Data Engineering with Python

Practical skills to design, build, and automate ETL pipelines, process big data, and manage scalable workflows using modern tools and cloud platforms.

Overview

This comprehensive Data Engineering with Python course equips learners with the practical skills to design, build, and automate ETL pipelines, process big data, and manage scalable workflows using modern tools and cloud platforms.

Through a hands-on, project-based approach, you will gain expertise in:

Course Duration: 120 hours over 10 weeks

Learning Outcomes

By the end of this program, you will be able to:

Course Modules

1. Introduction to Data Engineering with Python

Duration: 05 Hours

  • What is data engineering and why Python?
  • Key tools and technologies for ETL pipelines
  • Installing Python, PIP, and virtual environments
  • Setting up Jupyter Notebook and Online IDEs
  • Core libraries: pandas, SQLAlchemy, requests
2. Python Essentials for Data Engineering

Duration: 05 Hours

  • Python basics recap (data types, control flow, functions)
  • Working with built-in libraries: os, pathlib, shutil, datetime
  • File I/O for large datasets and compression (gzip, bz2)
3. Working with Structured Data

Duration: 10 Hours

  • Handling CSV, JSON, Parquet, Avro with pandas
  • Excel automation (openpyxl, xlsxwriter)
  • Parsing JSON and XML files
  • Data validation and cleaning best practices
4. Database Integration

Duration: 10 Hours

  • SQL refresher: SELECT, INSERT, UPDATE, DELETE
  • Connecting to SQL databases (SQLite, PostgreSQL, MySQL)
  • Executing queries and transactions
  • Connection pooling and best practices
5. Data Ingestion from APIs and Web Sources

Duration: 10 Hours

  • REST API integration with requests
  • Authentication, pagination, and rate-limiting
  • Web scraping with BeautifulSoup & lxml
6. Data Transformation and Cleaning

Duration: 10 Hours

  • Building ETL pipelines with pandas
  • Handling missing values, duplicates, outliers
  • String, datetime, and categorical transformations
  • Data normalization and schema consistency
7. Big Data Processing with PySpark

Duration: 10 Hours

  • Introduction to distributed processing
  • Working with large datasets using PySpark
  • Connecting to cloud storage (AWS S3, GCP, Azure)
  • Batch vs. streaming data workflows
8. Scheduling and Automation

Duration: 05 Hours

  • Reusable ETL scripts in Python
  • Automation with cron and Task Scheduler
  • Introduction to Apache Airflow DAGs
9. Error Handling and Logging in Pipelines

Duration: 05 Hours

  • Structured logging with Python logging module
  • Robust exception handling in ETL pipelines
  • Retry, monitoring, and failover mechanisms

Weekly Online Tests & Assignments

Final Project (30 Hours)

Course Delivery and Fee Structure

1 Person

Mode: Online - Google Classroom

Fee: Rs. 15,000

Lecture Hours/Week: 8 Hrs (4 Days/Week)

Duration: 10 Weeks

Group of 3

Mode: Online - Google Classroom

Fee: Rs. 14,000

Lecture Hours/Week: 8 Hrs (4 Days/Week)

Duration: 10 Weeks

Group of 5

Mode: Online - Google Classroom

Fee: Rs. 12,000

Lecture Hours/Week: 8 Hrs (4 Days/Week)

Duration: 10 Weeks

Explore. Enroll. Elevate.

Please review our course offerings and contact us to schedule your first class. Whether you're a student, job seeker, or aspiring developer, Digital Mindz Academy is your launchpad to a smarter future.

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