Career Roadmap Guide: Role-Specific Learning Paths

This guide provides clear, role-specific learning paths for different careers in data science and machine learning. Each path includes recommended modules, projects, and resources tailored to the specific role.

Honesty note: Month ranges are realistic study estimates for a human pace (about 8–12 hours/week with a job and life), not job guarantees. The low end assumes some prior coding comfort. The high end assumes more review, projects, and interrupted weeks. Titles like “Solution Architect” or “Research Scientist” also need depth, portfolio evidence, and (for research) math/experimentation practice beyond checklist completion.

Canonical stage order and the module 15 (time series) branch match the root README. Stage 6 is vision and language (modules 11–12). Module 15 is an optional parallel track, see TIME_SERIES_LEARNING_PATH.md. SQL is Stage 1.5 (module 19), not Stage 7.5.

Data Analyst
Data Scientist
Machine Learning Engineer
LLM Engineer (Large Language Models)
GenAI Solution Architect
Computer Vision Engineer
AI Engineer (Generalist)
Data Engineer
MLOps Engineer
Research Scientist
Business Intelligence Analyst
Full-Stack AI Engineer
Backend Engineer

Data Analyst

Role Focus: Analyze data to provide insights, create reports, and support business decisions. Focus on data manipulation, visualization, and statistical analysis.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python Basics, Statistics	2-3 months
Data Fundamentals	01	NumPy, Pandas, Visualization, EDA	3-4 months
SQL & Databases	19	SQL, Database Fundamentals, NoSQL basics	2-3 months
Essential Skills	20, 21	Imbalanced Data, Model Explainability	1-2 months
Total	5 modules	Complete Path	12-18 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python Basics, Statistics, Math Fundamentals	⭐⭐⭐ Critical
01-python-for-data-science	Pandas, NumPy, Matplotlib, Seaborn, Plotly, Streamlit, EDA	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL Queries, Joins, Window Functions, OLAP/OLTP	⭐⭐⭐ Critical
20-handling-imbalanced-data	Data Quality, Resampling Techniques	⭐⭐ Important
21-model-explainability	SHAP, LIME, Feature Importance	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Beginner	Customer Data Dashboard with Streamlit, House Price Prediction	Data Visualization, EDA, Dashboard Creation
Intermediate	Customer Segmentation, Time Series Forecasting	Clustering, Time Series Analysis

Essential Resources

Resource Type	Files
Tools	Excel Data Analysis Guide, Power BI Guide, Web Scraping Guide
Skills	Data Science Cheatsheet, Math Formulas, Git Guide
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Python programming (Pandas, NumPy)
Data visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI)
SQL and database querying
Statistical analysis and hypothesis testing
Excel for data analysis
Data cleaning and preprocessing
Dashboard creation (Streamlit, Power BI)
Report writing and data storytelling

Data Scientist

Role Focus: Build predictive models, perform advanced statistical analysis, and extract insights from complex datasets. Bridge between business and technical teams.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Math, Statistics	2-3 months
Data Fundamentals	01	Data Manipulation, Visualization, EDA	2-3 months
SQL & Databases	19	Queries, joins, window functions (Stage 1.5, parallel with 01–02)	1-2 months
ML Basics	02-05	ML Concepts, Regression, Classification, Evaluation	3-4 months
Advanced ML	06-07	Ensemble Methods, Feature Engineering	2-3 months
Unsupervised Learning	08	Clustering, Dimensionality Reduction	1-2 months
Time Series	15	Time Series Analysis, Forecasting (optional, pick one path)	1-2 months
Essential Skills	20-21	Imbalanced Data, Explainability	1-2 months
Total	12 modules	Complete Path	18-28 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, Linear Algebra, Statistics, Calculus	⭐⭐⭐ Critical
01-python-for-data-science	Pandas, NumPy, Visualization, EDA	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts, Workflow, Best Practices	⭐⭐⭐ Critical
03-supervised-learning-regression	Linear/Polynomial Regression, Regularization	⭐⭐⭐ Critical
04-supervised-learning-classification	Logistic Regression, Trees, SVM, KNN, Naive Bayes	⭐⭐⭐ Critical
05-model-evaluation-optimization	Cross-Validation, Hyperparameter Tuning, Calibration	⭐⭐⭐ Critical
06-ensemble-methods	Bagging, Boosting, Stacking	⭐⭐⭐ Critical
07-feature-engineering	Feature Selection, Transformation, Encoding	⭐⭐⭐ Critical
08-unsupervised-learning	Clustering, PCA, Anomaly Detection	⭐⭐ Important
15-time-series-analysis	ARIMA, LSTM, Time Series Forecasting	⭐⭐ Important
19-sql-database-fundamentals	SQL, Database Design	⭐⭐ Important
20-handling-imbalanced-data	SMOTE, Class Weights, Evaluation Metrics	⭐⭐ Important
21-model-explainability	SHAP, LIME, Feature Importance	⭐⭐⭐ Critical

Recommended Projects

Level	Projects	Skills Practiced
Beginner	House Price Prediction, Titanic Survival, Wine Quality	Regression, Classification, EDA
Intermediate	Customer Churn Prediction, Credit Card Fraud Detection, Time Series Forecasting, Customer Segmentation	Imbalanced Data, Time Series, Clustering
Advanced	Model Explainability & Interpretability	SHAP, LIME, Model Interpretation

