Full-Stack Data Science & AI Learning Roadmap: A Complete Bootcamp from Python Basics to Cloud Deployment

MAR26 is a structured data science and AI bootcamp project released by mrguezrodriguez on GitHub on June 7, 2026. With the core concept of 'full-stack data science education', the project provides an end-to-end learning path from programming basics to production deployment, emphasizing that data scientists need to have comprehensive capabilities in algorithms, data engineering, software development, and cloud deployment. Its content is designed in layers according to the skill pyramid, covering tech stacks such as Python, NumPy, Pandas, SQL, machine learning, deep learning, Docker, AWS, and big data, helping learners master full-stack skills step by step.

• Original Author: mrguezrodriguez
• Source Platform: GitHub
• Original Project Title: MAR26
• Original Link: https://github.com/mrguezrodriguez/MAR26
• Release Date: June 7, 2026
• Project Type: Data Science & AI Bootcamp
• Covered Tech Stack: Python, NumPy, Pandas, SQL, Machine Learning, Deep Learning, Docker, AWS, Big Data

The project content is designed in layers according to the learning curve, forming a skill pyramid:

1. Foundation Layer: Python Programming, NumPy Numerical Computing, Pandas Data Processing
2. Data Layer: SQL Database Operations, API Data Acquisition, Web Scraping
3. Analysis Layer: Matplotlib Data Visualization, Seaborn Statistical Charts, Power BI Business Intelligence
4. Modeling Layer: Traditional Machine Learning Algorithms, Deep Learning Neural Networks
5. Engineering Layer: Docker Containerization, AWS Cloud Services, Big Data Technologies

Learning Path Recommendations:

• Beginner Path (3-6 months): Month 1: Python Basics → Month 2: Data Processing → Month3: Visualization & EDA → Months4-6: Introduction to Machine Learning
• Advanced Path (6-12 months): Deep Learning Specialization → Engineering Capabilities → Big Data Skills

Basic Skills Module:

• Python Programming: Core Syntax, Data Structures, Object-Oriented Programming
• NumPy: ndarray, Vectorization Operations, Broadcasting Mechanism, Mathematical Operations
• Pandas: Data Reading & Writing, Cleaning, Transformation, Time Series Processing

Data Acquisition & Storage:

• SQL: Basic Queries, Aggregation, Multi-table Operations, Advanced Topics (Window Functions, CTE)
• API: HTTP Basics, Requests Library Usage, Authentication Mechanisms
• Web Scraping: requests+BeautifulSoup/Selenium/Scrapy, HTML/CSS Selectors, Anti-scraping Considerations

Visualization & Business Intelligence:

• Matplotlib: Various Charts (Line Charts, Scatter Plots, etc.), Customization Options
• Seaborn: Heatmaps, Distribution Plots, Violin Plots and other Advanced Charts, Theme Styles
• Power BI: Data Connection, Modeling, Visualization Design, DAX Language

Machine Learning & Deep Learning:

• ML: Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Dimensionality Reduction), Feature Engineering & Model Optimization
• DL: Neural Network Basics, CNN (Image Applications), RNN/LSTM (Sequence Applications)

Engineering & Deployment:

• Docker: Container Concepts, Dockerfile, Core Commands
• AWS: EC2, S3, RDS, Lambda, SageMaker and other services
• Big Data: Apache Spark (RDD, DataFrame, MLlib), Data Pipelines

End-to-End Project Flow:

1. Problem Definition →2. Data Collection →3. Data Cleaning →4. Exploratory Analysis →5. Feature Engineering →6. Model Training →7. Model Evaluation →8. Result Interpretation →9. Deployment to Production →10. Monitoring & Maintenance

Recommended Project Portfolio:

• Beginner: House Price Prediction (Regression), Customer Churn Prediction (Classification), Sales Data Analysis (EDA)
• Intermediate: Sentiment Analysis (NLP), Image Classification (CNN), Recommendation System
• Advanced: End-to-End ML Pipeline, Real-time Prediction System, Large-scale Data Processing

The value of the MAR26 bootcamp lies in its systematicness, progressiveness, practicality, and completeness, helping learners avoid knowledge gaps and master full-stack data science capabilities. For learners, it is recommended to follow the path step by step, focus on practice (3:7 ratio of theory to practice), and consolidate knowledge points through actual projects. Ultimately, the core competitiveness of data science is the ability to solve real problems—a mindset that is problem-oriented, data-driven, and value-targeted.