Scholarship in data science should consists of a complete software development environment along with instructions for all the results and figures. However, fully specifying and reproducing an arbitrary data science workflow can often be challenging, especially with the increasing complexity of software dependencies and computational infrastructure. Furthermore, reproducibility that relies on documentation or language specific tools can involve specialized adjustments and tweaking that many researchers may not have the time or background for. To address this common deficiency, we introduce a computational research framework to the data science community that can specify complex computational environments using an OS-level virtualization technology called containers. We show that the container-driven reproducibility approach balances flexibility and ease of use through Visual Studio Code, a popular code editor. In addition, to alleviate the steep initial learning curve of containers, we introduce a code-generating template repository for further simplifying the initial setup of Python and/or R-based workflows commonly used in data science.