Essential Resources

Resource Type	Files
Core Skills	Data Science Cheatsheet, Math Formulas, ML Glossary
Advanced Topics	Model Interpretability Guide, Recommender Systems, MLFlow Guide
Tools	Docker Tutorial, Git Guide, Web Scraping Guide
Career	Career & Portfolio Guide, Interview Prep, Kaggle Competitions

Skills Checklist

Statistical analysis and hypothesis testing
Machine learning algorithms (supervised and unsupervised)
Feature engineering and selection
Model evaluation and optimization
Time series analysis
Data visualization and storytelling
SQL and database querying
Model explainability (SHAP, LIME)
Experiment tracking (MLflow)
Business acumen and communication

Machine Learning Engineer

Role Focus: Design, build, and deploy ML models to production. Focus on software engineering, MLOps, and scalable ML systems.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Math, Algorithms	2-3 months
Data Fundamentals	01	Data Manipulation, APIs, Web Development	2-3 months
SQL & Databases	19	SQL, databases (Stage 1.5)	1-2 months
ML Basics	02-05	ML Concepts, Models, Evaluation	3-4 months
Advanced ML	06-07	Ensembles, Feature Engineering	2-3 months
Unsupervised Learning	08	Clustering, Dimensionality Reduction	1 month
Deep Learning	09-10	Neural Networks, TensorFlow, PyTorch	2-3 months
Production	13-14	Model Deployment, MLOps, CI/CD	3-4 months
Essential Skills	20-21	Imbalanced Data, Explainability	1-2 months
Total	16 modules	Complete Path	24-36 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, OOP, Algorithms, Math	⭐⭐⭐ Critical
01-python-for-data-science	NumPy, Pandas, Flask, FastAPI, Streamlit	⭐⭐⭐ Critical
02-introduction-to-ml	ML Workflow, Best Practices	⭐⭐⭐ Critical
03-supervised-learning-regression	Regression Models, Evaluation	⭐⭐⭐ Critical
04-supervised-learning-classification	Classification Models	⭐⭐⭐ Critical
05-model-evaluation-optimization	Cross-Validation, Hyperparameter Tuning	⭐⭐⭐ Critical
06-ensemble-methods	Ensemble Techniques	⭐⭐ Important
07-feature-engineering	Feature Engineering, Pipelines	⭐⭐ Important
08-unsupervised-learning	Clustering, PCA	⭐ Optional
09-neural-networks-basics	Neural Networks, Backpropagation	⭐⭐⭐ Critical
10-deep-learning-frameworks	TensorFlow, PyTorch	⭐⭐⭐ Critical
13-model-deployment	REST APIs, Docker, Cloud Deployment, AWS SageMaker, A/B Testing	⭐⭐⭐ Critical
14-mlops-basics	DVC, MLflow, CI/CD, Kafka, Spark	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL, Database Design	⭐⭐ Important
20-handling-imbalanced-data	Data Quality, Resampling	⭐ Optional
21-model-explainability	SHAP, LIME	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Beginner	House Price Prediction, Titanic Survival	Model Building, Evaluation
Intermediate	Customer Churn Prediction, Feature Engineering Mastery	Feature Engineering, Pipelines
Advanced	End-to-End ML Pipeline, Model Deployment & Serving	Full Pipeline, Deployment, APIs, Cloud

Essential Resources

Resource Type	Files
Core Skills	Data Science Cheatsheet, DSA for ML Guide, Git Guide
System Design	System Design for Beginners, ML System Design Guide
MLOps	MLFlow Guide, Docker Tutorial, ML Model Testing
Tools	Data Validation Guide, AutoML Basics
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Software engineering (Python, Git, Testing)
Machine learning algorithms and frameworks
Model deployment (REST APIs, Docker, Cloud)
MLOps (MLflow, DVC, CI/CD)
Data pipelines and ETL
Cloud platforms (AWS, GCP, Azure)
Containerization (Docker, Kubernetes)
Monitoring and logging
A/B testing and experimentation
System design for ML systems

LLM Engineer (Large Language Models)

Role Focus: Build, fine-tune, and deploy large language models. Work with transformers, RAG systems, and generative AI applications.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Math, Algorithms	2-3 months
Data Fundamentals	01	Data Manipulation, APIs	2 months
SQL & Databases	19	SQL (Stage 1.5)	1-2 months
ML Basics	02-05	ML Concepts, Models, Evaluation	3-4 months
Advanced ML	06-07	Ensembles, Feature Engineering	1-2 months
Deep Learning	09-10	Neural Networks, PyTorch, TensorFlow	2-3 months
NLP	12	NLP, Transformers, Fine-tuning, RAG	4-5 months
Generative AI	25	Modern LLMs, Prompt Engineering, RAG Systems, AI Agents	1-2 months
Production	13-14	Model Deployment, MLOps	2-3 months
Essential Skills	21	Explainability	0.5-1 month
Total	12 modules	Complete Path	24-36 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, Linear Algebra, Statistics	⭐⭐⭐ Critical
01-python-for-data-science	NumPy, Pandas, APIs, Flask/FastAPI	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts, Workflow	⭐⭐ Important
03-supervised-learning-regression	Regression Basics	⭐ Optional
04-supervised-learning-classification	Classification Basics	⭐⭐ Important
05-model-evaluation-optimization	Evaluation Metrics, Hyperparameter Tuning	⭐⭐⭐ Critical
06-ensemble-methods	Ensemble Basics	⭐ Optional
07-feature-engineering	Feature Engineering for Text	⭐⭐ Important
09-neural-networks-basics	Neural Networks, Backpropagation	⭐⭐⭐ Critical
10-deep-learning-frameworks	PyTorch, TensorFlow	⭐⭐⭐ Critical
12-natural-language-processing	Text Preprocessing, Word Embeddings, RNNs, LSTMs, Transformers, Fine-tuning, RAG	⭐⭐⭐ Critical
25-generative-ai-llms	Prompt Engineering, Vector Databases, RAG Systems, LLM Agents, Multi-Agent Systems, LangChain, LangGraph	⭐⭐⭐ Critical
13-model-deployment	REST APIs, Docker, Cloud Deployment	⭐⭐⭐ Critical
14-mlops-basics	MLflow, Version Control, CI/CD	⭐⭐ Important
19-sql-database-fundamentals	SQL, Vector Databases	⭐⭐ Important
21-model-explainability	Model Interpretation	⭐ Optional

