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Transform JSON & Fix Time Data

This course is part of Data Pipeline Engineering & Analytics Specialization

Instructor: Hurix Digital

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

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.

Module details

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

3 videosTotal 16 minutes

Why JSON Transformation Skills Matter in Data Analytics3 minutes
Understanding JSON Structure and Transformation Fundamentals7 minutes
Step-by-Step JSON Flattening with pandas json_normalize()6 minutes

1 readingTotal 8 minutes

JSON to DataFrame Transformation Patterns and Best Practices8 minutes

1 assignmentTotal 3 minutes

JSON Transformation Concepts and Implementation Knowledge Check3 minutes

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

2 videosTotal 14 minutes

Understanding Timezone Issues and Detection Strategies in Analytics Data9 minutes
Systematic Timezone Correction Workflow Using Python and Pandas5 minutes

1 readingTotal 10 minutes

Systematic Timezone Correction Techniques for Production Data10 minutes

3 assignmentsTotal 36 minutes

Diagnose and Correct Timezone Inconsistencies in Global User Analytics Dataset18 minutes
Timezone Data Quality Analysis and Correction Techniques Knowledge Check3 minutes
Comprehensive JSON and Timezone Data Transformation Mastery Assessment15 minutes