In the Google Cloud console, from the Navigation menu (Navigation menu), select Vertex AI > Dashboard.
Click Enable All Recommended APIs.
In the Navigation menu, click Workbench.
At the top of the Workbench page, ensure you are in the Instances view.
Click add boxCreate New.
Click Open Jupyterlab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.
Click the Python 3 icon to launch a new Python notebook.
Task 2. Clone a course repo within your JupyterLab interface
The GitHub repo contains both the lab file and solutions files for the course.
Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.
!git clone https://github.com/GoogleCloudPlatform/training-data-analyst
Task 2. Clone a course repo within your JupyterLab interface
The GitHub repo contains both the lab file and solutions files for the course.
- Copy and run the following code in the first cell of your notebook to clone the
training-data-analystrepository.
Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.
Task 3. Exploratory data analysis using Python and BigQuery
In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > launching_into_ml > labs and open python.BQ_explore_data.ipynb.
pop-up will appear for you to select a kernel. Choose the Python 3 (ipykernel) (Local) kernel from the options.
In the notebook interface, click Edit > Clear All Outputs.
Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code.
Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.
Hints may also be provided for the tasks to guide you along. Highlight the text to read the hints (they are in white text).
If you need more help, look at the complete solution at training-data-analyst > courses > machine_learning > deepdive2 > launching_into_ml > solutions and open python.BQ_explore_data.ipynb.
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