Documenting Assumptions and Decisions
When you work on data science projects, documenting your assumptions, data sources, and key decisions is essential for building analyses that others — and your future self — can understand and trust. As you explore data, select features, or choose modeling techniques, you make choices based on context, constraints, and available information. Without recording the why behind these choices, your reasoning can become lost, making it difficult to justify or reproduce your work later. Clear documentation helps others follow your thought process, enables seamless project handoffs, and provides transparency for stakeholders who may question the basis of your conclusions. You should always aim to capture not just what you did, but why you did it.
Make it a practice to use markdown cells in your notebooks to record the rationale for major steps. For example, after cleaning data or choosing a model, add a short explanation of your reasoning, referencing the importance of documenting assumptions, sources, and decisions as described above.
- Contains markdown cells explaining data source choices, such as why a particular dataset was selected and what limitations it has;
- Clearly states assumptions, like
Assume missing values are random, with justification; - Describes why certain preprocessing steps or models were chosen, referencing project goals or constraints;
- Future readers can quickly understand the context and reasoning behind every major action.
- Lacks explanations for data source selection or preprocessing steps;
- No record of why missing values were handled a certain way or why a specific model was used;
- Future readers are left guessing about the rationale, making it hard to validate, critique, or extend the analysis.
Merci pour vos commentaires !
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When you work on data science projects, documenting your assumptions, data sources, and key decisions is essential for building analyses that others — and your future self — can understand and trust. As you explore data, select features, or choose modeling techniques, you make choices based on context, constraints, and available information. Without recording the why behind these choices, your reasoning can become lost, making it difficult to justify or reproduce your work later. Clear documentation helps others follow your thought process, enables seamless project handoffs, and provides transparency for stakeholders who may question the basis of your conclusions. You should always aim to capture not just what you did, but why you did it.
Make it a practice to use markdown cells in your notebooks to record the rationale for major steps. For example, after cleaning data or choosing a model, add a short explanation of your reasoning, referencing the importance of documenting assumptions, sources, and decisions as described above.
- Contains markdown cells explaining data source choices, such as why a particular dataset was selected and what limitations it has;
- Clearly states assumptions, like
Assume missing values are random, with justification; - Describes why certain preprocessing steps or models were chosen, referencing project goals or constraints;
- Future readers can quickly understand the context and reasoning behind every major action.
- Lacks explanations for data source selection or preprocessing steps;
- No record of why missing values were handled a certain way or why a specific model was used;
- Future readers are left guessing about the rationale, making it hard to validate, critique, or extend the analysis.
Merci pour vos commentaires !
