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.
Transform JSON & Fix Time Data
Transform JSON & Fix Time Data
This course is part of Data Pipeline Engineering & Analytics Specialization
Instructor: Hurix Digital
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Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
JSON transformation needs structured methods to manage nested data while preserving integrity and scalability.
Time-based data issues arise from timezone errors that can be found and fixed using pattern checks.
Strong data quality relies on proactive transformations that prevent downstream analytics errors.
Data wrangling blends scripting skills and analytical thinking to fix structural data issues.
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Recently updated!
February 2026
Taught in English
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This course is part of the Data Pipeline Engineering & Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
Learners will master systematic approaches to transform complex nested JSON structures into pandas DataFrames, enabling reliable data preprocessing for analytics pipelines.
What's included
3 videos1 reading1 assignment
Learners will develop systematic approaches to identify, diagnose, and correct timezone-related data quality issues that fragment user sessions and compromise temporal analytics.
What's included
2 videos1 reading3 assignments1 ungraded lab
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