Recommended Projects

Level	Projects	Skills Practiced
Intermediate	Sentiment Analysis on Reviews	NLP, Text Classification
Advanced	LLM Chatbot & RAG System, End-to-End ML Pipeline	Modern LLMs, RAG, Vector Databases, LangChain, Deployment

Essential Resources

Resource Type	Files
Core Skills	Transformer Fine-Tuning Guide, Langchain Guide, LlamaIndex Guide
Advanced Topics	AI Agents Guide, MLFlow Guide
Tools	Docker Tutorial, Git Guide, Data Validation Guide
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Deep learning fundamentals (Neural Networks, Backpropagation)
NLP techniques (Tokenization, Embeddings, Attention)
Transformer architectures (BERT, GPT, T5)
Fine-tuning transformers (Hugging Face)
RAG (Retrieval Augmented Generation)
Langchain and LlamaIndex frameworks
Vector databases and embeddings (Pinecone, ChromaDB, Weaviate, FAISS)
Model deployment and serving
Prompt engineering (zero-shot, few-shot, chain-of-thought)
Generative configuration (temperature, top-p, top-k)
LLM evaluation and benchmarking
AI agents (CrewAI, AutoGen, Langgraph)
Multi-agent systems and coordination
LLM fine-tuning and RLHF

GenAI Solution Architect

Role Focus: Design and implement Generative AI solutions, multi-agent systems, and RAG architectures for real products. Lead technical teams and establish GenAI best practices at scale.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00-01	Python, Data Fundamentals	2-3 months
SQL & Databases	19	SQL, databases (Stage 1.5)	1-2 months
ML Basics	02-05	ML Concepts, Evaluation, Optimization	3-4 months
Deep Learning	09-10	Neural Networks, Frameworks	2-3 months
NLP & GenAI	12, 25	NLP, Transformers, Fine-tuning, RAG, Modern LLMs, AI Agents, Multi-Agent Systems	5-6 months
Production	13-14	Model Deployment, MLOps	3-4 months
Total	10 modules	Complete Path	22-32 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, Math, Algorithms	⭐⭐⭐ Critical
01-python-for-data-science	NumPy, Pandas, APIs, Flask/FastAPI	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts, Workflow	⭐⭐⭐ Critical
05-model-evaluation-optimization	Evaluation, Hyperparameter Tuning	⭐⭐⭐ Critical
09-neural-networks-basics	Neural Networks, Backpropagation	⭐⭐⭐ Critical
10-deep-learning-frameworks	PyTorch, TensorFlow	⭐⭐⭐ Critical
12-natural-language-processing	NLP, Transformers, Fine-tuning, RAG	⭐⭐⭐ Critical
25-generative-ai-llms	Prompt Engineering, Vector Databases, RAG Systems, LLM Agents, Multi-Agent Systems, LangChain, LangGraph, MCP, A2A	⭐⭐⭐ Critical
13-model-deployment	Deployment, APIs, Cloud, Hyperscalers	⭐⭐⭐ Critical
14-mlops-basics	MLOps, CI/CD, Experiment Tracking	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL, Databases, Vector Stores	⭐⭐ Important

Specialized Skills

Required Expertise:

RAG (Retrieval Augmented Generation) architectures
Multi-agent systems (CrewAI, AutoGen, Langgraph)
MCP (Model Context Protocol)
A2A (Agent-to-Agent) communication
Production GenAI deployment on hyperscalers (AWS, GCP, Azure)
Scaling strategies for GenAI applications
Cost optimization for LLM inference
Model serving and orchestration

Recommended Projects

Level	Projects	Skills Practiced
Intermediate	Chatbot Development, Sentiment Analysis	NLP, Transformers, RAG
Advanced	LLM Chatbot & RAG System, End-to-End ML Pipeline, Model Deployment & Serving	RAG Systems, Multi-Agent Systems, Full Stack GenAI, Production Deployment

Essential Resources

Resource Type	Files
Core Skills	Langchain Guide, LlamaIndex Guide, AI Agents Guide, GenAI Production Deployment Guide
Specialized	Transformer Fine-Tuning Guide, MLFlow Comprehensive Guide, Model Deployment Cheatsheet
Tools	Docker Tutorial, Web Scraping Guide, Git Guide
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Generative AI architectures (RAG, multi-agent systems)
RAG system design and implementation
Multi-agent orchestration (CrewAI, AutoGen, Langgraph)
MCP (Model Context Protocol) implementation
A2A (Agent-to-Agent) communication patterns
Production GenAI deployment (AWS SageMaker, GCP Vertex AI, Azure ML)
Scaling GenAI applications (horizontal scaling, model parallelism, batching)
Cost optimization for LLM inference
Model serving and API development (FastAPI, Flask)
Containerization and orchestration (Docker, Kubernetes)
MLOps for GenAI (experiment tracking, model registry, monitoring)
Vector databases and embeddings
Prompt engineering and optimization
Fine-tuning transformer models (T5, BERT, GPT)
Stakeholder communication and technical leadership

