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Module 1
Professional Python for Data Engineering
1
Python Data Structures and Comprehensions
Work with core Python data structures and use comprehensions/generators for efficient processing.
2
Modular Code with Functions and Classes
Write modular and reusable code using functions and classes.
3
Handling Complex Data Formats with Pandas and PyArrow
Read and write complex data formats like CSV, JSON, and Parquet using libraries like Pandas and PyArrow.
4
Mastering Date and Time in Data Processing
Handle date and time complexities, including timezones, in data processing.
5
Building Resilient Data Pipelines
Implement robust error handling, exception management, and retry logic in data pipelines.
Decorators and Profiling for Performance Optimization
Use decorators for logging and performance monitoring, and apply profiling tools to identify bottlenecks.
7
Mastering Pip and Virtual Environments
Manage project dependencies and environments using pip and virtual environments.
8
Unit Testing Data Processing with pytest
Write and run unit tests for data processing logic using pytest.
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