Decent introduction. The structure was logical, but I wish there had been more hands-on practice beyond the basic examples.
Python Programming Foundations for Data Engineering
Learn the essential Python programming fundamentals and data structures needed to write clean, efficient code for data engineering pipelines.
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About this course
Data engineering relies heavily on Python, but you do not need to master every advanced software development concept to build effective data pipelines. This course focuses on the exact Python programming foundations necessary to manipulate, clean, and process data efficiently.
You will transition from writing basic scripts to developing structured, readable Python code tailored for data workflows. By working through conceptual explanations, practical code examples, and a hands-on capstone project, you will gain the confidence to handle data structures, manage files, and write reusable code.
What you'll learn:
- Understand core Python syntax, variables, and essential data types.
- Manipulate collections like lists, dictionaries, and tuples to organize pipeline data.
- Apply control flow, loops, and functions to automate repetitive data processing tasks.
- Implement modern Python best practices, including type hints and virtual environments.
- Read and write structured data formats like CSV and JSON using built-in libraries.
- Solve real-world data challenges and build a foundational data-processing capstone project.
The journey begins with basic programming concepts and syntax before moving into data manipulation and file operations. You will then apply these skills to solve structured coding challenges and complete a practical data engineering project.
This course is designed for aspiring data engineers, database administrators, and data analysts who are new to Python and want a focused pathway to build data pipelines. No prior programming experience is required.
Start building your Python foundation for data engineering today.
Course contents
6 modules · 30 lessons · 3h
1
Environment and Python Basics
Set up a professional workspace and master basic syntax.
- Python's Role in Data Engineering
Understand why Python is the industry standard for data pipelines.6 min - Isolating Projects with Virtual Environments
Learn to create and use venv to manage project dependencies.7 min - Storing Data in Variables
Learn how to name and assign values to variables following PEP 8.6 min - Numbers and Strings
Work with integers, floats, and string manipulation for data labels.6 min - Python Syntax and Indentation
Master the importance of whitespace and basic code structure.6 min - Type Hints for Data Integrity
Use type hints to make data code more predictable and readable.5 min
2
Organizing Data with Collections
Manage groups of data using Python's built-in structures.
- Lists: Ordered Data Sequences
Store and access sequences of data points using lists.6 min - Modifying Lists for Pipelines
Add, remove, and sort data within list structures.6 min - Dictionaries: Key-Value Mapping
Represent complex data records using dictionary keys.6 min - Navigating Nested Dictionaries
Access data stored deep within complex JSON-like structures.6 min - Tuples: Immutable Records
Use tuples for data that should not change during processing.5 min - Sets for Unique Data Values
Identify unique entries in a dataset using sets.5 min
3
Control Flow and Automation
Automate repetitive tasks and add logic to data processing.
- Conditional Logic with If-Else
Filter data based on specific conditions.6 min - For Loops: The Engine of Data
Iterate over collections to process every record.6 min - While Loops for Continuous Tasks
Run processes until a specific data condition is met.6 min - List Comprehensions
Write concise, efficient code to transform lists in one line.7 min - Handling Nested Loops
Process lists within lists, common in hierarchical data.6 min
4
Functions and Modular Code
Write reusable, clean, and error-resistant code.
- Defining Reusable Functions
Encapsulate data logic into functions to avoid repetition.6 min - Function Arguments and Returns
Pass data into functions and retrieve processed results.6 min - Understanding Variable Scope
Manage where variables are accessible to prevent pipeline bugs.5 min - Writing Readable Code
Apply best practices for docstrings and modular design.6 min - Error Handling with Try-Except
Prevent pipeline crashes by catching data-related errors.7 min
5
Working with Data Files
Connect Python to external data sources and destinations.
- The File Context Manager
Safely open and close files using the 'with' statement.6 min - Processing CSV Files
Read and write tabular data using the built-in csv module.7 min - Handling JSON Data
Parse and generate JSON files for web-based data workflows.7 min - Path Management with Pathlib
Handle file paths across different operating systems reliably.6 min
6
Data Pipeline Logic and Capstone
Apply all skills to build a real-world data processing script.
- Designing a Data Pipeline
Plan the Extract, Transform, Load (ETL) steps for a project.6 min - Data Cleaning Techniques
Apply string methods and logic to fix messy input data.6 min - Data Transformation Logic
Perform calculations and reformat data for final output.6 min - Finalizing the Capstone Project
Assemble all components into a single, executable pipeline script.5 min
Final project
The 'Sales-to-JSON' ETL Pipeline
You will build a Python script that reads a raw CSV file containing sales transactions, cleans the data (removes whitespace, fixes types), calculates a 'total revenue' per item, and saves the summarized results into a structured JSON file.
What you'll get
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Certificate of completion
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Short & focused
3h of practical content
Certificate of completion
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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Python Programming Foundations for Data Engineering
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
✓
A/B test design
Proficient
1.7 hrs
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1.9 hrs
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Python Programming Foundations for Data Engineering
Page 2 of 2
Performance detail
Coursework summary
Lessons completed
14 / 14
Practice questions
26 / 28
Assignments submitted
4 (avg 4.5 / 5)
Capstone project
Reviewed — 4.6 / 5
Total practice
6.2 hrs
Performance benchmark
Cohort rank
Top 12% of 1,625
Time to completion
11 days (median: 22)
Mastery score
91 / 100
Practice-question score
94%
Skill verification
Verified Skill Path
Reviews (7)
It's a decent introduction. Could benefit from more diverse examples and a slightly better flow between modules.
This was a good introduction. The structure is logical, and it covers the basics effectively. Might be too introductory for advanced learners.
Good introduction. I appreciated the clear steps, although some of the later modules could have used more examples.
Such a great way to learn. Everything flowed so well, and the practical examples were spot on. Really enjoyed it.
Brilliant course! The flow of information was perfect, and the examples really solidified the concepts. Loved it!
Couldn't have asked for a better learning experience. The structure flowed perfectly, and the examples were incredibly relevant. Highly recommend!
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