build unified datasets for business intelligence. - Validate data integrity and handle missing values, duplicates, and inconsistent formatting. - Apply structured data preparation workflows to prepare raw data for downstream analytics. The course begins with core terminology and the fundamentals of database querying, then guides you through reading and writing Python scripts to manipulate data tables. You will progress through practical, text-based explanations and written exercises that simulate real enterprise scenarios. Designed for aspiring data analysts, business intelligence professionals, and beginners looking to build essential data preparation skills, this course requires no prior coding or database experience. Start your journey toward mastering enterprise data preparation today.