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.

Complete curriculum

Every chapter and lesson

40 lessons total

01
Chapter 1 · 4 lessons
Python Basics and Control Flow
Use variables, expressions, conditionals, and loops to control program behavior with clear branch logic.
Why it matters
Control flow is the foundation of every script because it decides which operations run, when they run, and how many times.
By the end, you will be able to
- Write and evaluate simple Python expressions with correct types.
- Use if, elif, and else blocks to encode decision rules.
- Choose for and while loops based on the shape of the task.
1. 1.1
  Values, variables, and expressions
  Create variables and predict expression results.
2. 1.2
  Conditionals with if and elif
  Build decision trees that are complete and non-overlapping.
3. 1.3
  Looping with for and while
  Iterate through collections and state-driven conditions.
4. 1.4
  Guide: basics and control flow
  Apply fundamentals to classify and validate records.
Study chapter 1 in detail
02
Chapter 2 · 4 lessons
Functions and Modules
Split scripts into reusable functions and modules with explicit inputs, outputs, and import-safe entry points.
Why it matters
Well-scoped functions reduce bugs, and module boundaries make code easier to test, maintain, and reuse.
By the end, you will be able to
- Define functions with parameters and return values.
- Organize related logic into importable modules.
- Use __name__ == '__main__' to separate script execution from reusable code.
1. 2.1
  Function signatures and returns
  Design small functions that perform one clear task.
2. 2.2
  Scope and state
  Avoid hidden dependencies by passing data explicitly.
3. 2.3
  Module imports
  Import reusable functions without side effects.
4. 2.4
  Guide: functions and modules
  Refactor one long script into reusable files.
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03
Chapter 3 · 4 lessons
Data Structures
Model records with lists, dictionaries, tuples, and sets so code is readable and operations are efficient.
Why it matters
Choosing the right structure directly affects correctness, lookup speed, and how easy transformations are to reason about.
By the end, you will be able to
- Use lists for ordered sequences and iteration.
- Use dictionaries for key-based lookup and mapping.
- Use sets for uniqueness checks and membership testing.
1. 3.1
  Lists and tuples
  Pick mutable versus immutable sequence types correctly.
2. 3.2
  Dictionaries
  Represent row-like records and keyed indexes.
3. 3.3
  Sets
  Detect duplicates and run fast membership checks.
4. 3.4
  Guide: data structures
  Build a clean record model for analysis tasks.
Study chapter 3 in detail
04
Chapter 4 · 4 lessons
File Handling
Read and write text, CSV, and JSON files safely with context managers, encoding control, and schema checks.
Why it matters
Reliable file handling prevents data corruption, leaked file handles, and silent parsing errors in automation pipelines.
By the end, you will be able to
- Use with open(...) to manage file lifecycle safely.
- Parse CSV and JSON with standard library tools.
- Validate required fields before downstream processing.
1. 4.1
  Context managers
  Open files safely and close them automatically.
2. 4.2
  CSV workflows
  Read and write tabular files with predictable formatting.
3. 4.3
  JSON workflows
  Load and validate structured payloads.
4. 4.4
  Guide: file handling
  Produce clean derived artifacts from raw files.
Study chapter 4 in detail
05
Chapter 5 · 4 lessons
pandas Data Cleaning
Clean tabular datasets by fixing dtypes, handling missing values, normalizing labels, and removing true duplicates.
Why it matters
Aggregation and modeling are only as good as input quality, so cleaning rules must be explicit and auditable.
By the end, you will be able to
- Profile datasets with shape, dtypes, and missing-value checks.
- Convert dates and numeric fields to correct types.
- Apply duplicate and null-handling rules based on business meaning.
1. 5.1
  Inspect before editing
  Measure quality issues before transforming data.
2. 5.2
  Type conversion
  Standardize date and numeric columns for correct operations.
3. 5.3
  Nulls and duplicates
  Resolve missing and repeated records with explicit policy.
4. 5.4
  Guide: pandas data cleaning
  Apply a repeatable cleanup sequence to real tables.
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06
Chapter 6 · 4 lessons
