Data Scientist
A research-backed roadmap from zero to job-ready across 8 stages — Python, Statistics, Classical ML, Deep Learning, LLMs, and MLOps in the exact dependency order that hiring managers are looking for in 2026.
Python & Programming
3–4 weeksThe universal language of data science — 85%+ of ML codebases, research papers, and Kaggle notebooks are Python. Build the right subset of skills before tackling ML algorithms.
Statistics & Probability
4–6 weeksThe mathematical foundation of machine learning. Every model you train is making probabilistic predictions — you need to understand the language before you can trust the outputs.
EDA & Feature Engineering
4–5 weeksThe best data scientists spend 40–60% of project time here. No model compensates for not understanding your data — and features are where domain expertise beats algorithmic sophistication.
Classical Machine Learning
6–8 weeksGradient boosting still wins on tabular data in 2026. Master the canonical algorithms, model evaluation, and the full scikit-learn workflow before moving to deep learning.
Deep Learning
6–8 weeksPyTorch dominates 85%+ of DL research in 2026. The transformer architecture powers GPT, Claude, AlphaFold, and ViT — mastering it is the prerequisite for working with any modern foundation model.
NLP, LLMs & GenAI
4–6 weeksRAG is the #1 production AI pattern in 2026. LLM APIs are a standard engineering tool. Data scientists who can build, evaluate, and deploy GenAI applications are in the highest-demand talent tier.
MLOps & Production
4–6 weeksA model that lives only in a notebook doesn't exist in production. FastAPI + Docker + MLflow is the 2026 baseline. EU AI Act compliance makes explainability a legal requirement.
Portfolio & Career
4–8 weeksSkills are necessary but not sufficient. End-to-end projects, a strong GitHub, and ML system design fluency convert skills into offers in the 2026 data science market.
Ready to start your path?
Python, statistics, and SQL are in 90%+ of data scientist job postings — start with the foundations.