# visualize-ml/book6_first-course-in-data-science

**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/visualize-ml-book6-first-course-in-data-science).**

2,603 stars · 464 forks · Jupyter Notebook

## Links

- GitHub: https://github.com/Visualize-ML/Book6_First-Course-in-Data-Science
- awesome-repositories: https://awesome-repositories.com/repository/visualize-ml-book6-first-course-in-data-science.md

## Topics

`data` `data-science` `data-visualization` `feature-engineering` `machine-learning` `python`

## Description

This project is a structured data science curriculum and Python-based textbook designed to teach the fundamentals of data science through executable scripts and hands-on lessons. It functions as a guided programming tutorial for data manipulation and analysis within the Python ecosystem.

The content covers introductory machine learning, including the implementation of basic models and algorithms, alongside Python data analysis for cleaning and processing datasets.

The material is delivered via Jupyter Notebooks, combining modular exercises and markdown-driven documentation to map theoretical concepts to practical coding tasks.

## Tags

### Education & Learning Resources

- [Data Science Curricula](https://awesome-repositories.com/f/education-learning-resources/data-science-curricula.md) — Offers a structured curriculum of executable scripts for learning data science workflows through practical application.
- [Data Science Learning Materials](https://awesome-repositories.com/f/education-learning-resources/data-science-learning-materials.md) — Teaches fundamental data science principles through guided Python coding examples and exercises.
- [Data Science Tutorials](https://awesome-repositories.com/f/education-learning-resources/data-science-tutorials.md) — Provides practical scripts and hands-on exercises to translate theoretical data science concepts into working code. ([source](https://github.com/Visualize-ML/Book6_First-Course-in-Data-Science/blob/main/README.md))
- [Python Tutorials](https://awesome-repositories.com/f/education-learning-resources/educational-resources/languages-and-programming-concepts/programming-language-mastery-guides/python-tutorials.md) — Provides guided coding examples to teach data manipulation and analysis using the Python ecosystem.
- [Jupyter Notebook Curricula](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula.md) — Delivers a structured learning path through Jupyter notebooks combining executable code and rich text.
- [Coding Exercises](https://awesome-repositories.com/f/education-learning-resources/coding-exercises.md) — Organizes content into discrete chapters and lessons featuring isolated practical coding tasks.
- [Introductory Machine Learning](https://awesome-repositories.com/f/education-learning-resources/introductory-machine-learning.md) — Implements basic machine learning models and algorithms to introduce the fundamentals of predictive systems.

### Artificial Intelligence & ML

- [Data Science Fundamentals](https://awesome-repositories.com/f/artificial-intelligence-ml/data-science-fundamentals.md) — Provides a structured educational path covering core data science and statistical workflows.
- [Python Data Science Primers](https://awesome-repositories.com/f/artificial-intelligence-ml/python-data-science-primers.md) — Serves as a comprehensive primer on data science fundamentals using NumPy, Pandas, and visualization libraries.

### Data & Databases

- [Data Analysis Workflows](https://awesome-repositories.com/f/data-databases/data-analysis-workflows.md) — Demonstrates how to use Python libraries to clean, process, and analyze datasets.

### Content Management & Publishing

- [Markdown Documentation](https://awesome-repositories.com/f/content-management-publishing/markdown-documentation.md) — Uses structured markdown prose and mathematical notation to provide theoretical frameworks for code examples.