NumPy or Git and GitHub What Beginners Should Pick First

import numpy as np

# Create arrays
a = np.array([1, 2, 3, 4, 5])
b = np.array([10, 20, 30, 40, 50])

# Vectorised operations — no loops needed
print(a + b)          # [11 22 33 44 55]
print(a * 2)          # [ 2  4  6  8 10]
print(a.mean())       # 3.0
print(a.std())        # 1.4142...

# Multi-dimensional
matrix = np.array([[1, 2, 3],
                   [4, 5, 6]])
print(matrix.shape)   # (2, 3)
print(matrix.T)       # transpose

import numpy as np

# A simple linear model, NumPy only
X = np.random.rand(100, 3)        # 100 samples, 3 features
y = X @ np.array([2.0, -1.0, 0.5]) + 0.1

# Closed-form least squares
X_b = np.c_[np.ones(X.shape[0]), X]        # add bias column
theta_best = np.linalg.inv(X_b.T @ X_b) @ X_b.T @ y

print("Learned weights:", theta_best)

# The same numeric engine — in a few lines, no loops
# Every ML framework below pandas uses NumPy-compatible
# arrays at this level.

# Install Git once (from git-scm.com), then configure
git config --global user.name "Your Name"
git config --global user.email "you@example.com"

# Create your first repo
mkdir my-numpy-project
cd my-numpy-project
git init

# Write a file
echo "print('hello numpy')" > main.py

# Stage and commit
git add main.py
git commit -m "First commit: hello numpy"

# Create a repo on github.com, then connect it
git remote add origin https://github.com/you/my-numpy-project.git
git branch -M main
git push -u origin main

# The daily loop — once Git is set up

# 1. Pull latest changes (if working with others)
git pull

# 2. Make changes to your files
#    (edit main.py, add data.csv, etc.)

# 3. See what changed
git status

# 4. Stage and commit
git add .
git commit -m "Add data cleaning script"

# 5. Push to GitHub
git push

# Branching — for larger features
git checkout -b feature/numpy-stats
# ... work ...
git add .
git commit -m "Add numpy statistics helper"
git push -u origin feature/numpy-stats

# Then open a Pull Request on github.com

Beginner plan — NumPy first, Git very soon after

Week 1 — NumPy basics
  - Arrays, shapes, dtypes, indexing, slicing
  - Vectorised operations and broadcasting
  - Read/write .npy and .csv files

Week 2 — NumPy for real data
  - Stats: mean, std, quantiles
  - Linear algebra: dot, matmul, solve
  - A small analysis project in a .py file

Week 3 — Git fundamentals
  - git init, status, add, commit, log
  - Understand "working directory / staging / history"
  - Push your first repo to GitHub

Week 4 — Put them together
  - Refactor your NumPy project into a GitHub repo
  - Add a README describing the project
  - Commit in small, meaningful messages

After week 4
  - pandas → matplotlib → scikit-learn
  - Keep using Git for every new project

# Your first NumPy project, properly tracked on GitHub

# 1. Create the folder and repo
mkdir numpy-stats-tool
cd numpy-stats-tool
git init

# 2. Write a minimal project
#    (files: numpy_stats.py, README.md, requirements.txt)
cat > requirements.txt << 'EOF'
numpy
EOF

cat > numpy_stats.py << 'EOF'
import numpy as np

def summary(x):
    x = np.asarray(x, dtype=float)
    return {
        "n": x.size,
        "mean": float(x.mean()),
        "std": float(x.std()),
        "min": float(x.min()),
        "max": float(x.max()),
    }

if __name__ == "__main__":
    print(summary([1, 2, 3, 4, 5]))
EOF

# 3. Commit it
git add .
git commit -m "Initial commit: numpy summary helper"

# 4. Push to GitHub
#    (create the repo on github.com first, then:)
git remote add origin https://github.com/you/numpy-stats-tool.git
git branch -M main
git push -u origin main

# 5. Make one small change, commit, push — repeat
#    That repetition is how Git sticks.