Global Certificate in Python Data Engineering Fundamentals

This course is designed for individuals with basic programming knowledge who wish to specialize in Python data engineering. It covers essential skills in data manipulation, analysis, and engineering using Python tools like pandas, NumPy, and SQL. Participants will gain proficiency in ETL processes, data pipeline development, and working with big data frameworks.

Upon completion, learners will be able to build and manage data pipelines, process large datasets efficiently, and integrate data from various sources. They will also understand best practices in data engineering and be equipped to apply Python in real-world data engineering projects.

1. 1. Introduction to Python Programming: Learners will explore the basics of Python programming, including variables, data types, and control structures. They will gain foundational coding skills essential for data engineering.
2. 2. Data Manipulation with Pandas: Learners will study how to use Pandas for data manipulation and analysis. They will learn to clean, transform, and analyze data efficiently, preparing them for larger data projects.
3. 3. Data Visualization with Matplotlib and Seaborn: Learners will delve into data visualization techniques using Matplotlib and Seaborn. They will create various types of plots to understand and communicate data insights effectively.
4. 4. Data Cleaning and Preparation: Learners will focus on the essential skill of data cleaning and preparation. They will learn to handle missing data, correct errors, and prepare datasets for further analysis or machine learning.
5. 5. Introduction to SQL for Data Engineering: Learners will gain an understanding of SQL, the language for managing and querying relational databases. They will learn to write complex SQL queries to extract and manipulate data from databases.
6. 6. Data Storage and Management: Learners will explore different data storage solutions and learn how to manage data in various formats such as CSV, JSON, and databases. They will also learn about data normalization and denormalization.
7. 7. Introduction to Machine Learning with Scikit-learn: Learners will be introduced to machine learning concepts and techniques using Scikit-learn. They will develop skills to build and evaluate machine learning models for predicting outcomes based on data.
8. 8. Data Pipelines and Automation: Learners will learn how to create automated data pipelines using Python scripts and tools like Apache Airflow. They will understand the importance of continuous data processing and automation in modern data engineering.
9. 9. Advanced Data Analysis Techniques: Learners will dive into advanced data analysis techniques including time series analysis, text analysis, and data mining. They will apply these techniques to real-world datasets to uncover deeper insights.
10. 10. Project: Building a Data Engineering Solution: Learners will work on a comprehensive project where they design and implement a complete data engineering solution. They will integrate all the skills learned throughout the programme to build a functional data pipeline.