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roadmap generator

phased plans. no fluff.

pick a goal. pick the route that fits your situation. get a real plan with milestones, projects, and mistakes to avoid.

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Data Science

from zero to junior data role

route summary — pick the framing that fits your situation

Scaler / IIIT-B PG Diploma. ~₹2–3L. 9–12 months.

progress0 / 8 milestones

early days. focus on phase 1.

0–3 months

Foundations

learn
  • ·Python basics (lists, dicts, functions, classes)
  • ·SQL — joins, group by, window functions
  • ·Statistics — distributions, hypothesis testing
  • ·Pandas + NumPy basics
build
  • ·1 data cleaning notebook (Kaggle Titanic-level)
  • ·1 SQL practice set (50+ queries on a real dataset)
resources
  • ·Krish Naik YouTube — Python + Stats
  • ·Mode SQL tutorial
  • ·StatQuest YouTube
  • ·Kaggle Learn
mistakes to avoid
  • ·Jumping to ML before SQL
  • ·Doing only notebooks, never deploying
milestones
3–6 months

Core analytics + visualization

learn
  • ·EDA workflow
  • ·Visualization — matplotlib, seaborn, Plotly
  • ·Tableau or Power BI
  • ·Intro to ML — regression, classification
build
  • ·1 dashboard published (Streamlit / Tableau Public)
  • ·1 EDA notebook on a real dataset with insights
resources
  • ·Coursera 'Google Data Analytics' (free audit)
  • ·Kaggle competitions
certs (if relevant)
  • ·Google Data Analytics Certificate (optional, entry signal)
mistakes to avoid
  • ·Skipping visualization fundamentals
  • ·Overfitting your portfolio to Kaggle
milestones
6–12 months

Real ML + end-to-end projects

learn
  • ·scikit-learn deeply
  • ·Feature engineering
  • ·Model evaluation and validation
  • ·Intro to deployment (FastAPI / Streamlit)
build
  • ·3 end-to-end projects (problem → data → model → deployment → writeup)
  • ·1 Kaggle competition top 25%
resources
  • ·Andrew Ng ML on Coursera
  • ·fast.ai (optional, DL track)
  • ·Made With ML
mistakes to avoid
  • ·Stacking models without understanding
  • ·Not writing about what you built
milestones
1–3 years

Specialize + ship

learn
  • ·Pick a domain — finance / e-commerce / healthcare
  • ·Production data engineering basics (dbt, Airflow)
  • ·Cloud (AWS/GCP) basics
  • ·Communication + stakeholder skills
build
  • ·Live freelance / internship project with real users
  • ·1 deep technical writeup or talk
resources
  • ·Designing Data-Intensive Applications (book)
  • ·Real internships > more courses
mistakes to avoid
  • ·Refusing to leave learning mode
  • ·Picking companies that don't have data culture
milestones

am i on track?

honest answer: most people consuming roadmaps don't ship. if you've completed ≥ 1 project per phase you've reached, you're ahead of 80% of students.

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