data-science-blogs VS awesome-datascience

Compare data-science-blogs vs awesome-datascience and see what are their differences.

data-science-blogs

A curated list of data science blogs (by rushter)

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems. (by academic)
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data-science-blogs awesome-datascience
2 4
5,775 17,780
- 1.8%
1.8 8.4
6 months ago 10 days ago
Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

data-science-blogs

Posts with mentions or reviews of data-science-blogs. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-13.

awesome-datascience

Posts with mentions or reviews of awesome-datascience. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-13.

What are some alternatives?

When comparing data-science-blogs and awesome-datascience you can also consider the following projects:

free-data-science-books - Free resources for learning data science

ds-cheatsheets - List of Data Science Cheatsheets to rule the world

Keras - Deep Learning for humans

Data-science-best-resources - Carefully curated resource links for data science in one place

DataScienceResources - Open Source Data Science Resources.

gdelt

Data-Science-Wiki - A wiki of DataScience, Statistics, Maths, R,Python, AI, Machine Learning, Automation, Devops Tools, Bash, Linux Tutorials, Scripts and Datasets

Awesome-VAEs - A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.