datascience VS awesome-bigdata

Compare datascience vs awesome-bigdata and see what are their differences.

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datascience awesome-bigdata
4 3
4,071 12,803
- -
8.3 1.0
22 days ago 15 days ago
Creative Commons Zero v1.0 Universal 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.

datascience

Posts with mentions or reviews of datascience. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-01.

awesome-bigdata

Posts with mentions or reviews of awesome-bigdata. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-13.

What are some alternatives?

When comparing datascience and awesome-bigdata you can also consider the following projects:

Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai

tensorbase - TensorBase is a new big data warehousing with modern efforts.

mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.

NEPSE-Data-Visualizer-with-Streamlit - This is a repository to do data visualization of Nepal Stock Exchange data.

machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer.

yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

ActivitySchema - Repository for the ActivitySchema spec and supporting materials

mlreef - The collaboration workspace for Machine Learning

awesome-dataviz - :chart_with_upwards_trend: A curated list of awesome data visualization libraries and resources.

darts - A python library for user-friendly forecasting and anomaly detection on time series.

data-science-with-ruby - Practical Data Science with Ruby based tools.