Summary

In this chapter, we covered the essentials of structuring a data science project, focusing on developing impactful data products.

We discussed three project categories, emphasizing the importance of selecting the right use cases that align with your organization’s goals and have the potential to deliver real value.

We provided a framework for evaluating and prioritizing use cases based on feasibility and impact, ensuring that you invest resources in projects that drive your business forward.

We also explored the key stages of data product development, from data preparation to model design, evaluation, and deployment, while adhering to best practices such as responsible AI principles, clear documentation, version control, and CI/CD practices.

Finally, we discussed evaluating the business impact of your data product by selecting relevant metrics and KPIs that align with your company’s goals. By demonstrating the tangible value and ROI of your data science...