  Nowadays, numerous industries have exceptional demand for skills in data
science, such as data analysis, data mining, and machine learning. The
computational notebook (e.g., Jupyter Notebook) is a well-known data science
tool adopted in practice. Kaggle and GitHub are two platforms where data
science communities are used for knowledge-sharing, skill-practicing, and
collaboration. While tutorials and guidelines for novice data science are
available on both platforms, there is a low number of Jupyter Notebooks that
received high numbers of votes from the community. The high-voted notebook is
considered well-documented, easy to understand, and applies the best data
science and software engineering practices. In this research, we aim to
understand the characteristics of high-voted Jupyter Notebooks on Kaggle and
the popular Jupyter Notebooks for data science projects on GitHub. We plan to
mine and analyse the Jupyter Notebooks on both platforms. We will perform
exploratory analytics, data visualization, and feature importances to
understand the overall structure of these notebooks and to identify common
patterns and best-practice features separating the low-voted and high-voted
notebooks. Upon the completion of this research, the discovered insights can be
applied as training guidelines for aspiring data scientists and machine
learning practitioners looking to improve their performance from novice ranking
Jupyter Notebook on Kaggle to a deployable project on GitHub.
