# Data Science Lab

- [Overview](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/overview.md): Get an overview of the Data Science Lab module.
- [What is Data Science Lab?](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/overview/what-is-data-science-lab.md): Explore the collaborative hub for Data Scientists and understand the key concepts of this module.
- [Data Science User Workflow](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/overview/data-science-user-workflow.md)
- [Supported Environments](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/overview/supported-environments.md)
- [Getting Started](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/getting-started.md): This page outlines the steps to access the DSLab module from the platform homepage and the left side navigation panel of the Data Science Lab module.
- [Projects](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/projects.md): This section details the procedures for creating and managing data science projects.
- [Creating a New Project](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/projects/creating-a-new-project.md)
- [Git Integration](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/projects/git-integration.md): Data Science Lab allows Git integration at the project creation level, such projects are termed as "Git Sync Project" .
- [Managing Projects](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/projects/managing-projects.md): This section provides a detailed overview of the various actions and operations that can be performed on a Data Science Lab project.
- [Workspace](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace.md): The Workspace is a placeholder to create and save various data science experiments inside the Data Science Lab modules.
- [Exploring Workspace](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace.md)
- [Repo Folder Operations](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace/repo-folder-operations.md): The Repo folder is a default folder created under the Workspace tab. It opens by default while accessing the Workspace tab.
- [Repo Folder Operations for a Repo Sync Project](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace/repo-folder-operations-for-a-repo-sync-project.md): A Repo Sync Project will have only a Repo folder allowing users create various Data Science experiments for the project.
- [Utils Folder](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace/utils-folder.md): This section provides an overview of the attributive operations provided for the Utils folder.
- [Managing Utility Files](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace/utils-folder/managing-utility-files.md): This page describes the various actions you can apply to utility scripts after you import them.
- [Accessing a Utility File inside a Notebook](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace/utils-folder/accessing-a-utility-file-inside-a-notebook.md): Steps to access a Utility script inside a Notebook.
- [Files Operations](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/exploring-workspace/files-operations.md): This section helps the user to understand the attributes provided to the file folder created inside a normal Data Science Lab project.
- [Create or Import Notebook](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/create-or-import-notebook.md): Users can create new notebooks, import existing notebooks, and push updates to Git repositories. This section covers how to add, import, and work with notebooks, as well as how to push files to Git.
- [Developing & Running Code in Notebooks](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/developing-and-running-code-in-notebooks.md): This section provides an overview of how to write and execute code within notebooks.
- [Develop Code using Code Assist](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/developing-and-running-code-in-notebooks/develop-code-using-code-assist.md): Unleash the power of Code Assist for faster and more accurate code development.
- [Review & Debug Code with Linter](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/developing-and-running-code-in-notebooks/review-and-debug-code-with-linter.md): Avail instant debugging by screening your data science script through Linter.
- [Markdown Cells](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/developing-and-running-code-in-notebooks/markdown-cells.md): This page describes steps to use the text cells of the Data Science Notebook.
- [Manage Notebook](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/manage-notebook.md): This section explains the credited actions to a notebook within a data science lab project workspace.
- [Register & Publish Notebook](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/register-and-publish-notebook.md): Transform your notebook from a static document into a reusable, versioned, and shareable asset across modules.
- [Notebook Version Control](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/notebook-version-control.md): The Push into VCS & Pull from VCS functionality allows users to manage and maintain different versions of the same notebook at various stages of a project.
- [Collaboration & Sharing](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/collaboration-and-sharing.md): Share your data science notebooks across teams or users while controlling access levels.
- [Operations](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations.md): This section describes the various operations available within a Data Science Project.
- [Data](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/data.md): The Data option allows users to add data to their project from the Data Science Notebook infrastructure.
- [Data Management Actions](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/data/data-management-actions.md)
- [Managing Secrets & Environment Variables](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/managing-secrets-and-environment-variables.md): Use Environment Variables inside your notebook to save your confidential information from getting exposed.
- [Algorithms](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/algorithms.md): Get steps on how to do Algorithm Settings and Project level access to use Algorithms inside Notebook.​
- [Transforms](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/transforms.md): Save and load models with transform script, register them or publish them as an API through DS Lab module.
- [Models](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/models.md): This page offers step-by-step process on how to develop, train, save, and load a model inside the Notebook infrastructure.
- [Artifacts](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/artifacts.md)
- [Variable Explorer](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/variable-explorer.md)
- [Writers](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/workspace/operations/writers.md)
- [Agentic Tools](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/agentic-tools.md): This section provides an overview of the Agentic Tools available within the Data Science Lab module. These tools enable users to build and manage autonomous AI systems.
- [Create an Agentic Tool](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/agentic-tools/create-an-agentic-tool.md)
- [Mapping an Agentic Tool within a Data Pipeline Workflow](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/agentic-tools/create-an-agentic-tool/mapping-an-agentic-tool-within-a-data-pipeline-workflow.md): This page provides step-by-step process to consume an Agentic Tool inside a Data Pipeline workflow.
- [Manage Agentic Tools](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/agentic-tools/manage-agentic-tools.md): This page outlines of the key steps for managing an agentic tool.
- [Models](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models.md): The Models tab includes various models created, saved, or imported using the Data Science Lab module. It broadly lists all the created and imported Data Science Models.
- [Accessing the Models Page](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/accessing-the-models-page.md): You can access the Models page by selecting Models from the left navigation panel within the Data Science Lab.
- [Filter Models List](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/filter-models-list.md)
- [Import a Model](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/import-a-model.md): External models can be imported into the Data Science Lab and experimented inside the Notebooks using the Import Model functionality.
- [Model Explainer Generator](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/model-explainer-generator.md): This functionality generates a model explainer dashboard to help users better interpret their models.
- [Model Explainer Dashboard](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/model-explainer-dashboard.md)
- [Classification Model Explainer](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/model-explainer-dashboard/classification-model-explainer.md)
- [Regression Model Explainer](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/model-explainer-dashboard/regression-model-explainer.md)
- [Forecasting Model Explainer](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/model-explainer-dashboard/forecasting-model-explainer.md)
- [Saving a Model](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/saving-a-model.md): This section aims to step down the process of creating, saving, and loading a Data Science model using the notebook infrastructure provided inside the Data Science Lab module.
- [Share a Model](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/share-a-model.md)
- [Register/ Unregister Model](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/register-unregister-model.md)
- [Consuming a Registered Model inside Data Pipeline Module](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/register-unregister-model/consuming-a-registered-model-inside-data-pipeline-module.md)
- [Register a Model as an API](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/register-a-model-as-an-api.md)
- [End-to-end Steps for Model Creation](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/end-to-end-steps-for-model-creation.md): Get end-to-end model creation tutorials to get started. You can import these notebooks to your BDB Data Science Lab.
- [ML Model](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/end-to-end-steps-for-model-creation/ml-model.md): This page provides step-by-step process to train an ML model inside a Data Science notebook.
- [PyTorch Model](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/models/end-to-end-steps-for-model-creation/pytorch-model.md)
- [AutoML](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/automl.md): This documentation section focuses on the creation and management of AutoML models (experiments).
- [Creating AutoML Experiment](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/automl/creating-automl-experiment.md): Learn how to create an AutoML Experiment and explore your options for the next phase.
- [Manage AutoML Models](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/automl/manage-automl-models.md): Manage your AutoML Models.
- [Trash](https://docs.bdb.ai/bdb-user-documentation/platform-modules/10.0/data-science-lab/trash.md): The Trash page facilitates the users to find all your deleted projects. From here, you can either restore them or permanently delete them.


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