freeCodeCamp Mini Projects — Data Analysis & Visualization

A collection of short Python projects completed while following freeCodeCamp tutorials. These mini-projects demonstrate core data-science skills: data cleaning, basic statistics, visualization, time-series analysis, and a simple prediction model. They are lightweight, well-suited for learning and showcasing on a CV or portfolio.

Projects included

demographic_data_analyzer.py — demographic data exploration and summary statistics (grouping, counts, proportions).

mean_var_std.py — simple statistical calculator that computes mean, variance and standard deviation for a dataset or list of numbers.

medical_data_visualizer.py — visual analysis of a medical dataset (histograms, boxplots, categorical comparisons).

sea_level_predictor.py — small regression example / predictive model for sea level / trend forecasting.

time_series_visualizer.py — visualization and basic analysis of time-series data (trends, seasonality, rolling statistics).

Key skills & technologies

Languages: Python (3.8+)

Libraries: pandas, NumPy, matplotlib, seaborn, scikit-learn (where applicable)

Concepts: Exploratory Data Analysis (EDA), basic statistics, data visualization, time-series exploration, data cleaning

Setup & quick run

Make sure you have Python 3.8+ installed.

Install recommended packages:

pip install pandas numpy matplotlib seaborn scikit-learn

Run a script:

python mean_var_std.py python demographic_data_analyzer.py python medical_data_visualizer.py python sea_level_predictor.py python time_series_visualizer.py

(If a script uses command-line arguments, open the script to see the exact usage comments.)

What to expect in each script

Clear, single-purpose scripts that are easy to read and adapt for different CSV files.

Inline comments explaining key steps: loading data, cleaning/filtering, analysis, and plotting.