What Projects Should a New Data Scientist Build First?

Month 1 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 1: Python and Pandas EDA
   - Week 2: Data cleaning
   - Week 3: Exploratory visualization
   - Week 4: Insights report
12:00 PM — Lunch break
1:00 PM — Practice (Kaggle, public datasets)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read documentation (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Can I load a dataset with Pandas?
□ Can I compute summary statistics?
□ Have I written an EDA notebook?

Week 2 Review:
□ Can I handle missing values?
□ Can I remove duplicates?
□ Have I produced a clean dataset?

Week 3 Review:
□ Can I create charts with Matplotlib/Seaborn?
□ Can I interpret trends?
□ Have I built 5 charts?

Week 4 Review:
□ Have I written an insights report?
□ Is Project 1 complete?
□ Is it on GitHub?

Month 2 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 5: Regression models
   - Week 6: Classification models
   - Week 7: Feature engineering
   - Week 8: Project 2
12:00 PM — Lunch break
1:00 PM — Practice (Kaggle competitions)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read documentation (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Do I understand linear regression?
□ Can I evaluate with RMSE/MAE?
□ Have I trained a regression model?

Week 6 Review:
□ Do I understand logistic regression?
□ Can I evaluate with accuracy/precision/recall?
□ Have I trained a classifier?

Week 7 Review:
□ Can I create new features?
□ Have I improved model performance?
□ Do I understand feature importance?

Week 8 Review:
□ Is Project 2 complete?
□ Is it documented?
□ Is it on GitHub?

Month 3 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 9: Project 3 (NLP or time series)
   - Week 10: Portfolio website
   - Week 11: Interview prep
   - Week 12: Applications and research
12:00 PM — Lunch break
1:00 PM — Networking and follow-ups
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Interview prep (30 min)
10:00 PM — Wind down

Week 9 Review:
□ Is Project 3 complete?
□ Does it use NLP or time series?
□ Is it documented?

Week 10 Review:
□ Is your portfolio live?
□ Are all projects linked?
□ Do you have write-ups for each project?

Week 11 Review:
□ Have I practiced SQL and Python?
□ Have I done mock interviews?
□ Have I reviewed case studies?

Week 12 Review:
□ Have I sent 25 quality applications?
□ Have I researched target companies?
□ Have I followed up?

Daily Tracker:
Date: _________
☐ Morning routine completed
☐ Deep work session 1 (3 hours)
☐ Deep work session 2 (2 hours)
☐ Practice exercises completed
☐ Project work completed
☐ Exercise completed
☐ Reviewed progress
☐ Planned tomorrow

Today's Accomplishments:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3. 

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

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. 

Applications Sent: _____
Interviews: _____
Offers: _____

Next Month's Goal:
_______________________________________________