Bank Employee to Data Scientist A Realistic Career Timeline

Phase 1 — Daily Schedule (alongside your bank job):
6:00 AM — Wake up, exercise (30 min)
7:00 AM — Review goals for the day
7:30 AM — Study session 1 (1.5 hours)
   - Month 1: Python basics and SQL
   - Month 2: Statistics and Pandas
   - Month 3: Visualization and Project 1
9:00 AM — Work (banking job)
6:00 PM — Dinner and rest
8:00 PM — Study session 2 (1.5 hours)
   - Practice coding / SQL exercises
   - Work on mini-project
10:00 PM — Review notes, plan tomorrow
10:30 PM — Wind down

Phase 1 Review:
□ Can I write Python functions and loops?
□ Can I write JOINs, GROUP BY, subqueries?
□ Do I understand mean, spread, and correlation?
□ Can I clean a dataset in Pandas?
□ Have I built my first project?
□ Is it on GitHub with a README?

Banking advantage used:
1. Understanding finance data
2. Understanding risk concepts
3. Compliance and audit mindset

Strongest area: _________
Weakest area: _________
Action for Phase 2: _________

Phase 2 — Daily Schedule:
6:00 AM — Wake up, exercise (30 min)
7:00 AM — Review goals for the day
7:30 AM — Study session 1 (1.5 hours)
   - Month 4: ML fundamentals
   - Month 5: Model evaluation
   - Month 6: Feature engineering and tuning
9:00 AM — Work (banking job)
6:00 PM — Dinner and rest
8:00 PM — Study session 2 (1.5 hours)
   - Build and evaluate models
   - Read ML write-ups
10:00 PM — Review notes, plan tomorrow
10:30 PM — Wind down

Phase 2 Review:
□ Can I explain regression vs classification?
□ Do I know when to use precision vs recall?
□ Can I tune a model with cross-validation?
□ Have I documented one complete ML project?
□ Can I explain my model to a non-technical person?
□ Can I relate ML to banking use cases?

Banking use cases I explored:
1. Credit risk scoring
2. Fraud detection
3. Customer churn prediction

Strongest model type: _________
Weakest metric: _________
Action for Phase 3: _________

Phase 3 — Daily Schedule:
6:00 AM — Wake up, exercise (30 min)
7:00 AM — Review goals for the day
7:30 AM — Study session 1 (1.5 hours)
   - Months 7-8: Build portfolio projects
   - Month 9: Capstone project
   - Month 10: Resume and interview prep
   - Months 11-12: Applications and follow-ups
9:00 AM — Work (banking job)
6:00 PM — Dinner and rest
8:00 PM — Study session 2 (1.5 hours)
   - Project work / mock interviews
   - Networking on LinkedIn
10:00 PM — Review progress, plan tomorrow
10:30 PM — Wind down

Phase 3 Review:
□ Are my 2 portfolio projects complete?
□ Is my capstone documented end to end?
□ Is my resume showing measurable impact?
□ Is my LinkedIn updated?
□ Have I done at least 5 mock interviews?
□ Have I applied to 25+ quality roles?
□ Have I followed up on every interview?

Career metrics:
Applications: _____
Interviews: _____
Offers: _____

What worked best:
1.
2.

What to change:
1.
2.

Weekly Tracker:
Week: _____ (Phase 1 / 2 / 3)

Goals for this week:
1.
2.
3.

Study hours:
Mon: ___
Tue: ___
Wed: ___
Thu: ___
Fri: ___
Sat: ___
Sun: ___

Weekly Wins:
1.
2.
3.

Blockers:
1.
2.

Next week's goals:
1.
2.
3.

Monthly Tracker:
Month: _____ (1-12)

Phase: _____

Skills gained:
1.
2.
3.

Projects completed:
1.
2.
3.

Applications sent: _____
Interviews scheduled: _____
Offers: _____

What to improve next month:
1.
2.

Next month's goal:
_______________________________________________

Career Timeline (12-month view):

Month 1: Python and SQL basics       ☐
Month 2: Statistics and Pandas       ☐
Month 3: Visualization + Project 1   ☐
Month 4: ML foundations              ☐
Month 5: Model evaluation            ☐
Month 6: Advanced ML and tuning      ☐
Month 7: Portfolio project 2         ☐
Month 8: Portfolio project 3         ☐
Month 9: Capstone project            ☐
Month 10: Resume and interview prep  ☐
Month 11: Applications and interviews ☐
Month 12: Offers and negotiation     ☐

Overall progress: ___ / 12 months