Computer Vision Engineer

Role Focus: Build and deploy computer vision models for image classification, object detection, segmentation, and image generation.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Math, Linear Algebra	2-3 months
Data Fundamentals	01	NumPy, Data Manipulation	1-2 months
SQL & Databases	19	SQL (Stage 1.5, optional)	0.5-1 month
ML Basics	02-05	ML Concepts, Models, Evaluation	3-4 months
Advanced ML	06-07	Ensembles, Feature Engineering	1-2 months
Deep Learning	09-10	Neural Networks, PyTorch, TensorFlow	2-3 months
Computer Vision	11	CNNs, Object Detection, Segmentation, GANs, Diffusion	4-6 months
Production	13-14	Model Deployment, MLOps	2-3 months
Essential Skills	21	Explainability	0.5-1 month
Total	11 modules	Complete Path	22-34 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, Linear Algebra, Statistics	⭐⭐⭐ Critical
01-python-for-data-science	NumPy, Pandas, Visualization	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts, Workflow	⭐⭐ Important
03-supervised-learning-regression	Regression Basics	⭐ Optional
04-supervised-learning-classification	Classification, Evaluation Metrics	⭐⭐⭐ Critical
05-model-evaluation-optimization	Cross-Validation, Hyperparameter Tuning	⭐⭐⭐ Critical
06-ensemble-methods	Ensemble Basics	⭐ Optional
07-feature-engineering	Feature Engineering	⭐ Optional
09-neural-networks-basics	Neural Networks, Backpropagation	⭐⭐⭐ Critical
10-deep-learning-frameworks	PyTorch, TensorFlow, Keras	⭐⭐⭐ Critical
11-computer-vision	Image Fundamentals, CNNs, Architectures (LeNet, AlexNet, VGG, ResNet), Object Detection (YOLO, R-CNN), Segmentation, GANs, Diffusion Models, Stable Diffusion, VAEs	⭐⭐⭐ Critical
13-model-deployment	REST APIs, Docker, Cloud Deployment	⭐⭐⭐ Critical
14-mlops-basics	MLflow, Version Control, CI/CD	⭐⭐ Important
19-sql-database-fundamentals	SQL Basics	⭐ Optional
21-model-explainability	SHAP for Images, Model Interpretation	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Intermediate	Handwritten Digit Recognition (MNIST)	CNNs, Image Classification
Advanced	Image Classification (CIFAR-10), Object Detection, Generative Model (GAN/VAE), Model Deployment & Serving	CNNs, Transfer Learning, Object Detection, GANs, Deployment

Essential Resources

Resource Type	Files
Core Skills	Data Science Cheatsheet, Math Formulas
System Design	System Design for Beginners, ML System Design Guide
Tools	Docker Tutorial, Git Guide, MLFlow Guide
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Deep learning fundamentals (Neural Networks, CNNs)
Computer vision architectures (ResNet, VGG, EfficientNet)
Object detection (YOLO, R-CNN, Faster R-CNN)
Image segmentation (Semantic, Instance, Mask R-CNN)
Transfer learning and fine-tuning
Data augmentation techniques
GANs and image generation
Diffusion models and Stable Diffusion
Model deployment and optimization
OpenCV and image processing
Model explainability for vision models

AI Engineer (Generalist)

Role Focus: Broad expertise across multiple AI domains including ML, NLP, Computer Vision, and Generative AI. Work on end-to-end AI solutions.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Math, Algorithms	2-3 months
Data Fundamentals	01	Data Manipulation, APIs, Web Development	2-3 months
SQL & Databases	19	SQL, databases (Stage 1.5)	1-2 months
ML Basics	02-05	ML Concepts, Models, Evaluation	3-4 months
Advanced ML	06-07	Ensembles, Feature Engineering	2-3 months
Unsupervised Learning	08	Clustering, Dimensionality Reduction	1-2 months
Deep Learning	09-10	Neural Networks, PyTorch, TensorFlow	2-3 months
Specialized DL	11-12	Computer Vision, NLP	5-7 months
Time series (branch)	15	Forecasting, sequence models (parallel to or after DL)	0.5-1 month
Generative AI	25	Modern LLMs, RAG, AI Agents, Multi-Agent Systems	1-2 months
Advanced Specialized	22-24	Reinforcement Learning, Graph Neural Networks, Audio/Speech	2-3 months
Production	13-14	Model Deployment, MLOps	3-4 months
Essential Skills	20-21	Imbalanced Data, Explainability	1-2 months
Total	23 modules	Complete Path	36-54 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, Math, Algorithms	⭐⭐⭐ Critical
01-python-for-data-science	NumPy, Pandas, APIs, Flask/FastAPI	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts, Workflow	⭐⭐⭐ Critical
03-supervised-learning-regression	Regression Models	⭐⭐⭐ Critical
04-supervised-learning-classification	Classification Models	⭐⭐⭐ Critical
05-model-evaluation-optimization	Evaluation, Hyperparameter Tuning	⭐⭐⭐ Critical
06-ensemble-methods	Ensemble Techniques	⭐⭐⭐ Critical
07-feature-engineering	Feature Engineering	⭐⭐⭐ Critical
08-unsupervised-learning	Clustering, PCA	⭐⭐ Important
09-neural-networks-basics	Neural Networks	⭐⭐⭐ Critical
10-deep-learning-frameworks	PyTorch, TensorFlow	⭐⭐⭐ Critical
11-computer-vision	CNNs, Object Detection, Segmentation, GANs, Diffusion	⭐⭐⭐ Critical
12-natural-language-processing	NLP, Transformers, Fine-tuning, RAG	⭐⭐⭐ Critical
25-generative-ai-llms	Modern LLMs, Prompt Engineering, RAG Systems, AI Agents, Multi-Agent Systems	⭐⭐⭐ Critical
15-time-series-analysis	Time Series, Forecasting	⭐⭐ Important
22-reinforcement-learning	RL, Q-Learning, DQN, Policy Gradients, Multi-Agent RL	⭐⭐ Important
23-graph-neural-networks	GNNs, GCNs, GATs, Graph Applications	⭐⭐ Important
24-audio-speech-processing	ASR, TTS, Audio Classification, Music Generation	⭐ Optional
13-model-deployment	Deployment, APIs, Cloud	⭐⭐⭐ Critical
14-mlops-basics	MLOps, CI/CD, MLflow	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL, Databases	⭐⭐ Important
20-handling-imbalanced-data	Data Quality	⭐ Optional
21-model-explainability	SHAP, LIME	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Beginner	House Price Prediction, Titanic Survival	Regression, Classification
Intermediate	Handwritten Digit Recognition, Sentiment Analysis, Time Series Forecasting	CNNs, NLP, Time Series
Advanced	Image Classification, LLM Chatbot & RAG System, Object Detection, End-to-End ML Pipeline, Generative Model (GAN/VAE), Model Deployment	Full Stack AI, Multiple Domains, GenAI

