AI & Data Scientist Roadmap

A structured beginner-to-job-ready roadmap covering Python, statistics, machine learning, deep learning, MLOps, and real-world AI engineering skills needed to land a data science or AI role.

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1. Stage 1: Programming & Math Foundations

Python for Data Science
Python is the primary language for all AI and data work
coursePython for Everybody – freeCodeCamp docPython Official Docs
NumPy & Pandas
Core libraries for numerical and tabular data manipulation
docNumPy Official Documentation docPandas Official Documentation
Linear Algebra & Calculus Basics
Underpins how ML models learn and optimize
courseMathematics for Machine Learning – Coursera videoEssence of Linear Algebra – 3Blue1Brown
Statistics & Probability
Statistical thinking drives every data science decision
courseStatistics with Python – Coursera docKhan Academy Statistics & Probability

2. Stage 2: Data Analysis & Visualization

Exploratory Data Analysis (EDA)
EDA reveals patterns and informs every modeling decision
tutorialPandas EDA Tutorial – Kaggle docPandas User Guide
Data Visualization
Communicating insights visually is a core data science skill
docMatplotlib Official Documentation docSeaborn Official Documentation
SQL for Data Science
Most real-world data lives in relational databases
courseSQL for Data Science – freeCodeCamp tutorialSQLZoo Interactive SQL Tutorial
Data Cleaning & Feature Engineering
Clean, well-engineered features directly determine model quality
tutorialData Cleaning – Kaggle Learn tutorialFeature Engineering – Kaggle Learn

3. Stage 3: Classical Machine Learning

Supervised Learning Algorithms
Regression and classification form the backbone of ML applications
docScikit-learn User Guide courseMachine Learning Specialization – Coursera (Andrew Ng)
Unsupervised Learning
Clustering and dimensionality reduction uncover hidden data structure
docScikit-learn Clustering Documentation tutorialUnsupervised Learning – Kaggle Learn
Model Evaluation & Validation
Proper evaluation prevents overfitting and misleading results
docScikit-learn Model Evaluation Docs tutorialCross-Validation & Metrics – Kaggle
Hyperparameter Tuning
Tuning unlocks the full performance potential of any model
docScikit-learn Hyperparameter Tuning Guide tutorialIntro to Hyperparameter Tuning – Kaggle

4. Stage 4: Deep Learning & Neural Networks

Neural Network Fundamentals
Deep learning powers modern AI from vision to language
courseDeep Learning Specialization – Coursera (Andrew Ng)docPyTorch Official Tutorials
Convolutional Neural Networks (CNNs)
CNNs are the standard architecture for image and spatial data
tutorialCNN Tutorial – PyTorch Docs courseConvolutional Neural Networks – Coursera
Recurrent Networks & Transformers
Transformers are the architecture behind all modern LLMs and NLP
tutorialThe Illustrated Transformer – Jay Alammar
Practical Deep Learning with PyTorch
PyTorch is the industry-standard framework for research and production
coursePractical Deep Learning for Coders – fast.ai docPyTorch Documentation

5. Stage 5: AI Engineering & LLMs

Large Language Models & Prompt Engineering
LLMs are now core tools in every AI engineer's workflow
docOpenAI API Documentation tutorialPrompt Engineering Guide
Retrieval-Augmented Generation (RAG)
RAG grounds LLMs in real data for reliable AI applications
docLangChain RAG Documentation docLlamaIndex Documentation
AI Agents & Tool Use
Agents enable LLMs to take actions and solve multi-step tasks
docLangChain Agents Documentation tutorialOpenAI Function Calling Guide
Model Fine-Tuning
Fine-tuning adapts foundation models to specific domains and tasks
docHuggingFace Fine-Tuning Tutorial tutorialPEFT & LoRA Guide – HuggingFace

6. Stage 6: MLOps & Production Engineering

Experiment Tracking & Model Registry
Tracking ensures reproducibility and team collaboration on models
docMLflow Documentation docWeights & Biases Documentation
Model Deployment & Serving
Deployed models create real business value from your ML work
docFastAPI Official Documentation docBentoML Documentation
Data & ML Pipelines
Pipelines automate and scale data processing end-to-end
docApache Airflow Documentation docPrefect Documentation
Model Monitoring & Drift Detection
Production models degrade without monitoring and retraining strategies
docEvidently AI Documentation

7. Stage 7: Portfolio, Specialization & Job Readiness

Kaggle Competitions & Real Datasets
Competition experience proves practical skills to employers
tutorialKaggle Competitions – Getting Started tutorialKaggle Learn Micro-Courses
Build an End-to-End AI Project
A shipped project demonstrates full-stack AI engineering ability
docGitHub Docs – Sharing Projects
Data Science Interview Preparation
Structured prep converts skills into offers at top companies
tutorialLeetCode Data Science Practice
Domain Specialization (FinTech, Healthcare, NLP)
Deep domain knowledge differentiates candidates in competitive markets
courseApplied Data Science with Python – Coursera docHuggingFace NLP Course

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