Data Analysis Projects with Python

Overview

These hands-on projects cover data cleaning, visualization, and statistical analysis using NumPy, Pandas, Matplotlib, and Seaborn.

Projects from the "Data Analysis with Python" program at FreeCodeCamp, where I earned my certificate.

Projects Included

• Demographic Data Analyzer: Analysis of demographic data to uncover patterns and insights.
• Mean-Variance-Standard Deviation Calculator: Calculation of fundamental statistical metrics to understand data distribution.
• Medical Data Visualizer: Visualization of medical data to identify trends and anomalies.
• Page View Time Series Visualizer: Visualization of time series data to track page view trends over time.
• Sea Level Predictor: Predictive analysis of sea level changes based on historical data.

Methodology

1. Data Cleaning: Cleaning and processing data to ensure accuracy and consistency.
2. Exploratory Data Analysis (EDA): Conducting initial analysis to uncover patterns, anomalies, and insights.
3. Advanced Analysis: Using statistical techniques and visualizations to perform in-depth analysis.
4. Visualization: Developing comprehensive visualizations to effectively communicate findings.

Skills Utilized

• NumPy: Fundamental package for numerical computations in Python.
• Pandas: Data manipulation and analysis library.
• Matplotlib: Library for creating static, animated, and interactive visualizations.
• Seaborn: Statistical data visualization library built on top of Matplotlib.
• Python: Programming language used for all analysis and visualization tasks.

Key Learnings

• Data Preparation: Mastery of techniques for cleaning and preparing data for analysis.
• Visualization Techniques: Enhanced ability to create detailed and informative visualizations.
• Statistical Analysis: Increased capacity to apply statistical methods and interpret complex datasets.

Contact

If you have any questions or would like more information, feel free to contact me.