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Considering Data Science Users in Your API Design
- Data scientists utilize APIs for tasks like exploratory data analysis, data quality assessments, and training machine learning models.
- They seek large amounts of data for representative samples, perform various analyses, and create visualizations like scatterplots and box plots.
- APIs can be utilized to create analytics reports, dashboards, and train machine learning models for tasks like classification and regression.
- Data scientists may use APIs in data pipelines to regularly retrieve updates and store data in different formats.
- New ways data scientists use APIs include generative AI applications like retrieval-augmented generation (RAG) and large language models (LLMs).
- APIs designed for data science users should include standard external identifiers, SDKs, last changed date/time on endpoints, and bulk download capabilities.
- Providing SDKs for quick access, accurate update information, and bulk download options can enhance the data scientist's experience with the API.
- Using tools like Python libraries (requests, pandas, matplotlib), data scientists can access and analyze API data effectively.
- Exploring generative AI applications in Python can provide insight into advanced API usage for data scientists.
- Engaging with data science users, understanding their needs, and incorporating feedback can help API providers better cater to this user group.
- Developing APIs with features tailored to data scientists can lead to increased adoption and satisfaction among this user segment.
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