Essential Resources

Resource Type	Files
Core Skills	Data Science Cheatsheet, DSA for ML Guide, Math Formulas
System Design	System Design for Beginners, ML System Design Guide
Specialized	Transformer Fine-Tuning Guide, Langchain Guide, LlamaIndex Guide, AI Agents Guide
MLOps	MLFlow Guide, Docker Tutorial, ML Model Testing
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Machine learning (supervised, unsupervised, reinforcement)
Deep learning (CNNs, RNNs, Transformers)
Computer vision (Object Detection, Segmentation)
Natural language processing (NLP, Transformers, RAG)
Generative AI and modern LLMs (Prompt Engineering, RAG, AI Agents, Multi-Agent Systems)
Reinforcement learning (Q-Learning, DQN, Policy Gradients)
Graph neural networks (GCNs, GATs, Graph Applications)
Audio and speech processing (ASR, TTS, Audio Classification)
Generative AI (GANs, Diffusion Models, LLMs)
Model deployment and MLOps
Software engineering and system design
Multiple frameworks (PyTorch, TensorFlow, Hugging Face)
Cloud platforms and distributed computing
Research and experimentation

Data Engineer

Role Focus: Design, build, and maintain data pipelines, data warehouses, and data infrastructure. Focus on data quality, ETL processes, and scalable data systems.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Algorithms	2-3 months
Data Fundamentals	01	Data Manipulation, ETL, APIs	3-4 months
ML Basics	02-05	ML Concepts (for understanding)	2-3 months
Production	13-14	Deployment, MLOps, Kafka, Spark	3-4 months
Databases	19	SQL, NoSQL, Database Design	3-4 months
Essential Skills	20	Data Validation, Quality	1-2 months
Total	8 modules	Complete Path	18-28 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, OOP, Algorithms	⭐⭐⭐ Critical
01-python-for-data-science	Pandas, NumPy, ETL, APIs, Web Scraping	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts (understanding)	⭐ Optional
13-model-deployment	Docker, Cloud Deployment, APIs	⭐⭐⭐ Critical
14-mlops-basics	DVC, MLflow, Apache Kafka, Apache Spark	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL, NoSQL (MongoDB, Redis, Cassandra, Neo4j), Database Design	⭐⭐⭐ Critical
20-handling-imbalanced-data	Data Quality, Validation	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Beginner	Customer Data Dashboard	Data Pipelines, ETL
Advanced	End-to-End ML Pipeline	Full Data Pipeline, Infrastructure

Essential Resources

Resource Type	Files
Core Skills	Data Science Cheatsheet, Git Guide, DSA for ML Guide
System Design	System Design for Beginners, ML System Design Guide
Tools	Docker Tutorial, Data Validation Guide, Web Scraping Guide
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Python programming and scripting
SQL and database design
NoSQL databases (MongoDB, Redis, Cassandra)
ETL/ELT pipelines
Data warehousing concepts
Apache Spark and distributed computing
Apache Kafka for streaming
Cloud platforms (AWS, GCP, Azure)
Containerization (Docker, Kubernetes)
Data quality and validation
Data modeling and schema design

