https://github.com/royungar/sql_chicago_data_analysis_project
SQL-based data analysis project using SQLite, pandas, and Jupyter SQL magic commands. Analyzes crime, school, and census data from Chicago to explore socioeconomic patterns using filtering, joins, aggregation, and subqueries.
https://github.com/royungar/sql_chicago_data_analysis_project
aggregation census-data chicago crime-data data-analysis data-engineering education-data ibm jupyter-notebook pandas sql sqlite subqueries
Last synced: 9 days ago
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SQL-based data analysis project using SQLite, pandas, and Jupyter SQL magic commands. Analyzes crime, school, and census data from Chicago to explore socioeconomic patterns using filtering, joins, aggregation, and subqueries.
- Host: GitHub
- URL: https://github.com/royungar/sql_chicago_data_analysis_project
- Owner: royungar
- Created: 2025-07-24T11:35:22.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-28T00:40:26.000Z (11 months ago)
- Last Synced: 2025-07-28T02:30:49.989Z (11 months ago)
- Topics: aggregation, census-data, chicago, crime-data, data-analysis, data-engineering, education-data, ibm, jupyter-notebook, pandas, sql, sqlite, subqueries
- Language: Jupyter Notebook
- Homepage: https://www.linkedin.com/in/royungar/
- Size: 936 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SQL Chicago Data Analysis Project – IBM Data Engineering Professional Certificate
## Overview
This project explores and analyzes real-world datasets from the City of Chicago using SQL within a Jupyter Notebook.
It was completed as the final project for **Course 5 – Databases and SQL for Data Science with Python**
in the [IBM Data Engineering Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-engineer).
The analysis is performed using SQLite and `%sql` magic commands, combining Python and SQL to uncover insights about crime rates, public schools, and socioeconomic conditions in Chicago.
---
## Objectives
- Create and connect to a SQLite database using Jupyter and SQL magic
- Import CSV datasets into pandas and persist them as relational tables in SQLite
- Perform SQL queries to extract insights using filtering, grouping, and aggregation
- Apply subqueries and joins to analyze relationships across datasets
- Investigate correlations between crime rates, community demographics, and school safety
---
## Tools & Technologies
| Category | Tools/Technologies |
|-------------------|--------------------------------|
| **Languages** | Python, SQL |
| **Libraries** | pandas, sqlite3, ipython-sql |
| **Database** | SQLite |
| **Environment** | Jupyter Notebook |
| **File Formats** | CSV |
---
## Datasets Used
1. **Chicago Census Data**
Socioeconomic indicators by community area (2008–2012)
[Source](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2)
2. **Chicago Public Schools Progress Report Cards**
Performance and safety data (2011–2012)
[Source](https://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t)
3. **Chicago Crime Data**
Reported crimes from 2001 to present
[Source](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2)
> **Note:** Cleaned and reduced versions of the datasets were provided by IBM for educational use.
---
## Tasks Completed
- Created a new SQLite database and established a connection using `%load_ext sql`
- Loaded three CSV files into pandas DataFrames
- Created corresponding tables in SQLite using pandas `.to_sql()` method
- Queried data using SQL operations:
- Filtering with `WHERE` and `LIKE` clauses
- Joining and grouping data across tables
- Using aggregation functions (`COUNT`, `AVG`)
- Writing subqueries to solve analytical problems
- Answered 10 domain-specific questions involving:
- Crimes involving minors and children
- School safety scores grouped by type
- Community areas with high poverty and hardship
- Identifying the most crime-prone neighborhood
---
## Sample SQL Queries
```sql
-- Total number of crimes
SELECT COUNT(*) FROM CHICAGO_CRIME_DATA;
-- Community areas with per capita income < $11,000
SELECT COMMUNITY_AREA_NAME, COMMUNITY_AREA_NUMBER
FROM CENSUS_DATA
WHERE PER_CAPITA_INCOME < 11000;
-- Crimes committed at school locations (distinct types)
SELECT DISTINCT PRIMARY_TYPE
FROM CHICAGO_CRIME_DATA
WHERE LOCATION_DESCRIPTION LIKE '%SCHOOL%';
```
---
## Key Insights
- **Riverdale** had both the highest hardship index and highest percentage of households below the poverty line.
- **Austin (Community Area 25)** was the most crime-prone area in the dataset.
- Safety scores varied across school types, with high schools and elementary schools showing similar average ratings.
---
## Repository Structure
```plaintext
SQL_Chicago_Data_Analysis_Project/
├── README.md # Project overview and instructions
├── data/
│ ├── ChicagoCensusData.csv # Socioeconomic data by community area
│ ├── ChicagoCrimeData.csv # Crime records
│ └── ChicagoPublicSchools.csv # School performance and safety scores
├── images/ # Screenshots of database setup and SQL query results
│ ├── low_income_areas.png # Community areas with low per capita income (Problem 2)
│ ├── most_crime_prone_area.png # Area with the highest crime count using a subquery (Problem 10)
│ ├── school_crimes_summary.png # Types of crimes reported at school locations (Problem 5)
│ ├── table_creation_and_database_setup.png # Creating SQLite DB and loading tables using pandas and df.to_sql()
│ └── total_crimes_count.png # Total number of crimes in the dataset (Problem 1)
├── notebook/
│ └── SQL_Chicago_Data_Analysis_Project.ipynb # Final notebook with all tasks and query outputs
```
---
## License
This project was completed as part of the IBM Data Engineering Professional Certificate and is intended for educational use.
## Links
- Course Page - [Databases and SQL for Data Science with Python](https://www.coursera.org/learn/sql-data-science)
- [GitHub Profile](https://github.com/royungar)
- [GitHub Repository](https://github.com/royungar/SQL_Chicago_Data_Analysis_Project)