Featured Case Study
Work with more than 2.5 million rows of simulated data from a New York Restaurant Group.
This New York restaurant group has eateries across New York City and Boston with a decidedly locavore flavor. They are rethinking every aspect of creating a meal from seed to service. Their mission is to embrace a deeper connection to our food system, supporting minority-run and small-scale farms along the way. They even started their own farm operations in upstate New York as a way to deeply understand the demands of the entire supply chain.
In one year alone, they purchased over 2 million pounds of produce from their 65 local farms. Do you think that creates a lot of data to analyze? Yes it does, in fact, using the restaurant data of more than 2.5 million sales transactions, you’ll apply your new-found Python skills to address some fundamental business questions such as:
- Which restaurant locations are our top performers and which are the weakest?
- How popular is delivery service in each of our locations?
- What are the average number of transactions per day by location and overall?
- Which locations are busier at lunch time and therefore require a different staffing regime?
- What was the ROI of the marketing investment made for a large special event in NYC on June 24?
- How did order volumes compare on the special event day as compared to our “normal” busy days in the summer months?
Later in the program, we’ll access Census data and connect to an API for weather data to analyze how demographics and weather affect sales. Finally, you’ll scrape information from this New York restaurant group website to learn more about how product offerings are organized and help determine which location is best for launching a new brand of healthy drinks.
Using this data set, you will get your hands dirty in the data using Python to help make decisions on where to invest and to know what’s working.
