Course Description

Embark on an exciting journey into the world of data engineering with the Python Project for Data Engineering course. This intermediate-level program, offered by IBM, is designed to equip you with hands-on experience in critical data engineering tasks using Python. As you progress through this course, you'll master essential skills in data collection, extraction, and transformation, preparing you to tackle real-world data engineering challenges with confidence.

What You'll Learn

• Perform web scraping and data extraction using APIs
• Transform data into specific data types
• Log operations and prepare data for loading
• Execute ETL (Extract, Transform, Load) tasks using Python and Jupyter Notebooks
• Enhance collaborative skills through peer reviews and constructive feedback
• Utilize Watson Studio for sharing and collaborating on Jupyter Notebooks

Prerequisites

While there are no specific prerequisites mentioned, having a basic understanding of Python programming and familiarity with data concepts would be beneficial. This course is designed for intermediate-level learners, so some prior experience with Python and data handling is recommended.

Course Coverage

• Web scraping techniques for data collection
• API integration for data extraction
• Data transformation and type conversion
• ETL processes using Python and Jupyter Notebooks
• Logging operations for transparency and reproducibility
• Data preparation for analysis and loading
• Collaborative workflows using Watson Studio
• Peer review processes and constructive feedback

Who Should Take This Course

• Aspiring data engineers looking to enhance their Python skills
• Data analysts seeking to expand their data processing capabilities
• Software developers interested in transitioning to data engineering roles
• Students pursuing careers in data science or data engineering
• Professionals aiming to gain practical experience in Python-based data engineering tasks

Real-World Applications

The skills acquired in this course are directly applicable to real-world scenarios. Graduates will be able to:

• Develop efficient data pipelines for organizations
• Automate data collection processes from various sources
• Implement data transformation workflows for business intelligence
• Contribute to big data projects with improved data processing techniques
• Collaborate effectively on data engineering teams using industry-standard tools
• Apply ETL principles to solve complex data integration challenges
• Enhance data quality and reliability through proper logging and documentation
• Leverage API integrations for real-time data acquisition and analysis

Syllabus

Module 1: Extract, Transform, Load (ETL)

• Introduction to ETL concepts
• Data extraction techniques (web scraping, API integration)
• Data transformation methods
• Loading processes and best practices

Module 2: Final Project

• Application of learned concepts in a real-world scenario
• End-to-end implementation of an ETL pipeline
• Collaborative work using Watson Studio
• Peer review and feedback process