Practical Data Engineering with Python and PySpark
Hands-on, Combo CourseA hands-on programme combining Python with Pandas for data wrangling and PySpark for distributed processing. Build practical data engineering workflows that span both small-scale and large-scale datasets
Program Fee
Offer ends: 5/30/2026
WhatsApp Support Group Q&A Assistance Hands-On Labs Mock Interviews Exam Guidance Learning Resources Career Support Ongoing Mentorship
Request Syllabus / Call BackBatch Details
Evening Batch- 4 Weeks
30 seats left
Evening Batch -4 Weeks
Program Overview
A hands-on programme combining Python with Pandas for single-machine data wrangling and Apache PySpark for distributed processing. Participants build practical data engineering workflows that span both small-scale and large-scale datasets, learning when to use each tool and how to move data smoothly between them.
What You'll Learn
- Apply Python and Pandas to clean, transform, and prepare structured datasets
- Build distributed data pipelines using Apache PySpark for large-scale processing
- Use Spark SQL and PySpark DataFrames to wrangle and join big data efficiently
- Design end-to-end data engineering workflows from source to analytics-ready output
Who Can Join
- •Aspiring data engineers and analysts working with mixed dataset sizes
- •Software developers transitioning into data-focused roles
- •IT professionals managing data pipelines and ETL workflows
- •Practitioners seeking exposure to both Python and PySpark
PREREQUISITES
• Basic Python programming knowledge (variables, loops, functions)
• Familiarity with tabular data and basic SQL is helpful
• No prior PySpark or distributed computing experience required
Batch Details
Evening Batch- 4 Weeks
30 seats left
Evening Batch -4 Weeks
Tools & Technologies
Projects Included
- ▹ Pandas data wrangling and feature engineering lab
- ▹ Distributed ETL pipeline with PySpark
- ▹ End-to-end project taking raw data to analytics-ready output
Official Certifications
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