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+++ b/docs/docs/integrate/data-science.md
@@ -4,31 +4,170 @@ sidebar_position: 4
 ---
 
 :::info
-Jupyter notebooks are a great way for data scientists to explore data, organize ad-hoc analysis, and share insights. We've included several template notebooks to help you get started working with OSO data. You can find these in the [community directory](https://github.com/opensource-observer/insights/tree/main/community/notebook_templates) of our insights repo. We encourage you to share your analysis and visualizations with the OSO community.
+Notebooks are a great way for data scientists to explore data, organize ad-hoc analysis, and share insights. We've included several template notebooks to help you get started working with OSO data. You can find these in the [community directory](https://github.com/opensource-observer/insights/tree/main/community/notebook_templates) of our insights repo. We encourage you to share your analysis and visualizations with the OSO community.
 :::
 
-## Getting Started
+## Setting Up Your Environment
 
 ---
 
-We will assume you have some familiarity with setting up a local Python environment and running Jupyter notebooks.
+We will assume you have some familiarity with setting up a local Python environment and running [Jupyter notebooks](https://jupyter.org/). We strongly recommend using Python >= 3.11. However, this guide should work for Python >= 3.7.
 
-In order to run the notebooks, you should have the following standard dependencies installed in your local environment:
+:::tip
+If this is your first time setting up a data science workstation, we recommend [downloading Anaconda](https://www.anaconda.com/download) and following their instructions for installation. Then, check out the [Jupyter docs](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/) to learn how to write your first notebooks.
+:::
+
+### Install Standard Dependencies
+
+You should have the following standard dependencies installed in your local environment. It is a best practice to use a Python virtual environment tool such as [virtualenv](https://virtualenv.pypa.io/en/latest/) to manage dependencies.
+
+#### For working with dataframes and vector operations
 
 - [pandas](https://pandas.pydata.org/)
+- [numpy](https://numpy.org/)
+
+#### For graph and statistical analysis
+
 - [networkx](https://networkx.org/)
+- [scikit-learn](https://scikit-learn.org/stable/)
+- [scipy](https://www.scipy.org/)
+
+#### For charting and data visualization
+
 - [matplotlib](https://matplotlib.org/)
 - [seaborn](https://seaborn.pydata.org/)
 - [plotly](https://plotly.com/python/)
-- [numpy](https://numpy.org/)
-- [scipy](https://www.scipy.org/)
-- [scikit-learn](https://scikit-learn.org/stable/)
 
-:::tip
-If you need help getting started, check out the [Jupyter docs](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/).
-:::
+### Install the BigQuery Python Client Library
+
+From the command line, install **google-cloud-bigquery** either directly on your machine or in a new virtual environment:
+
+```bash
+$ pip install google-cloud-bigquery
+```
+
+## Connecting to GCP
+
+---
+
+This section will walk you through the process of obtaining a GCP service account key and connecting to BigQuery from a Jupyter notebook. If you don't have a GCP account, you will need to create one (see [here](../getting-started/first-queries) for instructions).
+
+### Obtain a GCP Service Account Key
+
+From the [GCP Console](https://console.cloud.google.com/), navigate to the BigQuery API page by clicking **API & Services** > **Enabled APIs & services** > **BigQuery API**.
+
+You can also go there directly by following [this link](https://console.cloud.google.com/apis/api/bigquery.googleapis.com/).
+
+![GCP APIs](./gcp_apis.png)
+
+---
+
+Click the **Create Credentials** button.
+
+![GCP Credentials](./gcp_credentials.png)
+
+---
+
+You will prompted to configure your credentials:
+
+- **Select an API**: BigQuery API
+- **What data will you be accessing**: Application data (Note: this will create a service account)
+
+Click **Next**.
+
+---
+
+You will be prompted to create a service account:
+
+- **Service account name**: Add whatever name you want (eg, playground-service-account)
+- **Service account ID**: This will autopopulate based on the name you entered and give you a service account email
+- **Service account description**: Optional: describe the purpose of this service account
+
+Click **Create and continue**.
+
+---
+
+You will be prompted to grant your service account access to your project.
+
+- **Select a role**: BigQuery > BigQuery Admin
+
+![GCP Service Account](./gcp_service_account.png)
+
+Click **Continue**.
+
+---
+
+You can skip the final step by clicking **Done**. Or, you may grant additional users access to your service account by adding their emails (this is not required).
+
+You should now see the new service account under the **Credentials** screen.
+
+![GCP Credentials Keys](./gcp_credentials_keys.png)
+
+---
+
+Click the pencil icon under **Actions** in the **Service Accounts** table.
+
+Then navigate to the **Keys** tab and click **Add Key** > **Create new key**.
+
+![GCP Add Key](./gcp_add_key.png)
+
+---
+
+Choose **JSON** and click **Create**.
+
+It will download the JSON file with your private key info. You should be able to find the file in your downloads folder.
+
+Now you're ready to authenticate with BigQuery using your service account key.
+
+### Connect to BigQuery from a Jupyter Notebook
+
+From the command line, open a Jupyter notebook:
 
