A Complete Hands-on Learning Repository by Nguyen Tin Tin Do
This repository contains step-by-step Python tutorials and examples covering the core concepts of Data Science — from data cleaning and statistics to regression and visualization.
Each file is designed for learning-by-doing, helping you understand how data moves from raw input to actionable insight.
🎯 Goal: Build a strong foundation in Data Science through clean, reproducible, and explainable code.
| Category | Description | Example Files |
|---|---|---|
| Data Preparation | Cleaning, transforming, and loading datasets | Clean_data.py, Prepare_data.py, Dataframe.py |
| Statistics & Math | Compute statistical metrics, variance, and correlation | Statistics-data-science.py, calculate-var-statistics.csv |
| Linear Functions & Regression | Implementing and visualizing linear relationships | Linear_Functions.py, Plotting_Linear_Functions.py |
| Regression Analysis | Full pipeline for model fitting and coefficient extraction | Linear-Regression.py, Regression-Table.py, Regression-Table-R-Squared.py |
| Case Studies & Examples | Practical regression applications with CSV data | Linear-Regression-Case.py, data.csv |
| Visualization | Plotting and interpreting regression trends | Regression-Table-Coefficients.py, Regression-Table-P-Values.py |
- Languages: Python
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn
- Data Handling: CSV, DataFrames
- Visualization: Regression plots, Correlation heatmaps
- Environments: Jupyter Notebook, VS Code, Anaconda
Clone the repo and execute scripts in your preferred environment.
# Clone repository
git clone https://github.com/NguyenTin2026/Data-Science-Tutorial-Step-By-Step-.git
# Move into directory
cd Data-Science-Tutorial-Step-By-Step-
# Run a sample Python script
python Linear-Regression.py