NumPy vs Flask — Which Technology Should Beginners Learn First?

import numpy as np

# Basic array math
x = np.array([1, 2, 3, 4])
y = np.array([10, 20, 30, 40])

print(x + y)        # element-wise
print(x * 2)        # scalar multiply
print(x.mean())     # 2.5
print(x.reshape(2, 2))

Where NumPy is used:
- pandas (dataframes)
- scikit-learn (ML)
- TensorFlow / PyTorch (AI)
- Matplotlib (plotting)
- Scientific computing

from flask import Flask, jsonify

app = Flask(__name__)

@app.route("/")
def home():
    return "Hello, world!"

@app.route("/api/health")
def health():
    return jsonify({"status": "ok"})

if __name__ == "__main__":
    app.run(debug=True)

How to run Flask:
1. pip install flask
2. Save app.py
3. python app.py
4. Open http://127.0.0.1:5000

Next steps:
- Add routes
- Return JSON
- Connect a database

Data Science path:
1. Python + NumPy
2. pandas + matplotlib
3. SQL
4. scikit-learn
5. Portfolio projects
→ Data Analyst / Data Scientist

Backend path:
1. Python basics
2. Flask
3. SQL + databases
4. REST APIs
5. Deployment basics
→ Backend Developer

# Flask serving a NumPy-powered prediction
from flask import Flask, request, jsonify
import numpy as np

app = Flask(__name__)

@app.route("/api/predict", methods=["POST"])
def predict():
    values = np.array(request.json["values"])
    score = float(values.mean())
    return jsonify({"score": score})

if __name__ == "__main__":
    app.run(debug=True)

For full-stack data apps:
□ Python basics
□ NumPy / pandas
□ Flask basics
□ SQL
□ Deployment basics