If you want to become a data engineer in 2026, it’s crucial to learn Python correctly and avoid random tutorials that don’t prepare you for real jobs. The video recommends four top courses that take you from barely knowing Python to building real data pipelines that companies pay for. These courses are carefully selected to provide a structured and practical learning path, ensuring you gain the skills needed to succeed as a data engineer.
The first recommended course is DataCamp’s Data Engineer with Python, which offers a full skills stack covering Python, SQL, Spark, ETL, pipelines, and real-world workflows. This course is interactive and hands-on, built around DataCamp’s industry-recognized data engineer certification. Its strengths include real-world coding projects, structured progression, and coverage of the entire data pipeline from raw data to orchestration. The main downside is that it is a self-study course, requiring consistency and dedication, but its interactive nature helps maintain motivation.
The second course is also from DataCamp and is designed for complete beginners: Python Data Fundamentals. This course covers Python syntax, functions, loops, debugging, and essential data structures used in data engineering. It is perfect for those with no prior Python knowledge, featuring fast, interactive lessons without filler content. While it is self-study, the interactive format helps learners stay engaged. If you already have Python experience, this course might be unnecessary, but it is included in the same subscription as the first course, making it a good starting point.
The third course is a Udemy offering called Data Engineering Essentials: SQL, Python, and Spark. This project-driven course focuses on practical pipeline building, covering SQL, Python scripting, PySpark, ETL, and transformations. It is very hands-on, allowing you to create and run full pipelines and build a strong portfolio with tangible projects. However, it is less structured than DataCamp courses and more dependent on the instructor’s style. Udemy courses can be purchased individually or accessed via subscription, offering flexibility but less centralized learning.
The final course is a Coursera course titled Data Analysis with Python. Despite its name, it focuses on Python data manipulation fundamentals essential for data engineering, teaching libraries like pandas and numpy, handling messy data, and building transformations. It provides clear explanations and is a perfect bridge before working with big data tools like Spark. While it is more analysis-oriented, it equips learners with crucial data processing skills needed in data engineering. Overall, the recommended learning path starts with building Python fundamentals, then mastering data manipulation, real pipelines, and finally scaling with big data tools, all centered around Python. Links to these courses are provided for easy access.