Now for the fun stuff!

Here we have crafted a unique visualization utilizing the location data of a wholly fictionalized mobile phone user. 

With some reverse geocoding and location analysis, we find that this user frequents a residence, a nursery school, an elementary school, a business, a Trader Joe's, another residence, and a Costco.

With this information, we could infer that this user is a working parent and may be interested in home goods and services, automobile accessories, or children's services and products.

Here we have a dataset of 10,000 randomized location coordinates that we attributed to the consumers of a wholly fictionalized small business that can currently only sell to consumers within the US and Canada.

Using Python Geocoding, we can easily visualize which buyers reside within the US or Canada and find that out of 10,000 prospective customers, 4,854 leads are viable and should be pursued.

With this information, our client can appropriately order and allocate supplies and resources.

Here we have a report that we would run twice a year for the fictionalized Professor X. 

Professor X has a TA who tracks all of his student's grades throughout the semester and emails him five zip files. The professor has hired us to take these zip files, unzip them, and determine which students from each course are exempt from taking the final based on his provided criteria.

We deliver one zipped file with all exempt students organized by course bi-annually customized for his ease of use.

Here we have a report that we would run once a month for the fictionalized Mr. Z. 

Mr. Z has a high school aged son who has always wanted to work at Apple. His son is in the eleventh grade and is looking into possible majors and career tracks. 

Mr. Z has asked us to run a web-scraper through LinkedIn's job postings once a month to identify what job titles, locations, and salary information Apple is posting in hopes of helping his son make a data driven decision.

Here we have a report that we would run once a month for the fictionalized Ms. Q. 

Ms. Q runs a small business that purchases clothing and shoes before reselling them online. 

Mrs. Q provided our team with the Excel workbook she used to create POs manually and asked us to automate the work with macros in order to increase her productivity and remove human error.