Master the critical data transformation skills that turn messy, real-world data into analysis-ready formats. This course tackles two of the most common yet challenging data quality issues facing analysts today: extracting structured data from complex JSON and fixing timezone inconsistencies that corrupt datasets.

This Short Course was created to help data analysis professionals accomplish reliable data preprocessing that enables accurate downstream analytics. By completing this course, you'll be able to confidently handle the data wrangling challenges that cause delays and errors in production analytics pipelines. You'll transform from struggling with nested data structures to efficiently processing complex JSON with pandas, and from manually detecting time discrepancies to systematically correcting timezone offsets that fragment user sessions. By the end of this course, you will be able to: Apply scripting techniques to flatten nested JSON data into relational columns. Analyze time-based data to correct inconsistencies arising from timezone offsets. This course is unique because it focuses on real production data quality issues using hands-on Python transformations that mirror actual workplace scenarios, not theoretical exercises. To be successful in this project, you should have a background in Python programming, basic pandas operations, and familiarity with data analysis workflows.