MLOps Engineer

Role Focus: Specialize in deploying, monitoring, and maintaining ML models in production. Focus on CI/CD, model versioning, and ML infrastructure.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Algorithms	2-3 months
Data Fundamentals	01	Data Manipulation, APIs	2 months
SQL & Databases	19	SQL (Stage 1.5)	1-2 months
ML Basics	02-05	ML Concepts, Models	3-4 months
Advanced ML	06-07	Ensembles, Feature Engineering	1-2 months
Deep Learning	09-10	Neural Networks, Frameworks	2-3 months
NLP & GenAI	12, 25	NLP, Transformers, Modern LLMs, RAG, AI Agents	2-3 months
Production	13-14	Deployment, MLOps, CI/CD, Kafka, Spark	4-5 months
Essential Skills	20-21	Imbalanced Data, Explainability	1-2 months
Total	14 modules	Complete Path	24-36 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, OOP, Algorithms	⭐⭐⭐ Critical
01-python-for-data-science	Python, APIs, Flask/FastAPI	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts	⭐⭐ Important
03-05	ML Models, Evaluation	⭐⭐ Important
09-10	Deep Learning Basics	⭐⭐ Important
12-natural-language-processing	NLP, Transformers	⭐⭐ Important
25-generative-ai-llms	Modern LLMs, RAG, AI Agents, GenAI Deployment	⭐⭐⭐ Critical
13-model-deployment	REST APIs, Docker, Cloud, AWS SageMaker, A/B Testing	⭐⭐⭐ Critical
14-mlops-basics	DVC, MLflow, CI/CD, Kafka, Spark, Feature Stores	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL, Databases	⭐⭐ Important
20-handling-imbalanced-data	Data Validation	⭐ Optional
21-model-explainability	Model Monitoring	⭐ Optional

Recommended Projects

Level	Projects	Skills Practiced
Advanced	End-to-End ML Pipeline, Model Deployment & Serving	Full MLOps Pipeline, CI/CD, Monitoring

Essential Resources

Resource Type	Files
Core Skills	MLFlow Guide, Docker Tutorial, ML Model Testing
System Design	System Design for Beginners, ML System Design Guide
Tools	Data Validation Guide, Git Guide
Career	Career & Portfolio Guide, Interview Prep

Skills Checklist

Software engineering (Python, Git, Testing)
ML concepts and model understanding
Containerization (Docker, Kubernetes)
CI/CD pipelines for ML
Model versioning (MLflow, DVC)
Model monitoring and observability
Cloud platforms (AWS, GCP, Azure)
Distributed systems (Kafka, Spark)
Infrastructure as Code (Terraform, CloudFormation)
Model serving and APIs
A/B testing and experimentation
GenAI deployment and operations (RAG systems, LLM serving, vector databases)
LLM monitoring and cost optimization

Research Scientist

Role Focus: Novel methods, careful experiments, and clear write-ups, plus the depth to read papers, reproduce results, and contribute new ideas. Conduct research, develop algorithms, and push boundaries of ML/AI; strong theory and coding both matter.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python, Advanced Math, Algorithms	3-4 months
Data Fundamentals	01	Data Manipulation	1-2 months
SQL & Databases	19	SQL basics (Stage 1.5, optional)	0.5-1 month
ML Basics	02-05	ML Theory, Algorithms, Evaluation	4-5 months
Advanced ML	06-07	Advanced Algorithms, Theory	2-3 months
Unsupervised Learning	08	Advanced Clustering, Dimensionality Reduction	2-3 months
Deep Learning	09-10	Neural Networks, Frameworks	3-4 months
Specialized DL	11-12	Computer Vision, NLP	5-7 months
Time series (branch)	15	Forecasting, temporal evaluation (optional but common in applied research)	0.5-1 month
Generative AI	25	Modern LLMs, RAG, AI Agents, Multi-Agent Systems	1-2 months
Advanced Specialized	22-24	Reinforcement Learning, Graph Neural Networks, Audio/Speech	2-3 months
Essential Skills	21	Explainability	0.5-1 month
Total	20 modules	Complete Path	36-54 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python, Advanced Math, Linear Algebra, Statistics, Calculus	⭐⭐⭐ Critical
01-python-for-data-science	NumPy, Pandas	⭐⭐ Important
02-introduction-to-ml	ML Theory, Concepts	⭐⭐⭐ Critical
03-supervised-learning-regression	Regression Theory, Statistical Analysis	⭐⭐⭐ Critical
04-supervised-learning-classification	Classification Theory, Algorithms	⭐⭐⭐ Critical
05-model-evaluation-optimization	Evaluation Theory, Optimization	⭐⭐⭐ Critical
06-ensemble-methods	Ensemble Theory, Advanced Techniques	⭐⭐⭐ Critical
07-feature-engineering	Feature Engineering Theory	⭐⭐ Important
08-unsupervised-learning	Clustering Theory, Dimensionality Reduction	⭐⭐⭐ Critical
09-neural-networks-basics	Neural Network Theory, Backpropagation	⭐⭐⭐ Critical
10-deep-learning-frameworks	PyTorch, TensorFlow	⭐⭐⭐ Critical
11-computer-vision	CV Theory, Architectures, Research	⭐⭐⭐ Critical
12-natural-language-processing	NLP Theory, Transformers, Research	⭐⭐⭐ Critical
25-generative-ai-llms	Modern LLMs, Prompt Engineering, RAG, AI Agents, Multi-Agent Systems, RLHF	⭐⭐⭐ Critical
15-time-series-analysis	Time Series Theory	⭐⭐ Important
22-reinforcement-learning	RL Theory, MDPs, Deep RL, Multi-Agent RL	⭐⭐⭐ Critical
23-graph-neural-networks	GNN Theory, Graph Algorithms, Research	⭐⭐⭐ Critical
24-audio-speech-processing	Audio Processing Theory, ASR, TTS	⭐⭐ Important
19-sql-database-fundamentals	SQL Basics	⭐ Optional
21-model-explainability	Explainability Theory, Research	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Intermediate	Feature Engineering Mastery, Ensemble Methods Comparison	Advanced Techniques
Advanced	All Advanced Projects	Research, Experimentation, Innovation

