// server.js — run with: node server.js
const http = require("node:http");

const server = http.createServer((req, res) => {
  res.writeHead(200, { "Content-Type": "application/json" });
  res.end(JSON.stringify({ message: "Hello from Node.js" }));
});

server.listen(3000, () => console.log("http://localhost:3000"));

# pip install numpy
import numpy as np

marks = np.array([[72, 85, 90], [64, 70, 58], [88, 91, 95]])
print(marks.mean(axis=1).round(1))    # average per student: [82.3 64.  91.3]
print(marks.max(axis=0))              # top mark per subject: [88 91 95]
print((marks >= 70).sum())            # how many marks are 70 or above: 7

// Node handles many I/O tasks concurrently
const urls = [
  "https://jsonplaceholder.typicode.com/users/1",
  "https://jsonplaceholder.typicode.com/users/2",
];
const users = await Promise.all(urls.map(u => fetch(u).then(r => r.json())));
console.log(users.map(u => u.name));
// save as fetch.mjs and run with: node fetch.mjs (Node 18+)

import numpy as np

prices = np.array([499, 1299, 250, 899])
quantity = np.array([3, 1, 10, 2])
revenue = prices * quantity            # element-wise, no loop
print(revenue)                         # [1497 1299 2500 1798]
print(revenue.sum())                   # 7094
print(np.round(revenue / revenue.sum() * 100, 1))   # share of revenue %

# calc_service.py — pip install fastapi uvicorn numpy
import numpy as np
from fastapi import FastAPI

app = FastAPI()

@app.get("/emi")
def emi(principal: float, annual_rate: float, months: int):
    r = annual_rate / 12 / 100
    if r == 0:
        return {"emi": round(principal / months, 2)}
    value = principal * r * np.power(1 + r, months) / (np.power(1 + r, months) - 1)
    return {"emi": round(float(value), 2)}
# run: uvicorn calc_service:app --port 8000

// app.mjs — Node 18+
import http from "node:http";

http.createServer(async (req, res) => {
  const r = await fetch("http://localhost:8000/emi?principal=500000&annual_rate=9&months=60");
  const data = await r.json();
  res.writeHead(200, { "Content-Type": "application/json" });
  res.end(JSON.stringify({ emi: data.emi }));
}).listen(3000);
// Node handles the web request; Python + NumPy does the maths

What do you want to build?
☐ Websites, APIs, chat apps          → JavaScript + Node.js
☐ Reports, dashboards, analysis      → Python + NumPy/pandas
☐ Machine learning models            → Python + NumPy + ML libraries
☐ Both web and data                  → start with one, add the other later

Node.js path
1. JavaScript fundamentals
2. async/await and Promises
3. Node.js modules, npm
4. Express or another web framework
5. Databases and REST APIs
6. Project: a deployed API

NumPy / data path
1. Python fundamentals
2. NumPy arrays, shapes, vectorisation
3. pandas for tables
4. Visualisation
5. Statistics basics
6. Project: analysis of a real dataset