# microsoft/data-science-for-beginners **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/microsoft-data-science-for-beginners).** 35,657 stars · 7,256 forks · Jupyter Notebook · MIT ## Links - GitHub: https://github.com/microsoft/Data-Science-For-Beginners - awesome-repositories: https://awesome-repositories.com/repository/microsoft-data-science-for-beginners.md ## Topics `data-analysis` `data-science` `data-visualization` `microsoft-for-beginners` `pandas` `python` ## Description This project is a comprehensive educational curriculum designed to teach the fundamental concepts, workflows, and tools of data science. It provides a structured learning path that covers the end-to-end data science lifecycle, including data acquisition, maintenance, processing, and pattern discovery, while grounding theoretical knowledge in practical, real-world applications. The curriculum distinguishes itself through a data-driven pedagogical design that utilizes interactive, notebook-based lessons. By combining narrative text with live code blocks, the platform allows learners to experiment with data analysis and visualization techniques in real time. The content is organized into a modular structure that sequences topics by progressive complexity, ensuring that foundational skills are established before moving into more advanced analytical techniques. The material encompasses a broad capability surface, including tutorials on data visualization, relational database querying, and the integration of cloud computing into data science workflows. These resources rely on an established ecosystem of open-source libraries to ensure that the skills acquired are applicable to professional environments. The repository is hosted as a centralized collection of instructional modules and guided exercises. It includes self-contained code samples and assignments that require a standard Python environment to execute. ## Tags ### Education & Learning Resources - [Data Science Curricula](https://awesome-repositories.com/f/education-learning-resources/data-science-curricula.md) — Provides introductory data science programming examples and guided learning exercises. ([source](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/examples/README.md)) - [Data Visualization Tutorials](https://awesome-repositories.com/f/education-learning-resources/data-visualization-tutorials.md) — Offers practical tutorials on using plotting libraries for data visualization. ([source](https://github.com/microsoft/Data-Science-For-Beginners#readme)) - [Interactive Notebooks](https://awesome-repositories.com/f/education-learning-resources/interactive-notebooks.md) — Delivers educational content through interactive, executable notebooks that allow for immediate code experimentation. - [Cloud Computing Curricula](https://awesome-repositories.com/f/education-learning-resources/cloud-computing-curricula.md) — Provides educational resources on cloud computing benefits and service models. ([source](https://github.com/microsoft/Data-Science-For-Beginners#readme)) - [Data Querying Tutorials](https://awesome-repositories.com/f/education-learning-resources/data-querying-tutorials.md) — Provides instructional guides on relational database querying techniques. ([source](https://github.com/microsoft/Data-Science-For-Beginners#readme)) - [Database Fundamentals](https://awesome-repositories.com/f/education-learning-resources/database-fundamentals.md) — Explains the core concepts of relational tables and data organization. ([source](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/05-relational-databases/README.md)) - [Educational Repositories](https://awesome-repositories.com/f/education-learning-resources/educational-repositories.md) — Provides a structured collection of learning materials and hands-on exercises for foundational concepts. - [Pedagogical Frameworks](https://awesome-repositories.com/f/education-learning-resources/pedagogical-frameworks.md) — Provides a structured pedagogical approach that organizes instructional content around practical data analysis. - [Practical Assignments](https://awesome-repositories.com/f/education-learning-resources/practical-assignments.md) — Provides hands-on assignments for exploring datasets in a practical environment. ([source](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/05-relational-databases/README.md)) - [Sustainability Case Studies](https://awesome-repositories.com/f/education-learning-resources/sustainability-case-studies.md) — Includes case studies demonstrating data science applications across diverse fields. ([source](https://github.com/microsoft/Data-Science-For-Beginners#readme)) ### Graphics & Multimedia - [Visualization Frameworks](https://awesome-repositories.com/f/graphics-multimedia/visualization-mapping/visualization-frameworks.md) — Provides tools for creating interactive charts and graphical representations of data. ### Part of an Awesome List - [Data Science Learning](https://awesome-repositories.com/f/awesome-lists/devtools/data-science-learning.md) — Comprehensive data science curriculum for beginners. - [Educational Curricula](https://awesome-repositories.com/f/awesome-lists/learning/educational-curricula.md) — Ten-week curriculum covering core data science concepts and lessons. - [Learning Resources](https://awesome-repositories.com/f/awesome-lists/learning/learning-resources.md) — An introductory course covering core data science concepts. ### Artificial Intelligence & ML - [Cloud Machine Learning Examples](https://awesome-repositories.com/f/artificial-intelligence-ml/cloud-machine-learning-examples.md) — Provides tangible scenarios for applying machine learning techniques in cloud environments. ([source](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/5-Data-Science-In-Cloud/17-Introduction/README.md)) ### Development Tools & Productivity - [Interactive Notebooks](https://awesome-repositories.com/f/development-tools-productivity/interactive-notebooks.md) — Ships executable documents that combine explanatory text with live code blocks for data processing.