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.

翻译：如今，众多行业对数据分析、数据挖掘和机器学习等数据科学技能有着特殊需求。计算笔记本（如Jupyter Notebook）是实践中广泛采用的数据科学工具。Kaggle和GitHub是两个数据科学社区平台，用于知识共享、技能实践和协作。尽管这两个平台都为零基础数据科学学习者提供教程和指南，但获得社区高投票数的Jupyter Notebook数量仍然有限。高票笔记本被认为文档完善、易于理解，并遵循最佳数据科学与软件工程实践。本研究旨在理解Kaggle上高票Jupyter Notebook及GitHub上数据科学项目中热门Jupyter Notebook的特征。我们计划挖掘并分析这两个平台上的Jupyter Notebook，通过探索性分析、数据可视化和特征重要性分析，理解这些笔记本的整体结构，识别区分低票与高票笔记本的通用模式及最佳实践特征。研究完成后，所得见解可作为培训指南，帮助有志于从Kaggle新手级Jupyter Notebook进阶至GitHub可部署项目的数据科学家和机器学习从业者提升表现。