pandas GroupBy and Aggregation
Build pandas GroupBy summaries with clear dimensions, named aggregations, multiple business metrics, and reconciliation checks against source totals.
Why it matters
GroupBy tables power reporting and decisions, so they must be both accurate and easy for others to verify.
By the end, you will be able to
- Group data by one or more dimensions with deterministic output.
- Compute counts, sums, and averages using named aggregations.
- Validate grouped totals against baseline totals.
1. 6.1
  Grouping patterns
  Choose dimensions that match the reporting question.
2. 6.2
  Named aggregations
  Produce stable, readable output column names.
3. 6.3
  Time bucket summaries
  Aggregate by week or month after date parsing.
4. 6.4
  Guide: pandas groupby
  Create report-ready KPI tables from transaction data.
Study chapter 6 in detail
07
Chapter 7 · 4 lessons
Visualization
Use Matplotlib with pandas outputs to build clear line and bar charts for trend and category comparison.
Why it matters
Clear plots help teams see performance changes quickly and reduce misinterpretation in KPI discussions.
By the end, you will be able to
- Choose chart types that match trend or comparison questions.
- Label axes, units, legends, and titles clearly.
- Export figures with reproducible size and layout settings.
1. 7.1
  Line charts for trends
  Plot ordered time-series values correctly.
2. 7.2
  Bar charts for categories
  Compare grouped values without visual distortion.
3. 7.3
  Chart annotation
  Add targets and notes that explain gaps and outliers.
4. 7.4
  Guide: data visualization
  Turn summary tables into report-ready visuals.
Study chapter 7 in detail
08
Chapter 8 · 4 lessons
API Data Fetching
Fetch external data with requests, enforce status and timeout checks, and validate JSON before transformations.
Why it matters
Network calls are failure-prone, so defensive API handling is required for dependable automation.
By the end, you will be able to
- Make GET requests with explicit params and timeout.
- Handle status errors and malformed payloads safely.
- Normalize API JSON into stable tabular records.
1. 8.1
  Request patterns
  Build safe HTTP calls with predictable behavior.
2. 8.2
  Response validation
  Verify status and schema before use.
3. 8.3
  JSON to table
  Flatten payloads into analysis-friendly records.
4. 8.4
  Guide: API data fetching
  Integrate third-party data into local reporting.
Study chapter 8 in detail
09
Chapter 9 · 4 lessons
Automation Scripts
Design repeatable scripts that validate inputs, run deterministic transforms, and produce reliable daily outputs.
Why it matters
Automation creates leverage only when reruns are safe and output quality is consistent over time.
By the end, you will be able to
- Structure scripts into staged steps with clear failures.
- Make reruns idempotent for the same inputs and date.
- Write operational logs that support debugging and audit.
1. 9.1
  Pipeline structure
  Separate ingest, validate, transform, and export phases.
2. 9.2
  Idempotent design
  Prevent duplicate side effects across reruns.
3. 9.3
  Run logging
  Capture counts, paths, and statuses for each phase.
4. 9.4
  Guide: automation scripts
  Implement a daily KPI job with safe rerun behavior.
Study chapter 9 in detail
10
Chapter 10 · 4 lessons
Testing and Debugging
Use pytest and targeted debugging to verify data transformations, reproduce bugs, and prevent regressions.
Why it matters
Tests protect production workflows by detecting breaks early and preserving bug fixes over time.
By the end, you will be able to
- Write unit tests for deterministic transformation functions.
- Use fixtures to share setup and keep tests readable.
- Convert production bugs into regression tests.
1. 10.1
  Core pytest workflow
  Create discoverable test files with readable assertions.
2. 10.2
  Fixtures and test data
  Reuse setup logic without hidden coupling.
3. 10.3
  Debugging failing tests
  Isolate root causes with minimal repro cases.
4. 10.4
  Guide: testing and debugging
  Harden script reliability through tests and diagnostics.
Study chapter 10 in detail

Python Data Automation

Course overview

Every chapter and lesson

Python Basics and Control Flow

Functions and Modules

Data Structures

File Handling

pandas Data Cleaning

pandas GroupBy and Aggregation

Visualization

API Data Fetching

Automation Scripts

Testing and Debugging

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