Essential Resources

Resource Type	Files
Core Skills	Math Formulas, ML Glossary, Reinforcement Learning, Causal Inference Guide
Advanced Topics	Model Interpretability Guide, Recommender Systems
Career	Career & Portfolio Guide, Open Source Contribution

Skills Checklist

Strong mathematical foundation (Linear Algebra, Statistics, Calculus, Optimization)
Deep understanding of ML theory and algorithms
Research methodology and experimental design
Deep learning architectures and theory
Reinforcement learning theory and applications
Graph neural networks and graph algorithms
Audio and speech processing
Generative AI and modern LLMs (prompt engineering, RAG, AI agents)
LLM training, fine-tuning, and RLHF
Ability to read and implement research papers
Publication and writing skills
Programming (Python, PyTorch, TensorFlow)
Experimentation and hypothesis testing
Critical thinking and problem-solving
Domain expertise in specific areas

Business Intelligence Analyst

Role Focus: Create dashboards, reports, and visualizations to support business decisions. Focus on data visualization, reporting, and business metrics.

Core Learning Path

Phase	Modules	Focus Areas	Time Estimate
Foundation	00	Python Basics, Statistics	2-3 months
Data Fundamentals	01	Data Manipulation, Visualization, Dashboards	3-4 months
ML Basics	02-05	ML Concepts (basic understanding)	2-3 months
SQL & Databases	19	SQL, Database Design, OLAP/OLTP	2-3 months
Essential Skills	20-21	Data Quality, Explainability	1-2 months
Total	7 modules	Complete Path	14-22 months

Essential Modules

Module	Topics	Priority
00-prerequisites	Python Basics, Statistics	⭐⭐⭐ Critical
01-python-for-data-science	Pandas, Visualization (Matplotlib, Seaborn, Plotly), Streamlit, Tableau	⭐⭐⭐ Critical
02-introduction-to-ml	ML Concepts (understanding)	⭐ Optional
03-05	ML Basics (understanding)	⭐ Optional
19-sql-database-fundamentals	SQL, OLAP/OLTP, Database Design	⭐⭐⭐ Critical
20-handling-imbalanced-data	Data Quality	⭐⭐ Important
21-model-explainability	Explainability for Business	⭐⭐ Important

Recommended Projects

Level	Projects	Skills Practiced
Beginner	Customer Data Dashboard with Streamlit	Dashboard Creation, Visualization
Intermediate	Customer Segmentation, Time Series Forecasting	Business Analysis, Reporting

Essential Resources

Resource Type	Files
Core Skills	Excel Data Analysis Guide, Power BI Guide, Data Science Cheatsheet
Tools	Web Scraping Guide, Git Guide
Career	Career & Portfolio Guide, Interview Prep, Stakeholder Communication

Skills Checklist

Data visualization (Tableau, Power BI, Plotly, Streamlit)
SQL and database querying
Excel for data analysis
Dashboard creation and design
Business metrics and KPIs
Report writing and storytelling
Statistical analysis basics
Data cleaning and preprocessing
Stakeholder communication
Business acumen

Backend Engineer

Role Focus: Design, build, and operate APIs and services that clients and other systems depend on. You care about request lifecycle, data integrity, security, observability, and graceful failure. This path is language-agnostic at the concept layer and pairs with hands-on Node/Postgres lessons in the Full-Stack Track.

Start here: Backend Engineer Roadmap

Core learning path

Phase	Focus	Primary resources
0–1	HTTP, routing, serialization	Backend roadmap, System Design HTTP, Phase B
2–3	Auth, REST, layering, Postgres	API Design, Phase C, Module 19
4–5	Cache, queues, search	Caching, Message Queues, Phase E
6–7	Ops, security, scale, concurrency	Backend roadmap Phases 5–7, System Design scaling

Essential modules (this repository)

Module	Topics	Priority
19-sql-database-fundamentals	SQL, joins, relational design	⭐⭐⭐ Critical
13-model-deployment	FastAPI, serving, API patterns for ML	⭐⭐ Important (if you serve models)
14-mlops-basics	Monitoring, deployment pipelines	⭐⭐ Important (as you ship)

Recommended portfolio builds

Level	Build	Why it matters
Starter	REST API + Postgres + auth	Proves request path and persistence
Intermediate	Cache + background jobs + structured logs	Mirrors real team services
Advanced	Search + graceful shutdown + load test notes	Shows production thinking

Essential resources (in this repo)

Area	Files
Roadmap	Backend Engineer Roadmap
Architecture	System Design for Beginners
Hands-on	Full-Stack Track Phases B, C, E
ML APIs	Model Deployment, ML System Design Guide

Skills checklist

HTTP methods, status codes, and headers
REST resource design and OpenAPI contracts
Postgres schema, indexes, and transactions
Authn/authz (sessions or JWT + RBAC)
Caching and message queues
Structured logging and basic metrics
Security baseline (OWASP API Top 10 awareness)
IO vs CPU concurrency model for your stack

Full-Stack AI Engineer

Role Focus: Ship AI-enabled products end to end: typed application code, APIs, databases, frontend, containers, and safe LLM features (RAG, streaming, evaluation). You still lean on this repo for ML literacy and GenAI fundamentals; the rest is a structured companion path with external learning and portfolio builds.

Important naming note: In the Full-Stack AI Engineer Blueprint, labels like Module 01 are topic blocks inside that guide, not the same thing as this repository’s folder 01-python-for-data-science. Repo modules stay 00–25; the blueprint’s phases A–H describe software and product skills around them.

