NumPy vs Flask: A Clear Comparison for Beginners

# NumPy basics

import numpy as np

# Create an array
a = np.array([1, 2, 3, 4, 5])

# Vectorized math
print(a * 2)            # [2, 4, 6, 8, 10]
print(a.mean(), a.std())  # mean and std

# 2D arrays
m = np.array([[1, 2, 3], [4, 5, 6]])
print(m.shape)           # (2, 3)

# Matrix multiplication
result = m @ m.T
print(result)

# Random numbers
r = np.random.rand(3, 3)
print(r)

Week 1 Review:
□ Do I understand arrays vs lists?
□ Can I create and reshape arrays?
□ Am I comfortable with slicing?

Week 2 Review:
□ Can I do element-wise math?
□ Do I understand broadcasting?
□ Have I used aggregations (sum, mean)?

Week 3 Review:
□ Do I understand matrix operations?
□ Can I use boolean masks?
□ Have I loaded data with np.loadtxt?

Week 4 Review:
□ Have I built a small data analysis?
□ Is it documented?
□ Do I feel ready for pandas?

# Flask basics

from flask import Flask, jsonify

app = Flask(__name__)

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

@app.route("/api/hello")
def api_hello():
    return jsonify({"message": "Hello, world!"})

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

Week 1 Review:
□ Do I understand routes and view functions?
□ Can I return text and JSON?
□ Is my dev server running?

Week 2 Review:
□ Do I understand Jinja2 templates?
□ Can I pass data to templates?
□ Have I built a small page?

Week 3 Review:
□ Can I handle forms (GET/POST)?
□ Do I use jsonify for APIs?
□ Have I tested endpoints with curl or Postman?

Week 4 Review:
□ Have I built a small Flask API?
□ Is it pushed to GitHub?
□ Do I feel ready for databases?

Data path (NumPy first):

Week 1-2 — Python fundamentals
Week 3-4 — NumPy arrays and math
Week 5-6 — pandas DataFrames
Week 7-8 — visualization (matplotlib, seaborn)
Week 9-12 — a data project + notebook

Web path (Flask first):

Week 1-2 — Python fundamentals
Week 3-4 — Flask routes and templates
Week 5-6 — forms and SQLAlchemy
Week 7-8 — JSON APIs
Week 9-12 — a small web app + deploy

NumPy — 90-Day Roadmap:
Month 1 — Basics
  Week 1: Arrays and dtypes
  Week 2: Slicing, masks, broadcasting
  Week 3: Math and aggregations
  Week 4: Loading and saving data

Month 2 — Data Skills
  Week 5: Linear algebra
  Week 6: Random numbers, statistics
  Week 7: Vectorization and performance
  Week 8: Intro to pandas on top of NumPy

Month 3 — Projects
  Week 9:  Mini data analysis project
  Week 10: Visualize with matplotlib
  Week 11: Write case study
  Week 12: Apply to data roles

Milestones:
□ 2-3 NumPy projects on GitHub
□ One well-documented notebook
□ Comfortable with vectorized math

Flask — 90-Day Roadmap:
Month 1 — Basics
  Week 1: Routes and views
  Week 2: Jinja2 templates
  Week 3: Forms and flash messages
  Week 4: Flask-SQLAlchemy

Month 2 — Real Features
  Week 5: Auth with Flask-Login
  Week 6: JSON APIs
  Week 7: Blueprints and configs
  Week 8: Deploy to Render or Railway

Month 3 — Projects
  Week 9:  Capstone project
  Week 10: API docs + tests
  Week 11: Portfolio write-up
  Week 12: Apply to web roles

Milestones:
□ 1 full Flask app + 1 API
□ Auth working
□ Public deployed URL

Daily Tracker:
Date: _________
☐ Morning routine completed
☐ Learning (1 hour)
☐ Hands-on coding (2 hours)
☐ Read docs (30 min)
☐ Notes + reflection
☐ Exercise completed
☐ Reviewed progress
☐ Planned tomorrow

Today's Accomplishments:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3. 

Weekly Tracker:
Week: _____

Goals for this week:
1. 
2. 
3. 

Daily Progress:
Mon: ☐☐☐
Tue: ☐☐☐
Wed: ☐☐☐
Thu: ☐☐☐
Fri: ☐☐☐
Sat: ☐☐☐
Sun: ☐☐☐

Weekly Wins:
1. 
2. 
3. 

Challenges & Solutions:
1. 
2. 

Next Week's Goals:
1. 
2. 
3. 

Monthly Tracker:
Month: _____

Monthly Goal:
_______________________________________________

Week 1 Progress: ☐☐☐
Week 2 Progress: ☐☐☐
Week 3 Progress: ☐☐☐
Week 4 Progress: ☐☐☐

Monthly Wins:
1. 
2. 
3. 

Skills Gained:
1. 
2. 
3. 

Projects Completed:
1. 
2. 
3. 

Applications Sent: _____
Interviews: _____
Offers: _____

Next Month's Goal:
_______________________________________________