Professional Certificate in Python Data Engineering and Architecture

This course is designed for professionals with some programming experience looking to specialize in Python-based data engineering and architecture. Participants will gain skills in data pipeline development, ETL processes, data storage solutions, and cloud-based data processing. The curriculum covers essential tools and frameworks like Apache Airflow, AWS Lambda, and Glue, as well as best practices for scalable data architecture.

Students will learn to design, implement, and manage end-to-end data workflows, optimize data processing pipelines, and integrate data systems with modern cloud services. By the end, participants will be equipped to handle complex data engineering challenges and contribute to the development of robust data architectures.

1. 1. Introduction to Python for Data Engineering: Learners will be introduced to Python programming fundamentals and its use in data engineering. They will gain skills in basic syntax, data types, control structures, and functions, essential for data manipulation and analysis.
2. 2. Data Manipulation with Pandas: Learners will study the Pandas library for data manipulation and analysis. They will master techniques for data cleaning, transformation, and preparation, including handling missing values, merging datasets, and data aggregation.
3. 3. Data Storage and Retrieval: Learners will learn about data storage solutions including databases and file systems. They will gain skills in designing and implementing data storage strategies, and interacting with SQL databases using Python.
4. 4. Data Processing and Automation with Python: Learners will explore data processing techniques and automation tools in Python. They will learn to automate data workflows, schedule tasks, and use Python to streamline data engineering tasks.
5. 5. Data Visualization with Matplotlib and Seaborn: Learners will study data visualization techniques using Matplotlib and Seaborn. They will develop skills in creating effective visualizations, interpreting data, and communicating insights through visual representations.
6. 6. Data Engineering Best Practices: Learners will understand best practices in data engineering, including version control, continuous integration, and deployment strategies. They will learn how to maintain and scale data pipelines effectively.
7. 7. Advanced Python for Data Engineering: Learners will delve into advanced Python concepts and libraries relevant to data engineering, such as NumPy, SciPy, and Django. They will master more sophisticated data manipulation and application development techniques.
8. 8. Cloud Data Engineering with AWS: Learners will study data engineering practices on the Amazon Web Services (AWS) platform. They will learn to manage data storage, processing, and computing resources in the cloud, and implement secure and scalable architectures.
9. 9. Data Architecture and Design: Learners will learn to design data architectures for various applications, including big data and real-time systems. They will understand data warehousing concepts and design data models for efficient data retrieval and analysis.
10. 10. Project: Implementing a Complete Data Pipeline: Learners will apply their knowledge to build a complete data pipeline project. They will design, implement, and test a data pipeline, covering all aspects from data collection to visualization, and gain practical experience in real-world data engineering challenges.