Core learning path (this repo + companion blueprint)

Track	What to use	Focus	Time (indicative)
ML & data core (in-repo)	00, 01, 19, 25 (and 13–14 as you advance)	Python stack, SQL, production/MLOps touchpoints, LLMs and GenAI	Overlap with main roadmap; treat as parallel or prerequisite slices
Product engineering (companion)	Full-Stack AI Engineer Blueprint Phases A–H and in-repo lesson chapters	TypeScript, Node/Express, Postgres/Prisma, Next.js, Docker/Nginx, AI integration	18–30 months at ~8–12 hrs/week on ML plus parallel blueprint hours

Essential modules (this repository)

Module	Topics	Priority
00-prerequisites	Python, math, stats	⭐⭐⭐ Critical
01-python-for-data-science	Data stack, EDA, APIs where relevant	⭐⭐⭐ Critical
19-sql-database-fundamentals	SQL, relational design	⭐⭐⭐ Critical
25-generative-ai-llms	LLMs, prompting, app-facing GenAI	⭐⭐⭐ Critical
13-14	Deployment, MLOps, APIs	⭐⭐ Important (as you ship)

Recommended portfolio builds

Level	Build	Why it matters
Starter	Typed CLI or small API + Postgres	Proves fundamentals before frameworks
Intermediate	Auth + RBAC + CRUD + migrations	Mirrors real product backends
Advanced	Full-stack app + RAG or streaming assistant + observability	Matches hiring bar for “AI in production”

Essential resources (in this repo)

Area	Files
Shipping & ops	Git Guide, Docker Tutorial, Model Deployment Cheatsheet, MLOps Cheatsheet
GenAI & RAG	Generative AI Comprehensive Guide, RAG Comprehensive Guide, Langchain Guide
Full-stack lessons (in-repo)	Full-stack track index
System design	ML System Design Guide, GenAI Production Deployment Guide

Full phase-by-phase plan and free external links: Full-Stack AI Engineer Blueprint →

Skills checklist

TypeScript for application and API layers
REST design, validation, auth (sessions/JWT), and error handling
Relational modeling, SQL, migrations, and ORM basics
Frontend architecture (e.g. Next.js) and secure client/server boundaries
Containers, reverse proxy, and environment/config hygiene
LLM integration: prompts, structured output, streaming, RAG, basic evaluation and cost awareness

Quick Reference: Role Comparison

Role	Primary Focus	Key Skills	Typical Projects
Data Analyst	Insights & Reports	SQL, Visualization, Excel	Dashboards, Reports
Data Scientist	Predictive Models	ML, Statistics, Python	Classification, Forecasting
ML Engineer	Production ML	Software Engineering, MLOps	Model Deployment, APIs
LLM Engineer	Language Models	Transformers, NLP, RAG	Chatbots, RAG Systems
GenAI Solution Architect	Production GenAI	RAG, Multi-Agent, MCP, A2A, Hyperscalers	Enterprise GenAI Solutions
CV Engineer	Image Processing	CNNs, Object Detection	Image Classification, Detection
AI Engineer	Generalist AI	ML, DL, CV, NLP	Multi-domain Projects
Data Engineer	Data Infrastructure	ETL, Databases, Spark	Data Pipelines, Warehouses
MLOps Engineer	ML Operations	CI/CD, Monitoring, Infrastructure	ML Infrastructure, CI/CD
Research Scientist	Novel methods, experiments, write-ups	Theory, Math, Research	Research Projects, Papers
BI Analyst	Business Intelligence	Visualization, Reporting	Dashboards, Business Reports
Full-Stack AI Engineer	End-to-end AI products	TypeScript, APIs, SQL, Next.js, LLM/RAG	Auth + data app + AI feature in production
Backend Engineer	APIs and services	HTTP, Postgres, cache, queues, security	REST API + auth + observability in production

How to Use This Guide

Identify Your Target Role: Choose the role that aligns with your career goals.
Follow the Learning Path: Complete modules in the recommended order.
Build Projects: Work on projects relevant to your role to build a portfolio.
Use Resources: Refer to the essential resources for each role.
Track Progress: Use the skills checklist to track your learning progress.
Customize: Adjust the path based on your background and goals.

Additional Tips

Start Broad, Then Specialize: Begin with foundational modules, then focus on your area of interest.
Build a Portfolio: Complete projects relevant to your target role.
Practice Regularly: Consistent practice is key to mastery.
Join Communities: Engage with others in your field.
Stay Updated: ML/AI is rapidly evolving - keep learning new techniques.
Network: Connect with professionals in your target role.

Try next: Pick one role row above. Schedule the next two modules on your calendar this week.

Career Roadmap Guide: Role-Specific Learning Paths

Table of Contents

Data Analyst

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

Data Scientist

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

Machine Learning Engineer

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

LLM Engineer (Large Language Models)

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

GenAI Solution Architect

Core Learning Path

Essential Modules

Specialized Skills

Recommended Projects

Essential Resources

Skills Checklist

Computer Vision Engineer

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

AI Engineer (Generalist)

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

Data Engineer

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

MLOps Engineer

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

Research Scientist

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

Business Intelligence Analyst

Core Learning Path

Essential Modules

Recommended Projects

Essential Resources

Skills Checklist

Backend Engineer

Core learning path

Essential modules (this repository)

Recommended portfolio builds

Essential resources (in this repo)

Skills checklist

Full-Stack AI Engineer

Core learning path (this repo + companion blueprint)

Essential modules (this repository)

Recommended portfolio builds

Essential resources (in this repo)