📘 Data Science Docs + Examples (Beginner Friendly)

A complete beginner-friendly documentation project with runnable examples in NumPy, Pandas, Scikit-learn, Matplotlib and JavaScript equivalents.

Use this as your README.md for GitHub.

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🔰 Overview

This project contains easy-to-understand documentation and runnable examples for:

• NumPy (Array operations, math, reshaping)
• Pandas (DataFrames, CSV, cleaning)
• Scikit-learn (Regression, Classification, Training/Testing)
• Matplotlib (Basic plots)
• JavaScript Equivalents: math.js & TensorFlow.js

Perfect for beginners building a portfolio.

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📂 Folder Structure

📁 data-science-docs-examples
│
├── README.md
├── examples/
│   ├── numpy_basics.py
│   ├── pandas_basics.py
│   ├── sklearn_regression.py
│   ├── matplotlib_plot.py
│
├── js/
│   ├── mathjs_example.js
│   ├── tfjs_regression.js
│
└── datasets/
    └── sample.csv

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📘 1. NumPy Documentation + Examples

✨ What is NumPy?

NumPy is a Python library used for fast numerical computing.

Example 1: Creating Arrays

import numpy as np
arr = np.array([1, 2, 3])
print(arr)

Example 2: Array Math

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)
print(a * b)

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📘 2. Pandas Documentation + Examples

✨ What is Pandas?

Pandas is used for working with structured data.

Example 1: Create DataFrame

import pandas as pd
data = {"Name": ["A","B","C"], "Age": [20,25,30]}
df = pd.DataFrame(data)
print(df)

Example 2: Read CSV

df = pd.read_csv("datasets/sample.csv")
print(df.head())

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📘 3. Scikit-learn Examples

✨ Linear Regression

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1],[2],[3],[4]])
y = np.array([2, 4, 6, 8])

model = LinearRegression()
model.fit(X, y)
print(model.predict([[5]]))

✨ Train-Test Split

from sklearn.model_selection import train_test_split
import numpy as np

X = np.array([[1],[2],[3],[4],[5],[6]])
y = np.array([3,6,9,12,15,18])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print("Train: ", X_train)
print("Test: ", X_test)

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📘 4. Matplotlib Examples

import matplotlib.pyplot as plt

x = [1,2,3,4]
y = [2,4,6,8]

plt.plot(x, y)
plt.xlabel("X values")
plt.ylabel("Y values")
plt.title("Simple Plot")
plt.show()

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📘 5. JavaScript Data Examples

math.js Example

const math = require('mathjs');
let a = math.matrix([1,2,3]);
let b = math.matrix([4,5,6]);
console.log(math.add(a,b));

TensorFlow.js Regression

import * as tf from '@tensorflow/tfjs';

const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});

const xs = tf.tensor2d([1,2,3,4], [4,1]);
const ys = tf.tensor2d([2,4,6,8], [4,1]);

model.fit(xs, ys).then(() => {
  model.predict(tf.tensor2d([5], [1,1])).print();
});

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📊 Sample Dataset (datasets/sample.csv)

Name,Age,Score
A,20,85
B,22,90
C,25,88

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🚀 How to Run

Run Python Examples

python examples/numpy_basics.py
python examples/pandas_basics.py
python examples/sklearn_regression.py

Run JS Examples

node js/mathjs_example.js
node js/tfjs_regression.js

"create full code files".

Installation

Install dependencies using:

pip install -r requirements.txt

Quick Start

Run the examples:

python examples/pandas_basics.py
python examples/numpy_basics.py
python examples/matplotlib_basics.py
python examples/sklearn_basics.py

Troubleshooting

• Ensure Python 3.7+ is installed
• Install all dependencies from requirements.txt