Make a summary of this including tools and what they are used for: Watson Studio is a collaborative platform for the data science community. Data Analysts, Data Scientists, Data Engineers, Developers and Data Stewards all use Watson Studio to analyze data and construct models. With Watson Studio, you can create projects to organize data connections, data assets, and Jupyter notebooks. You can upload files to your project, and you can clean and shape the data to refine it for analysis. You can then create and share data visualizations via dashboards without using any coding, Watson Knowledge Catalog provides a secure enterprise catalog management platform to deliver trusted and meaningful data. and Watson Machine Learning offers tools and services to build, train, and deploy machine learning models. Cloud Pak for Data as a Service is a secure, seamless data access and integration platform that enables a single view of the data, no matter how many data sources you are working with. It includes IBM Watson Studio, IBM Watson Knowledge Catalog, and IBM Watson Machine Learning and more. In IBM Cloud Pak for Data, you can find step-by-step tutorials that show how to integrate data from multiple sources, build, deploy, test, and more. You can create collaborative data workspaces called Projects where your team can perform tasks for data science, data engineering, data curation, or machine learning and AI. And you can read Cloud Pak for Data news and updates. As you scroll down, you will see your work highlights, recent activities, and shortcuts. The Quick start section has links to get you started, and the navigation menu is on the upper left. In the navigation menu, Projects shows projects and jobs you have created. In Deployments, you can train, deploy, and manage machine learning models in collaborative deployment spaces. In Services, you can view different services associated with your account, and explore the catalog. and Gallery shows a collection of data sets, notebooks, industry accelerators, and sample projects. Now, on the Gallery page, you can search for projects and filter your search by clicking All filters You can filter by format and topics. You can then explore different project types. and you can also explore the data. Once you select the project, you will see the notebook, when it was last modified, the problem statement, and more. You can Add to your project or download the notebook to your system. One of the core services in Cloud Pak for Data, Watson Studio architecture is centered around the project. To create a project, select Work with data from the Cloud Pak for Data homepage. The Create a project popup will appear with options to create an empty project or create a project from a sample or file. Now when you click, Create an empty project, this page loads. Here you can manage all your projects. Use the context information and actions menu from anywhere in the project to view project information or load data files as assets. The RStudio integrated development environment (IDE) is included with IBM Watson Studio so you can use R notebooks and scripts in your projects. Launch RStudio from the Launch IDE menu after you create a project. The Overview page keeps you up to date with recent assets you created, the resource usage of the project, a readme for your project description, and a project history. To find project assets, click the Assets tab. The New Assets button lets you use data and tools to create analytical assets, like flows, visualizations, experiments, or notebooks, and the Import Asset button lets you import assets. A job is a way of running assets, such as Data Refinery flows or Notebooks in a project. Under the Jobs tab, you can run a job immediately or schedule a job. You can manage your projects under the Manage tab. There you can control access with user groups, define environments and monitor active runtimes, see resource usage, and tools and processing power using Services and integrations. In Service & Integration, you can associate IBM Cloud services with your project to add tools, environments, and capabilities. And you can also use third-party Integrations so your project can interact with external tools. For example, you can integrate with a Git repository to export the project, work with documents and notebooks in JupyterLab, or back up the project. From the Asset tab, you can use graphical builders, like: Dashboards Editor, to create a sharable visualization in the dashboard, or Data Refinery to create flows to refine data. Decision Optimization has the Decision Optimization model builder to solve scenarios, and with SPSS Modeler, you can quickly develop predictive models and deploy them into business operations to improve decision-making. The New asset tool type options also include Code editors, which provides a Jupyter notebook editor where you can analyze data or build models. A Jupyter notebook is a web-based environment for interactive computing. You can use notebooks to run small pieces of code that process your data, and you can immediately view the results of your computation. Now, this Jupyter notebook editor is largely used for interactive, exploratory data analysis programming and data visualization. You should use it if you are new to Jupyter notebooks. In this video, you learned that Watson Studio is a helpful tool for: analyzing and viewing data, cleaning and shaping data, embedding data into streams, and creating, training, and deploying. Learning Watson Studio promotes career growth. Learning Watson Studio is easy and requires no special skills. And Watson Studio offers many available resources.