Without deliberate and conscious effort towards organization, tool choice, and process, complex and large data science projects can quickly grow out-of-hand and become irreproducible. This talk will discuss when and why one should add tools such as GitHub, Make and Docker to their data science workflow to mitigate chaos and maximize transparency, reproducibility, and productivity.