Reproducibility in data science hinges on capturing code, data, and the runtime environment, and making them versionable and well documented. Version control for code keeps a traceable history of changes, so you can understand how results evolved and revert to a previous state if needed. Data versioning ensures the exact dataset used (including preprocessing steps and transformations) can be retrieved later, preventing shifts in results when data changes. Environment management guarantees the same runtime setup—Python or R version, OS specifics, and a consistent set of libraries—so runs aren’t affected by subtle system differences. Dependency pinning locks specific library versions, protecting against drift that can alter behavior or results across runs. Logging captures the details of each experiment—parameters, seeds, data splits, and metrics—so you can reproduce a particular run precisely and compare results across attempts. Documentation ties everything together, explaining how to reproduce steps, what each parameter means, and how data flows through the pipeline. Together, these practices create a stable, auditable trail from code to results, which is essential for reliable replication, collaboration, and long-term maintenance.