Data Science Learning Roadmap
Your complete guide from beginner to advanced data scientist
Phase 1: Foundations
Building your data science fundamentals
Mathematics
Learn linear algebra, calculus, and statistics – the essential math for data science.
Programming
Master Python or R, along with essential libraries like NumPy and Pandas.
Data Wrangling
Learn to collect, clean, and preprocess data for analysis.
Basic Visualization
Create basic charts and graphs to explore and present data.
Phase 2: Core Techniques
Essential data science methods and algorithms
Machine Learning
Learn supervised and unsupervised learning algorithms.
Model Evaluation
Master techniques for evaluating and validating models.
Feature Engineering
Create meaningful features to improve model performance.
Statistical Methods
Apply statistical tests and inference to draw conclusions.
Phase 3: Advanced Topics
Specialized data science areas
Deep Learning
Study neural networks, CNNs, RNNs, and frameworks like TensorFlow.
NLP
Learn natural language processing techniques for text data.
Recommendation Systems
Build systems that suggest relevant items to users.
Reinforcement Learning
Explore algorithms that learn through interaction with environments.
Phase 4: Production & Deployment
Taking models to the real world
ML Engineering
Build scalable machine learning systems and pipelines.
Cloud Platforms
Deploy models on AWS, GCP, or Azure cloud services.
Model Monitoring
Implement systems to track model performance in production.
MLOps
Automate ML workflows with continuous integration and deployment.