-## Structuring Your Analysis
+```bash
+$ jupyter notebook
+```
+
+A Jupyter directory will open in your browser. Navigate to the directory where you want to store your notebook.
+
+Click **New** > **Python 3** to open a new notebook. (Use your virtual environment if you have one.)
+
+---
+
+You should have a blank notebook open.
+
+Import the BigQuery client library and authenticate with your service account key.
+
+```python
+
+from google.cloud import bigquery
+import os
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '' # path to your service account key in your downloads folder
+client = bigquery.Client()
+```
+
+Try a sample query to test your connection:
+
+```python
+query = """
+    SELECT *
+    FROM `opensource-observer.oso_playground.collections`
+"""
+results = client.query(query)
+results.to_dataframe()
+```
+
+If everything is working, you should see a dataframe with the results of your query.
+
+### Safekeeping Your Service Account Key
+
+You should never commit your service account key to a public repository. Instead, you can store it in a secure location on your local machine and reference it in your code using an environment variable.
+
+If you plan on sharing your notebook with others, you can use a package like [python-dotenv](https://pypi.org/project/python-dotenv/) to load your environment variables from a `.env` file.
+
+Always remember to add your `.env` or `credentials.json` file to your `.gitignore` file to prevent it from being committed to your repository.
+
+## Running Your Own Analysis
 
 ---
 
@@ -42,24 +181,145 @@ These notebooks typically have the following structure:
 - **Analyze**: Perform analysis and generate visualizations.
 - **Export**: Export the results to a CSV or JSON file.
 
-## Fetching Data
+This next section will help you create a notebook from scratch, performing each of these steps using the OSO playground dataset.
+
+The example below fetches the latest code metrics for all projects in the OSO data warehouse and generates a scatter plot of the number of forks vs the number of stars for each project.
+
+You can find the full notebook [here](https://github.com/opensource-observer/insights/blob/main/community/notebooks/oso_starter_tutorial.ipynb).
+
+### Setup
+
+From the command line, create a new Jupyter notebook:
+
+```bash
+$ jupyter notebook
+```
+
+A Jupyter directory will open in your browser. Navigate to the directory where you want to store your notebook. Create a new notebook.
+
+Import the following dependencies:
+
+```python
+from google.cloud import bigquery
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+```
+
+Authenticate with your service account key:
+
+```python
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '' # path to your service account key in your downloads folder
+client = bigquery.Client()
+```
+
+### Query
+
+In this example, we will fetch the latest code metrics for all projects in the OSO data warehouse.
+
+```python
+query = """
+    SELECT *
+    FROM `opensource-observer.oso_playground.code_metrics_by_project`
+    ORDER BY last_commit_date DESC
+"""
+results = client.query(query)
+```
+
+We recommend exploring the data in [the BigQuery console](https://console.cloud.google.com/bigquery) before running your query in your notebook. This will help you understand the structure of the data and the fields you want to fetch.
+
+[![GCP Playground](./gcp_playground.png)](https://console.cloud.google.com/bigquery)
 
