This repository contains the five required projects for the freeCodeCamp Data Analysis with Python Certification. Each project demonstrates core data analysis skills using Python, pandas, NumPy, Matplotlib, Seaborn, and SciPy.
- ✅ Issued by: freeCodeCamp
- 📜 Credential: Data Analysis with Python Certification
├── demographic-data-analyzer/
│ └── demographic_data_analyzer.py
│ └── README.md
│
├── mean-variance-standard-deviation-calculator/
│ └── mean_var_std.py
│ └── README.md
│
├── medical-data-visualizer/
│ └── catplot.png
│ └── heatmap.png
│ └── medical_data_visualizer.py
│ └── medical_examination.csv
│ └── README.md
│
├── page-view-time-series-visualizer/
│ └── bar_plot.png
│ └── box_plot.png
│ └── fcc-forum-pageviews.csv
│ └── line_plot.png
│ └── README.md
│ └── time_series_visualizer.py
│
├── sea-level-predictor/
│ └── README.md
│ └── sea_level_plot.png
│ └── sea_level_predictor.py
│
└── README.md ← (This file)
- Analyzes U.S. Census data to compute statistics on race, education, income, work hours, and country-based insights.
- Tools used: pandas
- Reshapes a list into a 3x3 NumPy array and computes mean, variance, standard deviation, max, min, and sum across axes.
- Tools used: NumPy
- Visualizes medical data using categorical plots and heatmaps.
- Computes BMI, normalizes data, and reveals correlations between health indicators.
- Tools used: pandas, seaborn, matplotlib
- Visualizes forum page view trends using line, bar, and box plots.
- Shows monthly and yearly patterns with outlier removal.
- Tools used: pandas, seaborn, matplotlib
- Uses linear regression to predict sea level rise through 2050.
- Plots historical data and best fit lines for all data and post-2000 data.
- Tools used: pandas, matplotlib, scipy.stats.linregress
- 📊 Data Cleaning and Transformation
- 📈 Exploratory Data Analysis (EDA)
- 🧮 Statistical Aggregation & Grouping
- 🖼️ Data Visualization with Seaborn & Matplotlib
- 📉 Linear Regression Modeling
- 🧹 Outlier Detection & Feature Engineering