Quick start tutorials
Quick start tutorials Take quick start tutorials to learn how to perform specific tasks, such as refine data or build a model. These tutorials help you quickly learn how to do a specific task or set of related tasks. The quick start tutorials are categorized by task: * [Preparing data](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/quickstart-tutorials.html?context=cdpaas&locale=enprepare) * [Analyzing and visualizing data](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/quickstart-tutorials.html?context=cdpaas&locale=enanalyze) * [Building, deploying, and trusting models](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/quickstart-tutorials.html?context=cdpaas&locale=enbuild) * [Working with foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/quickstart-tutorials.html?context=cdpaas&locale=enprompt) Each tutorial requires one or more service instances. Some services are included in multiple tutorials. The tutorials are grouped by task. You can start with any task. Each of these tutorials provides a description of the tool, a video, the instructions, and additional learning resources. The tags for each tutorial describe the level of expertise ( Beginner , Intermediate , or Advanced ), and the amount of coding required ( No code , Low code , or All code ). After completing these tutorials, see the [Other learning resources](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/quickstart-tutorials.html?context=cdpaas&locale=enresources) section to continue your learning. Preparing data To get started with preparing, transforming, and integrating data, understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform. Your data preparation workflow has these basic steps: 1. Create a project. 2. If necessary, create the service instance that provides the tool you want to use and associate it with the project. 3. Add data to your project. You can add data files from your local system, data from a remote data source that you connect to, data from a catalog, or sample data. 4. Choose a tool to analyze your data. Each of the tutorials describes a tool. 5. Run or schedule a job to prepare your data. Tutorials for preparing data Each of these tutorials provides a description of the tool, a video, the instructions, and additional learning resources: Tutorial Description Expertise for tutorial [Refine and visualize data with Data Refinery](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-refine.html) Prepare and visualize tabular data with a graphical flow editor. Select operations to manipulate data. <br><br>Beginner<br><br>No code [Generate synthetic tabular data](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-generate-data.html) Generate synthetic tabular data using a graphical flow editor. Select operations to generate data. <br><br>Beginner<br><br>No code Analyzing and visualizing data To get started with analyzing and visualizing data, understand the overall workflow, choose a tutorial, and check out other learning resources for working with other tools. Your analyzing and visualizing data workflow has these basic steps: 1. Create a project. 2. If necessary, create the service instance that provides the tool you want to use and associate it with the project. 3. Add data to your project. You can add data files from your local system, data from a remote data source that you connect to, data from a catalog, or sample data. 4. Choose a tool to analyze your data. Each of the tutorials describes a tool. Tutorials for analyzing and visualizing data Each of these tutorials provides a description of the tool, a video, the instructions, and additional learning resources: Tutorial Description Expertise for tutorial [Analyze data in a Jupyter notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-analyze.html) Load data, run, and share a notebook. Understand generated Python code. <br><br>Intermediate<br><br>All code [Refine and visualize data with Data Refinery](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-refine.html) Prepare and visualize tabular data with a graphical flow editor. Select operations to manipulate data. <br><br>Beginner<br><br>No code Building, deploying, and trusting models To get started with building, deploying, and trusting models, understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform. The model workflow has three main steps: build a model asset, deploy the model, and build trust in the model. ![Overview of model workflow](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/ml-engineer-overview-wx.svg) Tutorials for building, deploying, and trusting models Each tutorial provides a description of the tool, a video, the instructions, and additional learning resources: Tutorial Description Expertise for tutorial [Build and deploy a machine learning model with AutoAI](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-build.html) Automatically build model candidates with the AutoAI tool. Build, deploy, and test a model without coding. <br><br>Beginner<br><br>No code [Build and deploy a machine learning model in a notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-build-notebook.html) Build a model by updating and running a notebook that uses Python code and the Watson Machine Learning APIs. Build, deploy, and test a scikit-learn model that uses Python code. <br><br>Intermediate<br><br>All code [Build and deploy a machine learning model with SPSS Modeler](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-build-spss.html) Build a C5.0 model that uses the SPSS Modeler tool. Drop data and operation nodes on a canvas and select properties. <br><br>Beginner<br><br>No code [Build and deploy a Decision Optimization model](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-do.html) Automatically build scenarios with the Modeling Assistant. Solve and explore scenarios, then deploy and test a model without coding. <br><br>Intermediate<br><br>No code [Automate the lifecycle for a model with pipelines](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-pipeline.html) Create and run a pipeline to automate building and deploying a machine learning model. Drop operation nodes on a canvas and select properties. <br><br>Beginner<br><br>No code Prompting foundation models To get started with prompting foundation models, understand the overall workflow, choose a tutorial, and check