A project to showcase my self-taught SQL skills using the NYC 311 dataset.
The 311 Dataset is provided by NYC Open Data. The entire dataset includs all service requests from 2010 to present, and is updated daily. However, for my project I chose to limit my analysis to entries made in the year 2019, in order to decrease the file size that I would be processing.
The full dataset, table schema, and data dictionary can be found at: https://data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9
In addition to limiting the data I worked with to service requests created in 2019, I also downloaded the data in separate sections in order to create 2 tables. I organized the data sections by "general"-- consisting of the information like unique key, dates created and closed, agency handling the request, complaint type etc., and "location" -- consisting of information like borough, zipcode, address etc.
-GROUP BY, HAVING -JOINS -BASIC SUBQUERIES IN SELECT, FROM, and WHERE -COMPLEX SUBQUERIES: CORRELATETD and NESTED -CASE WHEN: BASIC, use with WHERE CLAUSE, with AGGREGATES -DATE & TIME -CTE or TEMP TABLE -WINDOW FUNCTIONS: BASIC, SLIDING, ADVANCED
- Most fields in this dataset are TEXT datatype. This limited by ability to perform mathematical operations.
- Since I divided one dataset into two tables using just one primary key, I was limited in the types of JOINS I could perform, as well as set operations like UNION.
The purpose of this project was exploratory data analysis. Some findings I'd like to highlight include:
- In 2019, NYPD responded to 32% of all 311 calls (perhaps not surprising), followed by HPD (Housing Preservation & Development) at 18%, DSNY (Department of Sanitation) at 17%, and DOT (Department of Transportation) at 11%.
- The 5 most frequent complaints in 2019 were: 'Noise- Residential', 'Request Large Bulky Item Collection', 'HEAT/HOT WATER', 'Illegal Parking', 'Blocked Driveway'
- In 2019, it took an average of 4 days for a service request to be resolved, and a maximum of 30 days.
Finally, please take a look at the attached csv files which show a selection of Views.
Thank you for viewing, I hope you enjoy!!