High School Data Science Projects
Overview
This is a collection of data science projects I completed for assignments during high school. These projects demonstrate fundamental data science techniques including data cleaning, exploratory data analysis, statistical modeling, and data visualization using Python’s scientific computing ecosystem.
Tools and Technologies
The projects utilize a comprehensive data science stack:
- Python - Core programming language
- Pandas - Data manipulation and analysis
- GeoPandas - Geospatial data processing
- NumPy - Numerical computing
- Scikit-learn - Machine learning algorithms
- Matplotlib - Static visualizations
- Seaborn - Statistical data visualization
Project Highlights
Data Analysis and Cleaning
Each project involved working with real-world datasets that required extensive cleaning and preprocessing. This included handling missing values, removing duplicates, normalizing data formats, and dealing with outliers.
Exploratory Data Analysis
Using visualization libraries like Matplotlib and Seaborn, I explored datasets to identify patterns, trends, and relationships. This involved creating histograms, scatter plots, correlation matrices, and distribution plots to better understand the underlying data structure.
Statistical Modeling
Applied various statistical techniques and machine learning algorithms from Scikit-learn to build predictive models. This included regression analysis, classification tasks, and clustering algorithms depending on the project requirements.
Geospatial Analysis
Some projects involved working with geographic data using GeoPandas, allowing for spatial analysis and the creation of choropleth maps and other geographic visualizations.
Learning Outcomes
Through these projects, I developed strong foundational skills in:
- Data wrangling and preprocessing
- Statistical analysis and hypothesis testing
- Machine learning model development and evaluation
- Data visualization and storytelling
- Python programming for data science
- Working with real-world messy datasets
Academic Context
These projects were completed as part of high school coursework, demonstrating early exposure to data science concepts and practical application of analytical techniques. They represent my initial steps into the field of data science and computational analysis.