Data Engineering · Python for Data Engineering
Use Python to Process and Move Data
Learn the Python skills data engineers use every day: working with files and APIs, processing and validating data, automating tasks and taking your first steps in pipeline development.
- Python fundamentals for data work
- Files, formats and APIs
- Data processing and validation
- Introduction to data pipelines
Course overview
About This Course
Python for Data Engineering teaches Python with a clear focus on data: reading it from files and APIs, cleaning and transforming it, and moving it reliably from one place to another.
Python is the most widely used language in data engineering. It powers data processing scripts, automation, orchestration tools and big data frameworks such as PySpark, so it is a core skill for anyone building data pipelines.
You will learn Python fundamentals, the data structures that matter most for data work, how to handle common file formats and APIs, how to process data with libraries such as pandas, and how to structure a simple pipeline.
The course follows SQL in our Data Engineering learning paths and prepares you for the Azure and AWS Data Engineering courses.
Who it is for
Who Should Take This Course?
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Learners who know some SQL
You can query data and now want to process and automate it with code.
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Beginners in programming
You want to learn Python through practical data tasks rather than abstract exercises.
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Analysts and report developers
You want to automate repetitive data work and handle data beyond spreadsheets.
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Aspiring data engineers
You are building the programming foundation for a data engineering role.
Learning outcomes
What You Will Learn
By working through the course, you will build towards being able to:
Write Python programs using variables, control flow, functions and modules.
Choose and use lists, dictionaries, sets and tuples for different data tasks.
Read and write CSV, JSON and other common file formats.
Retrieve data from web APIs and handle errors and pagination.
Clean, transform and validate tabular data with pandas.
Automate data tasks with scripts that log what they do and handle failures.
Describe the stages of a data pipeline and build a simple extract, transform and load script.
Curriculum
Course Curriculum
The course is organised into modules. Session order and depth may vary by training arrangement.
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Module 1: Python fundamentals
The core language features you will use in every data script.
Topics covered include
Practical activity: Write small scripts that calculate summary figures from a list of records.
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Module 2: Data structures for data work
Pick the right structure to hold and reshape data.
Topics covered include
Practical activity: Reshape nested JSON-style records into a flat table.
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Module 3: Files and data formats
Read and write the file formats data engineers meet most often.
Topics covered include
Practical activity: Convert a folder of CSV files into a single cleaned output file.
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Module 4: Working with APIs
Pull data from web services reliably.
Topics covered include
Practical activity: Collect data from a public sample API and save it for processing.
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Module 5: Data processing with pandas
Clean, transform and combine tabular data in code.
Topics covered include
Practical activity: Clean and combine two sample datasets and check the result for quality issues.
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Module 6: Automation and pipeline basics
Turn scripts into repeatable, observable data jobs.
Topics covered include
Practical activity: Build a small end-to-end pipeline that extracts, transforms and loads sample data.
Practical learning
Hands-On Learning
Every topic is practised with code. Examples of practical activities include:
Practical work uses sample data and free, open-source Python tools. Ask us which activities are included in your training arrangement.
- Writing scripts that process sample datasets
- Collecting data from public sample APIs
- Cleaning and validating data with pandas
- Building a small extract, transform and load pipeline
Before you start
Prerequisites and Learning Requirements
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Recommended background
Basic SQL knowledge is recommended. Our SQL course covers this.
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Technical prerequisites
Comfort using a computer and working with files and folders. No prior programming experience is required.
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Software and tools
A computer with a reliable internet connection for live online sessions. Python and a code editor are used for practice. Any additional software or accounts for practical activities will be confirmed when you enquire.
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Prior experience
Suitable for beginners in programming who already understand basic data concepts.
Learning Format
IT Champion Hub offers live, instructor-led online training and workshops. Formats, duration and schedules vary by course and are not the same for every program.
- Live online sessions led by an instructor
- Practical activities alongside explanations
- Upcoming batches and schedules shared on enquiry
Enquire to find out the formats and upcoming batches available for this course. Enquire about this course
FAQ
Frequently Asked Questions
Do I need to know Python already?
No. The course starts with Python fundamentals and applies them to data tasks throughout.
Do I need to know SQL first?
Basic SQL is recommended because data engineering combines both skills. If SQL is new to you, start with our SQL course.
Which libraries are covered?
The course uses the Python standard library and pandas for data processing, with an introduction to the concepts behind pipeline and orchestration tools.
Are there practical exercises?
Yes. You will write scripts for every module, ending with a small end-to-end data pipeline.
How can I enquire about upcoming training?
Select Enquire About This Course. The enquiry form will note that you are asking about Python for Data Engineering, and we'll get back to you with upcoming batches and available schedules.
Ready to Explore This Learning Path?
Have questions about the curriculum, prerequisites, or training arrangements? Get in touch with IT Champion Hub to discuss your learning goals.