 ---
 
-In order to access OSO data directly, you will need access to BigQuery. See our guide for writing your first queries [here](../getting-started/first-queries).
+### Transform
+
+Once you have fetched your data, you can transform it into a format that is ready for analysis.
+
+Store the results of your query in a dataframe and preview the first few rows:
+
+```python
+df = results.to_dataframe()
+df.head()
+```
+
+Next, we will apply some basic cleaning and transformation to the data:
+
+- Remove any rows where the number of forks or stars is 0; copy to a new dataframe
+- Create a new column to indicate whether the project has had recent activity (commits in the last 6 months)
+
+```python
+dff = df[(df['forks']>0) & (df['stars']>0)].copy()
+dff['recent_activity'] = dff['commits_6_months'] > 0
+```
+
+### Analyze
+
+Now that we have our data in a format that is ready for analysis, we can perform some basic analysis and generate visualizations.
 
-Here's a sample BigQuery query that fetches the latest GitHub metrics for all projects in the OSO data warehouse:
+We'll start by creating a logscale scatter plot of the number of forks vs the number of stars for each project.
 
-```sql
-SELECT *
-FROM `opensource-observer.oso.github_metrics_by_project`
+```python
+fig, ax = plt.subplots(figsize=(10,10))
+sns.scatterplot(
+    data=dff,
+    x='stars',
+    y='forks',
+    hue='recent_activity',
+    alpha=.5,
+    ax=ax
+)
+ax.set(
+    xscale='log',
+    yscale='log',
+    xlim=(.9,10_000),
+    ylim=(.9,10_000)
+)
+ax.set_title("Ratio of stars to forks by project", loc='left')
 ```
 
-Here's a more complex query that fetches onchain user data:
+Here's a preview of the scatter plot:
+
+![Stars vs Forks](./stars_vs_forks.png)
+
+We can continue the analysis by differentiating between projects that have a high ratio of stars to forks and those that have a low ratio. We'll borrow [Nadia Asparouhova](https://nadia.xyz/oss/)'s term "stadium" and simplistically apply it to any project that has a higher than average ratio of stars to forks.
+
+```python
+dff['stars_to_forks_ratio'] = dff['stars'] / dff['forks']
+avg = dff['stars_to_forks_ratio'].mean()
+dff['stadium_projects'] = dff['stars_to_forks_ratio'] >= avg
+print(avg)
+```
+
+We can now perform further analysis to see how the distribution of stars to forks ratios is spread across the dataset, with a vertical line indicating the average ratio.
+
+```python
+fig, ax = plt.subplots(figsize=(15,5))
+sns.histplot(dff['stars_to_forks_ratio'], ax=ax)
+ax.axvline(avg, color='red')
+```
+
+Here's a preview of the histogram:
+
+![Stars to Forks Ratio](./histogram.png)
+
+Finally, we'll make a crosstab to see how many projects are classified as "stadium" and how many are not.
+
+```python
+pd.crosstab(dff['recent_activity'], dff['stadium_projects'])
+```
+
+At the time of writing, the crosstab shows 110 "stadium" projects with recent activity versus 829 non-stadium projects.
+
+Some of the top projects in the OSO dataset by this categorization include [IPFS](https://github.com/ipfs), [Trail of Bits](https://github.com/trailofbits), and [Solidity](https://github.com/ethereum/solidity).
+
+### Export
+
+When working with smaller datasets like this one, it's helpful to export the results of your analysis to a CSV or JSON file. This preserves a snapshot of the data for further analysis or sharing with others.
 
-```sql
-SELECT
-FROM placeholder
+```python
+dff.to_csv('code_metrics.csv', index=False)
 ```
 
 ## Creating Impact Vectors
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