How to Rebuild Portfolio Projects Without Proprietary Code

In Justin Norman's latest post, he tackles a challenge familiar to many developers and data scientists: how to showcase past work when the original code is owned by a former employer. His solution? Build a simulation engine to recreate the problem space without proprietary data or IP, then reconstruct the solution using modern tools. This approach struck me as brilliant for two reasons. First, it respects intellectual property boundaries—a must in our industry. Second, it allows you to demonstrate not just what you built, but how you’d build it now with updated frameworks and techniques. For example, Justin reimplemented a freight forecasting system using GRUs and Prophet, and a security event clustering pipeline with K-means and LSA—both reflecting current best practices. This isn’t just about recreating code; it’s about showcasing adaptability, problem-solving, and technical growth. By rebuilding projects from the ground up, you prove you understand the fundamentals, not just the imp