Data Analysis with Python Certification Projects

This repository contains the Python projects completed as part of a Data Analysis with Python certification.

Projects:

1. Mean-Variance-Standard Deviation Calculator

• Description: A Python function that uses NumPy to calculate the mean, variance, standard deviation, max, min, and sum of the rows, columns, and flattened elements of a 3x3 matrix.
• Libraries/Functions:
  • NumPy (np): np.array, reshape, mean, var, std, max, min, sum, tolist.

2. Demographic Data Analyzer

• Description: (Based on provided context, details of this project are not fully clear. It likely involves analyzing demographic data using Pandas.)
• Libraries/Functions:
  • Pandas (pd): DataFrames, data loading (read_csv), data manipulation (filtering, calculations).

3. Medical Data Visualizer

• Description: Visualizes medical examination data using Pandas, Matplotlib, and Seaborn to understand patterns and relationships.
• Libraries/Functions:
  • Pandas (pd): DataFrames, data loading (read_csv), data manipulation.
  • Matplotlib (plt): Creating plots (figure, subplots, scatter, plot, xlabel, ylabel, title, legend, savefig).
  • Seaborn (sns): Statistical data visualization (catplot, heatmap).
  • NumPy (np): Array operations (e.g., masking for heatmap).

4. Page View Time Series Visualizer

• Description: Visualizes time series data of freeCodeCamp.org forum page views using Pandas, Matplotlib, and Seaborn to understand daily, yearly, and monthly trends.
• Libraries/Functions:
  • Pandas (pd): Time series data handling (setting index to date, creating year/month columns), data manipulation (melt, value_counts, groupby, unstack).
  • Matplotlib (plt): Line charts, bar charts, box plots (plot, bar, boxplot, xticks, tight_layout).
  • Seaborn (sns): Box plots (boxplot).

5. Sea Level Predictor

• Description: Analyzes a dataset of global average sea level change and uses linear regression from SciPy to predict sea level rise through the year 2050.
• Libraries/Functions:
  • Pandas (pd): Data loading (read_csv), data filtering.
  • Matplotlib (plt): Scatter plots, line plots (scatter, plot, xlabel, ylabel, title, legend, savefig).
  • SciPy (scipy.stats): Linear regression (linregress).
  • NumPy (np): Creating numerical ranges (arange).

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