Before proceeding, it's crucial to understand LinkedIn's terms of service. Scraping data from LinkedIn without permission can violate their policies. Ensure compliance with LinkedIn's legal guidelines to avoid any legal issues.

Set up a local development environment with Python. Install necessary libraries such as `requests` for HTTP requests, `BeautifulSoup` for parsing HTML, and `boto3` for interacting with AWS services. You can do this using pip:

```bash
pip install requests beautifulsoup4 boto3
```

You'll need to authenticate your requests to LinkedIn. This typically requires simulating a login, which can be complex and may violate LinkedIn's terms. One common approach is to manually log in via a browser, inspect network requests to capture the session cookies, and use them in your script. Be cautious and ensure compliance with LinkedIn's policies.

Use Python to send HTTP requests to retrieve LinkedIn page content. Parse the HTML using BeautifulSoup to extract the required data, such as company details, job postings, or other public profiles. Here's a basic example of how to parse HTML:

```python
from bs4 import BeautifulSoup
import requests

url = 'https://www.linkedin.com/company/linkedin'
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')

# Extract data
company_name = soup.find('h1', class_='org-top-card-summary__title').text.strip()
```

Once you have the scraped data, process it by cleaning and structuring it into a desired format such as JSON or CSV. This will make it easier to upload to S3 and use in further data analysis.

```python
import json

data = {
'company_name': company_name,
# Add more fields as necessary
}

json_data = json.dumps(data)
```

Log in to your AWS Management Console and create an S3 bucket where you will store the scraped data. Note the bucket name and region for use in your Python script. Ensure that you have the necessary permissions to upload data to the bucket.

Use the `boto3` library to upload the processed data to your S3 bucket. Configure your AWS credentials either in your environment or through the AWS CLI.

```python
import boto3
from botocore.exceptions import NoCredentialsError

s3 = boto3.client('s3')

try:
s3.put_object(Bucket='your-bucket-name', Key='linkedin_data.json', Body=json_data)
print("Upload Successful")
except NoCredentialsError:
print("Credentials not available")
```

By following these steps, you can move data from LinkedIn pages to S3 without third-party connectors. Remember to always stay compliant with LinkedIn's terms and conditions when accessing their data.