Data Engineering
Python for Data Engineers: Automate, Extract, Deliver
The Python you need to process data, call APIs, and write ETL scripts that keep working when nobody is watching them.
The Python you need to process data, call APIs, and write ETL scripts that keep working when nobody is watching them.
Part of Data Engineering: The Complete, Job-Ready Program
Taught by Sharuk Shaik
Enterprise data engineering trainer with 10+ years of experience and 3000+ students trained
- Lessons
- 16
- Length
- 5h 35m
- Live classes
- 2
- Level
- Beginner
₹7,999
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- 16 recorded lessons, yours for the life of the enrollment
- 2 live classes with your instructor
- 2 downloadable resources
- Verifiable certificate on completion
What you will be able to do
- Process and clean real-world data with pandas confidently
- Call a REST API correctly, including pagination and authentication
- Write an ETL script that is safe to re-run without duplicating or corrupting data
- Structure Python data code so someone else on the team can maintain it
About this course
This is not a general Python course. Every example here is a data engineering task: reading a messy file, calling a rate-limited API, cleaning a DataFrame, writing a script that can safely run twice.
We move from fundamentals through pandas and into the part that actually matters on the job — automation, logging, and idempotency, so a script you write and forget about does not quietly corrupt data the second time it runs.
You will finish having built a real script that pulls from an API and lands the result in a database table, structured the way a production ETL job should be.
Who it is for
- SQL users who want to add scripting and automation to their toolkit
- Analysts moving toward a data engineering role
- Anyone automating a manual, repetitive data task for the first time
What you need
- No prior programming experience required
- A computer able to install Python (setup is covered in Module 1)
Curriculum
4 modules · 16 lessons · 5h 35m
Lessons marked Preview are free to watch.
- Setting up a real working environment Preview 14m
- Variables, control flow and functions 26m
- Working with files: CSV, JSON and plain text Preview 21m
- Error handling that fails loudly 17m
- DataFrames: loading, filtering and reshaping 28m
- Cleaning messy real-world data 25m
- Merging and aggregating like a warehouse join 22m
- Why pandas is not always the right tool 10m
- Calling a REST API and handling pagination 24m
- Authentication: keys, tokens and secrets 18m
- Scheduling a script without a full orchestrator 16m
- Logging so you can debug a 3am failure 19m
- Structuring an ETL script that is testable 23m
- Writing to a database from Python safely 20m
- Idempotency: making a script safe to re-run 22m
- Real-world project: syncing an API into a warehouse table 30m
Your instructor
Sharuk Shaik
Enterprise data engineering trainer with 10+ years of experience and 3000+ students trained
Sharukh Sir is a seasoned data engineering professional with over a decade of experience building and managing enterprise data platforms across multiple industries including banking, insurance, retail, and healthcare.
Having worked with organizations ranging from fast-growing startups to Fortune 500 companies, he brings a unique perspective that blends deep technical knowledge with practical business understanding.
His teaching philosophy is simple: theory without practice is incomplete. Every concept is taught with real-world context, every module ends with hands-on exercises, and every student graduates with production-grade project experience.
His mission is to make every student industry-ready from day one — not just in tools and technology, but in mindset, problem-solving, and professional communication.
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Python for Data Engineers: Automate, Extract, Deliver
16 lessons, 5h 35m of instruction , plus 2 live sessions.
₹7,999
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