Python Programming Career Options for Students in Gurugram

Data Science — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review learning goals
9:00 AM — Deep learning (3 hours)
   - Week 1: Python fundamentals
   - Week 2: NumPy & Pandas
   - Week 3: Data visualization
   - Week 4: First project
12:00 PM — Lunch break
1:00 PM — Practice on Kaggle / LeetCode
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read Python articles (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Do I know Python syntax, loops, and functions?
□ Can I work with lists, dicts, sets?
□ Have I written at least 20 small programs?

Week 2 Review:
□ Can I clean and filter data with Pandas?
□ Do I understand groupby and joins?
□ Have I completed 5 data wrangling tasks?

Week 3 Review:
□ Can I create line, bar, and scatter plots?
□ Can I tell a story with a dashboard?
□ Have I built 3 visualizations?

Week 4 Review:
□ Is my end-to-end project complete?
□ Is it documented with insights?
□ Is it on GitHub?

Web Development — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review learning goals
9:00 AM — Deep learning (3 hours)
   - Week 5: Django / Flask basics
   - Week 6: Build first web app
   - Week 7: APIs & authentication
   - Week 8: Deploy project
12:00 PM — Lunch break
1:00 PM — Code challenges & debugging
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read Django docs (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Can I set up a Django or Flask project?
□ Do I understand routing and views?
□ Have I built a simple CRUD app?

Week 6 Review:
□ Do I have models and a database?
□ Can users log in and log out?
□ Is the app complete?

Week 7 Review:
□ Have I built a REST API?
□ Do I understand JWT and permissions?
□ Is the API tested?

Week 8 Review:
□ Is the app deployed live?
□ Are secrets managed with env vars?
□ Is the code documented on GitHub?

AI/ML & Automation — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review learning goals
9:00 AM — Deep learning (3 hours)
   - Week 9: ML basics with scikit-learn
   - Week 10: Build ML project
   - Week 11: Automation with Python
   - Week 12: Portfolio & applications
12:00 PM — Lunch break
1:00 PM — Kaggle / scripting practice
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read ML tutorials (30 min)
10:00 PM — Wind down

Week 9 Review:
□ Can I train a classification model?
□ Do I understand train/test split and metrics?
□ Have I built 2 ML models?

Week 10 Review:
□ Is my end-to-end ML project complete?
□ Is the model saved and loadable?
□ Is it deployed or documented?

Week 11 Review:
□ Have I automated at least 3 real tasks?
□ Do I understand file and API automation?
□ Is the code reusable?

Week 12 Review:
□ Is my GitHub portfolio polished?
□ Is my resume updated for AI/ML roles?
□ Have I sent 20+ applications?

Daily Tracker:
Date: _________
☐ Morning routine completed
☐ Python learning session 1 (3 hours)
☐ Python learning session 2 (2 hours)
☐ Coding practice (Kaggle / LeetCode)
☐ Project work completed
☐ Exercise completed
☐ Reviewed progress
☐ Planned tomorrow

Today's Accomplishments:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3. 

Weekly Tracker:
Week: _____ (Data Science / Web Dev / AI-ML)

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: _____ (1 / 2 / 3)

Monthly Goal:
_______________________________________________

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

Monthly Wins:
1. 
2. 
3. 

Skills Gained:
1. 
2. 
3. 

Portfolio Items:
1. 
2. 
3. 

Projects Completed: _____
Applications Sent: _____
Interviews: _____

Next Month's Goal:
_______________________________________________