Python Data Automation

Course overview

This course follows a production-style workflow: write correct Python basics, structure code into modules, shape data with pandas, fetch external data safely, automate recurring tasks, and verify behavior with tests.

Syllabus

1. 01

   Python Basics and Control Flow

   Use variables, expressions, conditionals, and loops to control program behavior with clear branch logic.

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2. 02

   Functions and Modules

   Split scripts into reusable functions and modules with explicit inputs, outputs, and import-safe entry points.

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3. 03

   Data Structures

   Model records with lists, dictionaries, tuples, and sets so code is readable and operations are efficient.

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4. 04

   File Handling

   Read and write text, CSV, and JSON files safely with context managers, encoding control, and schema checks.

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5. 05

   pandas Data Cleaning

   Clean tabular datasets by fixing dtypes, handling missing values, normalizing labels, and removing true duplicates.

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6. 06

   pandas GroupBy and Aggregation

   Build pandas GroupBy summaries with clear dimensions, named aggregations, multiple business metrics, and reconciliation checks against source totals.

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7. 07

   Visualization

   Use Matplotlib with pandas outputs to build clear line and bar charts for trend and category comparison.

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8. 08

   API Data Fetching

   Fetch external data with requests, enforce status and timeout checks, and validate JSON before transformations.

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9. 09

   Automation Scripts

   Design repeatable scripts that validate inputs, run deterministic transforms, and produce reliable daily outputs.

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10. 10

    Testing and Debugging

    Use pytest and targeted debugging to verify data transformations, reproduce bugs, and prevent regressions.

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