// learn by building

Python Data Engineering Projects

Q: Do I need prior data engineering experience?

No. Beginner projects assume only basic Python and command-line familiarity. The beginner section starts with an environment setup project if you're starting from scratch.

Q: How long does each project take?

Beginner projects: 2 to 4 hours. Intermediate: 4 to 8 hours. Advanced: a full day or more.

33 hands-on projects to take you from first pipeline to production-grade systems. Work through them in order or jump to what you need.

12 beginner→16 intermediate→5 advanced

Filter by topic

All topics Getting Started2 ORMs for Python3 Data/Schema Validation3 Database Migration Tools3 Data Wrangling4 ETL Frameworks3 Orchestration Tools3 Stream Processing3 API Development3 Data Visualization3 Machine Learning Libraries3

// level up

Intermediate Projects

1 projects

Combine multiple tools, add orchestration and testing, and build systems that look like real production work.

$pythonscikit_learn_customer_churn.pyintermediate

Customer Churn Prediction with Scikit-learn

Build a machine learning model to predict customer churn using Scikit-learn's Random Forest classifier. Learn data preprocessing, model training, evaluation metrics, cross-validation, and feature importance analysis - foundational ML skills every data engineer should master.

scikit-learn

// go deep

Advanced Projects

2 projects

Production-scale challenges: distributed processing, complex architectures, performance tuning, and streaming systems.

$pythontensorflow_energy_forecasting.pyadvanced

Energy Consumption Forecasting with TensorFlow

Create a deep learning time series forecasting model using TensorFlow and LSTM networks. Learn to build windowed datasets, train neural networks with Keras API, make future predictions, and deploy models - essential for any time-series prediction task.

tensorflow

$pythonpytorch_anomaly_detection.pyadvanced

Network Anomaly Detection with PyTorch

Implement an autoencoder neural network in PyTorch for unsupervised anomaly detection in network traffic. Learn PyTorch's nn.Module, custom datasets, DataLoaders, and how to identify outliers - critical for security and monitoring applications.

pytorch

// common questions

Frequently Asked Questions

Why learn data engineering through projects rather than courses?

Courses teach concepts; projects teach you to debug, integrate tools, and ship working systems. Employers care about demonstrated ability, not certificates. Each project here produces a real artifact you can show — a working pipeline, a schema migration, a monitoring dashboard — which matters far more in interviews than course completions.

How do I choose between beginner, intermediate, and advanced?

Start at beginner if you're new to data engineering or learning a specific new tool. Move to intermediate when you're comfortable with basic ETL and want to add orchestration, testing, and multi-tool workflows. Tackle advanced when you need production-scale patterns: distributed processing, streaming, or complex architecture decisions. Each project lists exact prerequisites so there's no guessing.

What will I actually build in these projects?

Real pipelines: weather data ingestion with DLT, e-commerce transformations with dbt, schema validation with Pandera, ORMs with SQLAlchemy, orchestrated workflows with Airflow and Prefect, streaming with Kafka, and machine learning pipelines with Scikit-learn and MLflow. Every project has a working GitHub repo and a full tutorial walkthrough.

Do I need prior data engineering experience?

No. Beginner projects assume only basic Python (variables, functions, loops) and basic command-line familiarity. SQL helps for the database-focused projects but is not required to start. The beginner section has an environment setup project as the very first step if you're starting from scratch.

How long does each project take?

Beginner projects typically take 2 to 4 hours for a first pass. Intermediate projects run 4 to 8 hours depending on how deep you go. Advanced projects can take a full day or more if you extend them to production quality. All timings assume you follow the tutorial — exploring and adapting will take longer, which is the point.