Best Data Science Training Institute in Faridabad for 2026

Phase 1 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review the day's learning goals
9:00 AM — Live class / recorded lecture (2 hours)
   - Week 1: Python for data
   - Week 2: statistics
   - Week 3: SQL for data science
   - Week 4: exploratory data analysis
12:00 PM — Lunch break
1:00 PM — Hands-on practice (2 hours)
   - Code with Pandas, write SQL, analyse datasets
3:00 PM — Mentor check-in / doubt session (30 min)
4:00 PM — Assignment work (1 hour)
5:00 PM — Review notes, plan tomorrow
6:00 PM — Break / exercise
8:00 PM — Read data blogs (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Can I manipulate data with Pandas?
□ Do I understand NumPy arrays?
□ Have I visualised data with Matplotlib?

Week 2 Review:
□ Do I know descriptive statistics?
□ Can I run a hypothesis test?
□ Have I analysed 2 datasets?

Week 3 Review:
□ Can I write JOINs and aggregations?
□ Do I understand window functions?
□ Have I solved 40 SQL problems?

Week 4 Review:
□ Can I do a full EDA on a dataset?
□ Can I present insights clearly?
□ Have I published 1 EDA notebook?

Phase 2 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review the day's learning goals
9:00 AM — Live class / recorded lecture (2 hours)
   - Week 5: supervised learning
   - Week 6: unsupervised & ensembles
   - Week 7: deep learning
   - Week 8: end-to-end project
12:00 PM — Lunch break
1:00 PM — Hands-on practice (2 hours)
   - Train models, evaluate results, deploy demos
3:00 PM — Mentor check-in / doubt session (30 min)
4:00 PM — Assignment work (1 hour)
5:00 PM — Review notes, plan tomorrow
6:00 PM — Break / exercise
8:00 PM — Read ML blogs / papers (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Can I build a regression model?
□ Can I build a classification model?
□ Do I understand evaluation metrics?

Week 6 Review:
□ Can I apply clustering?
□ Do I understand ensemble methods?
□ Have I built a full ML pipeline?

Week 7 Review:
□ Do I understand neural network basics?
□ Can I train a model in TensorFlow or PyTorch?
□ Have I evaluated it properly?

Week 8 Review:
□ Is my end-to-end project complete?
□ Is it deployed and live?
□ Is it documented on GitHub?

Phase 3 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review the day's goals
9:00 AM — Portfolio / interview prep (2 hours)
   - Week 9: Portfolio & case studies
   - Week 10: Mock interviews
   - Week 11: Placement drives
   - Week 12: Evaluate offers
12:00 PM — Lunch break
1:00 PM — Applications & follow-ups (2 hours)
3:00 PM — Mentor check-in / feedback (30 min)
4:00 PM — Interview practice (1 hour)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Break / exercise
8:00 PM — Practice ML questions (30 min)
10:00 PM — Wind down

Week 9 Review:
□ Have I published 2 ML projects?
□ Are they documented with problem, approach, results?
□ Are they on GitHub with READMEs?

Week 10 Review:
□ Have I done 5 mock interviews?
□ Can I solve ML coding questions?
□ Can I explain my projects clearly?

Week 11 Review:
□ Have I attended 3+ placement drives?
□ Have I sent follow-ups?
□ Have I practiced more?

Week 12 Review:
□ Have I evaluated offers?
□ Have I negotiated?
□ Have I accepted a role?

ML Project Brief:

Project name: _______________________________
Problem it solves: ____________________________
Dataset source: _______________________________
Model type: ☐ Regression ☐ Classification ☐ CNN ☐ NLP

Features:
1. 
2. 
3. 

Metrics:
- Accuracy / RMSE / F1: ____
- Baseline comparison: ____

Deployment:
☐ Streamlit ☐ FastAPI ☐ Flask ☐ Hugging Face Spaces

Deliverables:
☐ Notebook / training code
☐ Deployed demo URL
☐ GitHub repo with README
☐ Short write-up of results

Weekly Learning Tracker:

Week: _____
Topics covered:
1. 
2. 
3. 

Hours spent: _____
Models trained: _____
Assignments completed: _____

What I learned:
1. 
2. 

What needs more practice:
1. 
2. 

Next week's focus:
_______________________________

Interview Prep Tracker:

Company: _______________________________
Role: __________________________________
Interview date: _________________________

Topics to revise:
☐ ML theory (bias-variance, overfitting)
☐ Python & scikit-learn
☐ Deep learning (CNNs, transformers)
☐ Model evaluation metrics
☐ System design for ML
☐ SQL basics

Mock interview score: ____ / 10
Feedback:
_______________________________

Follow-up sent: ☐ Yes ☐ No