A collection of data analysis projects completed through the Femanalytica × DataCamp Scholarship Program
DataCamp – Femanalytica Scholarship Recipient
Completed over 36 courses covering:
- Data Science & Analysis
- Statistical Modeling
- Machine Learning & AI
- Data Storytelling & Visualization
Through the Femanalytica × DataCamp online scholarship program, empowering women in data science and analytics.
This repository showcases 6 data analysis projects demonstrating proficiency in Python programming, data manipulation, statistical analysis, and data visualization.
Focus: Exploratory Data Analysis | Time Series
Skills: Data filtering, statistical analysis, trend identification
Analyzed Netflix movies from the 1990s to identify duration patterns and genre trends for nostalgic-style production decisions.
Key Findings:
- Most frequent movie duration: 94 minutes
- Identified 7 short action movies (< 90 min)
Focus: Function Development | Input Validation
Skills: Error handling, security best practices, modular design
Built a secure user registration system with comprehensive validation for names, emails, and passwords.
Highlights:
- Multi-level input validation
- Custom error handling
- Reusable function architecture
Focus: Data Preprocessing | Feature Engineering
Skills: Data cleaning, imputation, transformation
Prepared customer data for machine learning through systematic cleaning and feature engineering.
Techniques:
- Missing value imputation
- Feature scaling & encoding
- Data quality assurance
Focus: Market Analysis | Geospatial Data
Skills: EDA, pricing analysis, market insights
Analyzed NYC Airbnb listings to uncover pricing patterns and market dynamics.
Insights:
- Neighborhood pricing variations
- Room type preferences
- Market saturation indicators
Focus: Educational Data Analysis
Skills: Statistical analysis, comparative analysis
Examined SAT scores across NYC public schools to identify educational trends and disparities.
Analysis:
- Top-performing schools
- Borough-level comparisons
- Score distribution patterns
Focus: Data Visualization | Historical Analysis
Skills: Time series visualization, demographic analysis
Explored Nobel Prize winner demographics and trends from 1901 to present.
Visualizations:
- Category trends over time
- Geographical distribution
- Gender representation evolution
- Language: Python 3.8+
- Data Manipulation: pandas, numpy
- Visualization: matplotlib, seaborn, plotly
- Machine Learning: scikit-learn
- Environment: Jupyter Notebook
- Exploratory Data Analysis (EDA)
- Data Cleaning & Preprocessing
- Statistical Analysis
- Data Visualization
- Feature Engineering
- Function Development
- Error Handling
- Market Analysis
# Python 3.8 or higher required
python --version
# Install dependencies
pip install pandas numpy matplotlib seaborn scikit-learn plotly jupyter-
Clone this repository
git clone https://github.com/Jimmy-JayJay/data-camp-projects.git cd data-camp-projects -
Navigate to any project folder
cd "Investigating Netflix Movies"
-
Launch Jupyter Notebook
jupyter notebook notebook.ipynb
-
Run cells sequentially
data-camp-projects/
├── Creating Functions to Register App Users/
│ ├── notebook.ipynb
│ ├── python_functions.py
│ └── README.md
├── Customer Analytics - Preparing Data for Modeling/
│ ├── notebook.ipynb
│ ├── customer_train.csv
│ └── README.md
├── Exploring Airbnb Market Trends/
│ ├── notebook.ipynb
│ ├── data.zip
│ └── README.md
├── Exploring NYC Public School Test Result Scores/
│ ├── notebook.ipynb
│ └── README.md
├── Investigating Netflix Movies/
│ ├── notebook.ipynb
│ ├── netflix_data.csv
│ └── README.md
├── Visualizing the History of Nobel Prize Winners/
│ ├── notebook.ipynb
│ └── README.md
└── README.md
Special thanks to:
- Femanalytica for providing the scholarship opportunity
- DataCamp for the comprehensive learning platform
- Portfolio: jimmy-matewere.vercel.app
- LinkedIn: Jimmy Matewere
- GitHub: @Jimmy-JayJay
This project is licensed under the MIT License - see the LICENSE file for details.
Jimmy Edward Jr Matewere
Empowering climate action through data science