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Alexandra Wörner: A data scientist's guide to code reviews
Data scientists, prioritize code reviews to improve code clarity, reduce bugs, and ensure reproducibility, while also learning from each other's coding styles and styles guides.
- Code reviews are crucial for data science work, but many data scientists don’t prioritize them.
- Conduct code reviews to improve code clarity, check for logical errors, and ensure reproducibility.
- Prioritize ongoing tests and code reviews, especially for new colleagues.
- Developers should focus on understanding each other’s coding styles and following style guides.
- Weekly dedicated time for code reviews can be beneficial, but it depends on the team’s size and expertise.
- Code reviews help reduce bugs, improve code quality, and ensure compliance with coding standards.
- Peer reviews can be categories as software engineers, data scientists, or machine learning engineers.
- Code reviews can be tedious, but they can also be a learning experience and improve one’s skills.
- In data science, code reviews focus on understanding the model, evaluation metrics, and trained data.
- Standardized code review process can be challenging, but it is essential for ensuring quality and reproducibility.
- Small teams can have a standardized code review process, even if it’s more relaxed.
- Data scientists should explain their code and results to stakeholders, and provide supplementary documentation.
- Code reviews can be time-consuming, but they can also be beneficial for learning and improving code quality.
- In software engineering, code reviews focus on technical implementation, architecture, and design decisions.
- Peer reviews in data science are different from traditional code reviews, focusing on higher-level abstractions.
- The goal of code reviews is to ensure the defined task has been completed and provide feedback for improvement.
- Automatic tools can aid code reviews, such as nbconvert for converting ipython notebooks.
- Code reviews can detect misunderstandings, logical errors, and bugs, but they may not detect all issues.
- The reviewer’s role is to ensure the code meets the requirements, is maintainable, and reusable.
- Code reviews are important for data science work, even if they may seem tedious or time-consuming.