By Mohammad Sayem Chowdhury
Welcome to my personal journey through the world of Chicago’s public data! Using SQL and Python, I set out to explore, analyze, and draw insights from a variety of datasets, each offering a unique perspective on the city’s schools, communities, and public safety. This story brings together all my notebooks, reflecting my approach, discoveries, and the skills I developed along the way.
I began with Sayem_SQL_Connecting.ipynb, where I set up my environment and established connections to the databases. This foundational step allowed me to run SQL queries directly from my notebook, making the entire analysis process seamless and interactive.
With the connection in place, I moved to Sayem_SQL_Querying.ipynb and Sayem_SQLMagic_Tutorial.ipynb. Here, I experimented with different SQL queries, learned the nuances of the ipython-sql extension, and built confidence in extracting meaningful information from large datasets. These notebooks served as my playground for testing ideas and refining my SQL skills.
The real excitement began in Sayem_RealWorldData_SQL.ipynb and Sayem_SQL_Analysis.ipynb. I dove into actual datasets, exploring everything from school performance to community demographics. I focused on understanding the structure of the data, cleaning it where necessary, and using SQL functions to answer practical questions. Each query brought new insights and sometimes surprising results.
In Sayem_ChicagoSchools_SQLPractice.ipynb, I took a closer look at the Chicago Public Schools dataset. I examined school-level performance, attendance, and safety scores, and even explored how these factors varied across different community areas. This notebook was a deep dive into the challenges and successes of Chicago’s education system, all through the lens of data.
Sayem_ChicagoData_Analysis.ipynb brought everything together. Here, I combined socioeconomic indicators, school performance, and crime data to paint a holistic picture of Chicago’s neighborhoods. By joining tables and running complex queries, I uncovered patterns and relationships that might otherwise go unnoticed. This comprehensive analysis was both challenging and rewarding, pushing me to think critically about the data and its real-world implications.
Finally, in Sayem_PeerAssignment.ipynb, I tackled a peer assignment that tested my ability to apply everything I’d learned. This notebook showcases my problem-solving process, from understanding the requirements to delivering clear, well-documented solutions.
This journey has been a testament to the power of data and the importance of curiosity. Each notebook represents a step in my growth as a data analyst, and I’m proud to share my work and insights. If you’re interested in data, SQL, or the stories hidden within Chicago’s public records, I hope you find inspiration in my notebooks.
All content, analysis, and commentary are original and reflect my own perspective.