Data Science Best Practices

Although the documentation process of any data science team will likely vary between different projects, teams, companies, and industries, our data science team takes pride in its efficient but thorough practices. We’ve decided to share a few general data science best practices below. Let us know your thoughts in the comments!

Identify the problem statement

Before the problem is identified, it’s important to understand who the user is fully and the project's overall goal. Asking questions such as, “who will be using this data” “why do they need it” and “how will they be using it” is a good place to start.

Once this is understood, it becomes easier to find the right data sources and determine how the data should be analyzed and integrated.

Select appropriate tools and metrics

To ensure you and your team are ready to take on the project, consider the infrastructure, the amount of data that will need to be analyzed, the frequency with which users will reference the analysis and the amount of processing power required.

Developing an action plan

Organizations need a clear action plan that outlines how these insights should inform the next steps and who is accountable for them. For stakeholders to implement action plans, insights must be packaged, answer original business questions, and be clearly visualized.

Communicate the results

There are situations when data scientists produce results but cannot communicate these results/findings to stakeholders due to a lack of documentation. Document your findings and share them with the relevant stakeholders or decision-makers to ensure data results create value.

Be prepared for future enhancements

It’s essential to be open to changes as needed. Don’t be afraid to adapt your project plan as the data becomes available or make additional changes that align with users’ needs. It’s important to be flexible and adaptable as new data is collected.

What best practices does your team have?

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