FreeCodeCamp Data Analysis with Python

Compiling my work for the FreeCodeCamp Data Analysis with Python Certification, this repository includes completed projects and supplementary learning resources. It should be noted that certain learning materials, specifically Jupyter Notebooks within designated folders (notebooks), are direct extractions from the FreeCodeCamp course content, used herein for educational demonstration and appropriate citation.

Table of Contents

• Projects
• Learning Resources / Notebooks
• Setup and Installation
• Contact

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Projects

This directory (Projects/) contains the main certification projects required by FreeCodeCamp. Each project addresses a specific data analysis challenge, demonstrating skills in data manipulation, visualization, and statistical computation.

Access Projects README here for details

• Project 1: Page View Time Series Visualizer Processes fcc-forum-pageviews.csv to create visualizations of daily forum page views, including line plots, bar charts, and box plots.

  • Files:
    • Projects/Page View Time Series Visualizer/time_series_visualizer.py
    • Projects/Page View Time Series Visualizer/fcc-forum-pageviews.csv
    • Projects/Page View Time Series Visualizer/line_plot.png (generated)
    • Projects/Page View Time Series Visualizer/bar_plot.png (generated)
    • Projects/Page View Time Series Visualizer/box_plot.png (generated)
  • Key Learning Points:
    • Time series data handling and cleaning.
    • Data visualization with Matplotlib and Seaborn.
    • Creating different types of plots to represent trends and distributions.

• Project 2: Demographic Data Analyzer Analyzes demographic data from adult_data.csv to calculate various statistics, including race counts, average age by gender, education percentages, and more.

  • Files:
    • Projects/demographic_data_analyzer/demographic_data_analyzer.py
    • Projects/demographic_data_analyzer/adult_data.csv
  • Key Learning Points:
    • Data loading and initial exploration with Pandas.
    • Filtering, grouping, and aggregation of data.
    • Conditional logic for data analysis.

• Project 3: Mean-Variance-Standard Deviation Calculator Implements a function to calculate the mean, variance, standard deviation, max, min, and sum of a 3x3 matrix (or a flattened version) using NumPy.

  • Files:
    • Projects/mean_std_var/stats_calc.py
  • Key Learning Points:
    • Efficient numerical operations with NumPy arrays.
    • Reshaping and flattening arrays.
    • Handling ValueError for invalid input.

• Project 4: Sea Level Predictor Predicts future sea level rise using historical data from epa-sea-level.csv. This project involves scatter plots, line of best fit calculation, and extrapolation using linear regression.

  • Files:
    • Projects/sea_level_predictor/sea_level_predictor.py
    • Projects/sea_level_predictor/epa-sea-level.csv
    • Projects/sea_level_predictor/sea_level_plot.png (generated)
  • Key Learning Points:
    • Linear regression for trend analysis and prediction.
    • Scatter plots and customizing plot elements.
    • Understanding and interpreting regression results.

• Project 5: Medical Data Visualizer Processes medical examination data from medical_examination.csv to create visualizations, including a categorical plot (catplot) and a heatmap of correlations.

  • Files:
    • Projects/medical_data_visualizer/medical_data_visualizer.py
    • Projects/medical_data_visualizer/medical_examination.csv
    • Projects/medical_data_visualizer/catplot.png (generated)
    • Projects/medical_data_visualizer/heatmap.png (generated)
  • Key Learning Points:
    • Data cleaning and preparing data for visualization.
    • Creating categorical plots with Seaborn.
    • Calculating and visualizing correlations using heatmaps.

Learning Resources / Notebooks

This section holds various materials used for learning and practising data analysis concepts.

• notebooks/: Contains Jupyter Notebooks related to the topics mentioned below, which are directly extracted from the Data Analysis with Python FCC Course.
• JupyterNotebooks/: Contains Jupyter Notebooks used for exploration, learning, and demonstrations of data analysis concepts.
• NumPy/: Exercises and examples specifically focused on the NumPy library.
• Pandas/: Exercises and examples specifically focused on the Pandas library.

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Setup and Installation

To set up and run the code in this repository:

1. Clone the repository:

   git clone https://github.com/GovindhKishore/fcc-data-analysis-with-python.git
   cd fcc-data-analysis-with-python

2. Install dependencies (for the main projects): The core projects (in the Projects/ directory) have their dependencies listed in requirements.txt.

   cd Projects
   pip install -r requirements.txt
   cd .. # Go back to the root if you want to explore other folders

   Note: It is highly recommended to use a Python virtual environment to manage dependencies.

3. Execute the main script for each project:

   • Page View Time Series Visualizer: python "Projects/Page View Time Series Visualizer"/time_series_visualizer.py (This will generate the plot images).
   • Demographic Data Analyzer: python Projects/demographic_data_analyzer/demographic_data_analyzer.py
   • Mean-Variance-Standard Deviation Calculator: The Projects/mean_std_var/stats_calc.py file contains a function. You would typically import and call it in another script.
   • Sea Level Predictor: python Projects/sea_level_predictor/sea_level_predictor.py (This will generate the plot image).
   • Medical Data Visualizer: python Projects/medical_data_visualizer/medical_data_visualizer.py (This will generate the plot images).

Contact

Feel free to reach out if you have any questions or feedback.

• Name - govindhkishore7@gmail.com
• Project Link: https://github.com/GovindhKishore/fcc-data-analysis-with-python