Make $1000s in Minutes By Finding Leads
How I made quick money from a gig using Python
I became interested in data science over ten years ago. Before that I was tutoring a lot on the side, making over $3000 monthly during the school year. I often tutored groups of MBA students in challenging math-oriented classes, sometimes earning as much as $600 per hour.

However, after these students graduated, I needed to boost my tutoring revenue as high school students generally want one on one tutoring, so I went on Craigslist and fortunately found a posting that required 4,000,000 business email addresses. I didn’t have 4 million email addresses on hand, but I figured it would be a lucrative job so I decided to get creative. As a result, I scanned the internet for any database containing business email addresses that I could buy.
I discovered a database with over 64,000,000 corporate email addresses available for purchase. Of course, as people shift to new responsibilities, some data became invalid. However, about 80% of them remained valid. For roughly $500, I was able to obtain this database (sorry — I can’t share the source).
My client required data for specific types of firms. This is where data science enters the picture. I spent approximately 10 minutes writing a small Python script that used pandas to search for businesses in the required categories. Naturally, it took a bit longer to complete, and I had to send the results to my client.

Here is a quick overview of the code:
- Step 1, place the requested industries in a list.
- Step 2, get the dictionary of all the input files in my local directories by walking through the directories.
- Step 3, create an empty list named frames to store the relevant dataframe by industry.
- Step 4, Append the dataframes to the frames list by performing a str.contains search, by using ‘ |’.join(industries) I was able to search for all industries in a single search.
- Step 5, Concatenate the frames to create a single dataframe
- Step 6, Output data to multiple sheets in an excel file since the list contained more than 1M plus entries, exceeding the upward bound for an Excel worksheet.
- Step 7, Send data to the customer
- Step 8, the most important step — LOL. Collect payment from customers.
I included an example snippet of code to demonstrate how I read in all of my input files, constructed the Pandas DataFrame, and searched for relevant industries.
My new buyer sent me $6000 via PayPal the next day. He was even willing to pay the cost — so, I received the full $6K from this customer. Over the last two years, this consumer has needed assistance in several other areas. I’ve made at least $20,000 from him with very little effort on my part. Sometimes, I write a quick script that takes a few days to learn — but no real heavy lifting on my part.
I’ve also done a number of other searches for this customer, including locating all.ai domains on the internet and searching and categorizing them by keywords.
I even attended an AI conference in New York hosted by one of my customers. Now that I’m a member of the team, my customer turns to me for all comparable requests.
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