Analyzing School District Data to Help Make Strategic Decisions

Pandas Challenge Project - Key Achievements:

Project Overview:
● Assumed the role of data analyst for the city's school district, contributing to strategic decision-making regarding school budgets and priorities.

Repository Setup:
● Established a new repository named pandas-challenge for the project, showcasing dedication to well-organized and version-controlled project management.
● Implemented a clear folder structure with a designated PyCitySchools folder, simplifying file navigation and enhancing project clarity.

Git Version Control:
● Utilized Git to clone the repository to the local machine, demonstrating proficiency in collaborative version control.
● Pushed changes to GitHub or GitLab, ensuring a collaborative and well-documented approach to the project.

Data Analysis - District Summary:
● Utilized Pandas and Jupyter Notebook to perform in-depth data analysis on district-wide standardized test results.
● Compiled a high-level snapshot of key metrics, including total unique schools, students, budget, average math and reading scores, % passing math, % passing reading, and % overall passing.
● Crafted a descriptive report that identified and communicated two observable trends based on the data.

Data Analysis - School Summary:
● Conducted an in-depth analysis of each school, summarizing key metrics such as school name, type, total students, total school budget, per-student budget, average math and reading scores, % passing math, % passing reading, and % overall passing.

Top and Bottom Performing Schools:
● Identified and presented the top 5 highest and lowest-performing schools based on % overall passing.

Math and Reading Scores by Grade:
● Computed average math and reading scores for each grade level (9th, 10th, 11th, 12th) at each school.

Scores by School Spending:
● Utilized bins to categorize school performance based on average spending per student.
● Calculated and presented the average math score, average reading score, % passing math, % passing reading, and % overall passing for each spending range.

Scores by School Size:
● Employed bins to categorize school performance based on school size (small, medium, or large).
● Generated a comprehensive summary showcasing the average math score, average reading score, % passing math, % passing reading, and % overall passing for each school size category.

Scores by School Type:
● Leveraged the per_school_summary DataFrame to create a new DataFrame called type_summary, depicting school performance based on school type.

Conclusion:
● Successfully fulfilled the role of Chief Data Scientist, delivering a comprehensive report on district-wide standardized test results to aid in strategic decision-making.
● Demonstrated advanced proficiency in Pandas, Jupyter Notebook, and Git, showcasing adaptability and expertise in data analysis and version control.
● Provided valuable insights through trend analysis and comprehensive metrics, contributing to informed decision-making processes for school budgets and priorities.