All roadmapsRoadmap · 2026

Data Science Roadmap 2026

The complete Data Science roadmap in 7 stages — Foundational Skills, Programming & Data Handling, Statistical & Mathematical Core, Machine Learning Fundamentals, Advanced ML & Deep Learning, Deployment & MLOps and Portfolio & Job Readiness. Open any stage in the mind map to see the topics and tools inside it, or download the whole roadmap as an image.

Mind map

Data Science Roadmap

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Data Science roadmap, stage by stage

01Foundational Skills
- Basic Computer Science
  - Data Structures
  - Algorithms
  - Computational Thinking
- Linux & Command Line
  - Basic Commands
  - Shell Scripting
  - Environment Setup
- Version Control
  - Git Basics
  - GitHub Workflow
  - Branching Strategies
02Programming & Data Handling
- Python Programming
  - Syntax & Data Types
  - Functions & Classes
  - Object-Oriented Programming
- SQL for Data
  - SELECT, FROM, WHERE
  - JOINs & Aggregations
  - Database Design Basics
- Data Manipulation & Analysis
  - Pandas Library
  - NumPy for Arrays
  - Data Cleaning Techniques
- Data Visualization
  - Matplotlib
  - Seaborn
  - Plotly & Dash
03Statistical & Mathematical Core
- Probability & Statistics

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Descriptive Statistics

Inferential Statistics

Hypothesis Testing

Probability Distributions

Linear Algebra

Vectors & Matrices
Matrix Operations
Eigenvalues & Eigenvectors

Calculus Essentials

Derivatives
Integrals
Gradient Descent

04Machine Learning Fundamentals

ML Concepts
- Bias-Variance Tradeoff
- Overfitting & Underfitting
- Cross-Validation
Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Gradient Boosting (XGBoost)
Unsupervised Learning
- K-Means Clustering
- DBSCAN
- PCA for Dimensionality Reduction
Model Evaluation
- Accuracy, Precision, Recall, F1
- ROC AUC
- RMSE, MAE

05Advanced ML & Deep Learning

Feature Engineering
- Categorical Encoding
- Feature Scaling
- Time Series Features
Deep Learning Basics
- Neural Networks
- Activation Functions
- TensorFlow/PyTorch
Natural Language Processing
- Text Preprocessing
- Word Embeddings (Word2Vec)
- Transformers (BERT)
Computer Vision Intro
- Image Preprocessing
- Convolutional Neural Networks (CNNs)
- Object Detection Basics

06Deployment & MLOps

Cloud Platforms
- AWS Sagemaker
- Google Cloud AI Platform
- Azure ML
MLOps Concepts
- Model Versioning
- Experiment Tracking (MLflow)
- CI/CD for ML
API Development
- Flask
- FastAPI
- Docker for Containers

07Portfolio & Job Readiness

Project Building
- End-to-End Projects
- Kaggle Competitions
- Open Source Contributions
Communication Skills
- Storytelling with Data
- Presentation Skills
- Technical Writing
Interview Preparation
- Behavioral Questions
- Technical Questions
- Case Studies