5 Free Data Engineering Courses

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Data engineers are unsung heroes. The heroes all other data team members only sometimes deserve but always need.

If your goal is to become one, you’ll need to travel a long path of learning and investing in your knowledge.

To make it a bit more approachable and not get you immediately stuck in debt, include (some of) these free online courses in your learning path.

1. Understanding Data Engineering on DataCamp

 
Level: Beginner

Link: Understanding Data Engineering

Description: This is a 2-hour course consisting of 11 videos and 32 exercises. It’s a no-coding course, which will teach you:

• What is data engineering
• Storing data: managing different data structures, working in SQL, implementing data storage solutions
• Moving and processing Data: Creating pipelines, automation, parallel and cloud computing

2. DelftX: AI Skills for Engineers: Data Engineering and Data Pipelines on edX

 
Level: Beginner

Link: DelftX: AI Skills for Engineers: Data Engineering and Data Pipelines

Description: This is an introductory course provided by the Delft University of Technology for those who want to learn how to develop Python-based AI applications. In 6 weeks, It will provide you with the fundamentals of:

• Data management for AI
• Relational data management (SQL)
• Handling data with Python, pandas, and Jupyter
• Data visualization with seaborn

3. DE Zoomcamp

 
Level: Intermediate

Link: DE Zoomcamp

Description: This is a bootcamp-style course focusing on teaching practical knowledge by working on real-world projects. You will work with technologies such as:

The course covers the following topics:

• Workflow orchestration
• Data warehousing
• Analytics engineering 
• Batch processing
• Stream processing

4. AI: Spark, Hadoop, and Snowflake for Data Engineering on edX

 
Level: Intermediate

Link: AI: Spark, Hadoop, and Snowflake for Data Engineering

Description: This is another course focusing on using data engineering technologies. In 4 weeks, you will learn:

• Managing and optimizing Apache Hadoop, Apache Spark, and Snowflake
• Use Databricks to execute data analytics and ML tasks
• Optimize data pipelines using Python, PySpark, and MLflow
• Kaizen, DevOps, and DataOps data engineering methodologies

5. AI: Advanced Data Engineering on edX

 
Level: Advanced

Link: AI: Advanced Data Engineering

Description: This course will equip you with the expertise in handling large datasets and scaling data systems in 4 weeks. Specifically, you will learn:

• Celery and RabbitMQ for scalable data consumption
• Apache Airflow for workflow optimization
• Vector and graph databases for data management at scale

Bonus: Projects & Real Data for Practicing Data Engineering Skills

 
The five courses above deal with practical data engineering skills to various degrees. No technical skill will sink in unless you practice it on real data.

Here are several resource suggestions where you can find it.

1. StrataScratch: 50 data science projects, also suitable for practicing data engineering skills.
2. Kaggle: Source for lots of quality real datasets.
3. Open Data on AWS: Public datasets on AWS.
4. Google Cloud Public Datasets: Public datasets on Google Cloud.
5. Data.gov: The US Government’s open-access datasets.
6. FiveThirtyEight: Datasets on politics, sports, science & health, economics, and culture.

Conclusion

 
Data engineering being such a complex and vast field, there’s no one course that will teach you everything. Even these five courses only scratch the surface. However, they give you a good overall idea of techniques and technologies used in data engineering.

With several additional resources for data engineering projects and data, you are off to a good start.
 
 

Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.

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