awesome-datascience and awesome-data-centric-ai

These two repositories are complements as the first offers a broad resource for learning and applying data science, while the second provides specific resources focused on the emerging field of data-centric AI, which is a specialized application within the broader data science landscape.

awesome-datascience
71
Verified
awesome-data-centric-ai
55
Established
Maintenance 20/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 10/25
Adoption 10/25
Maturity 16/25
Community 19/25
Stars: 28,556
Forks: 6,397
Downloads:
Commits (30d): 39
Language:
License: MIT
Stars: 345
Forks: 47
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No Package No Dependents
No Package No Dependents

About awesome-datascience

academic/awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

This repository provides a comprehensive learning path for individuals aiming to understand and apply data science concepts. It organizes a wealth of resources, including tutorials, courses, algorithms, and tools, to help you navigate the field. Whether you're a student, an analyst, or a professional looking to transition into data science, this collection offers a structured guide to go from beginner to solving real-world problems using data.

data science education machine learning basics analytics career learning resources data analysis

About awesome-data-centric-ai

Data-Centric-AI-Community/awesome-data-centric-ai

Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖

This is a curated collection of resources for anyone developing or managing AI systems. It helps practitioners improve their AI outcomes by focusing on the quality and characteristics of the data used, rather than just the AI model itself. It offers tools and tutorials for tasks like understanding data, creating realistic synthetic data, and accurately labeling data, benefitting data scientists, machine learning engineers, and data quality specialists.

data-quality machine-learning-operations data-labeling synthetic-data-generation exploratory-data-analysis

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