Categories of Data Science Tools

 

1. * the Categories of Data Science Tools. It's evident that various tasks are involved in the data science process, and different tools and environments play crucial roles in supporting these tasks. Let's summarize the key points:*

   • Data Management: Involves collecting, persisting, and retrieving data securely and efficiently from various sources.*
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   • Data Integration and Transformation (ETL): Extracts, transforms, and loads data from multiple repositories into a central repository like a Data Warehouse.*
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   • Data Visualization: Represents data graphically through charts, plots, maps, etc., to convey information effectively.*
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   • Model Building: Involves training data and analyzing patterns using machine learning algorithms to create predictive models.*
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   • Model Deployment: Integrates developed models into production environments, making them available for use via APIs.*
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   • Model Monitoring and Assessment: Runs continuous quality checks on models to ensure accuracy, fairness, and robustness.*
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4. Data Science Task Categories:

5. Supporting Tools and Environments:

   • Code Asset Management: Involves version control and collaboration for managing code files. Examples include GitHub.*
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   • Data Asset Management (DAM): Organizes and manages important data from various sources, supporting versioning and collaboration.*
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   • Development Environments (IDEs): Provide a workspace and tools for developing, testing, and deploying source code. IBM Watson Studio is an example.*
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   • Execution Environments: Compile and execute source code, with cloud-based options offering flexibility. IBM Watson Studio is mentioned for data preprocessing, model training, and deployment.*
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6. Model Monitoring and Assessment Tools:*

   • Model Monitoring: Uses tools like Fiddler to track the performance of deployed models in production.*
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   • Model Assessment: Utilizes evaluation metrics like F1 score or true positive rate. IBM Watson OpenScale is highlighted for continuous monitoring.

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8. Integrated Visual Tools:*

   • Fully integrated visual tools like IBM Watson Studio and IBM Cognos Dashboard Embedded cover various components for developing deep learning and machine learning models.

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This comprehensive set of tools and tasks enables data scientists to manage, analyze, and deploy models effectively throughout the data science lifecycle.