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Great lists There are some famous reservoirs of the gut hub, which often attracts thousands of stars in the community. These manufactured lists collect high quality resources, tools and lessons on a particular topic, making them the same valuable references for developers and learners.
However, adding the word “horrible” to your reservoir name does not guarantee that you will automatically find many stars. The popularity of an amazing list depends on the quality and utility of its content, as well as its integrity in society. If your amazing list has been officially confirmed or added by the original Great List Creator, SundersorosIt can significantly increase the reputation and reputation of your reservoir. People rely on the “terrible” brand.
In this article, we will review some of the most popular and impressive lists for data science. We will find collections of tools, resources, lessons, leaders and learning paths, all of this is designed to help you maximize your learning journey in data science.
1. Amazing Ujjar: Final Uzar Resources List
Link: Wenta/awesome tiger
Here is a comprehensive list of Azigar framework, libraries, software and resources that have been going on for at least 10 years and are still active. It is essential for any data scientist working with Azigar, which covers everything from data analysis and machine learning to web development and automation.
2. Terrible r: essential r packages and tools
Link: Qinwf/amazing-r
Finding the best R Tools can be difficult, as its community is relatively smaller than the agglong. This combination of top Rack packages, framework and software provides a stop shop to discover all kinds of RA packages for various use matters. Whether you are interested in data manipulation, concept, or statistical modeling, this list is your gateway to the RC environmental system.
3. Great public datases: high quality open data
Link: AWESOMEDATA/TRUE Public doctates
Here is a manufactured list of high quality open datases, organized by the title. It is ideal for data science projects, machine learning experiences, and whatever real -world data wants to work. After Kagal, this is a great source for free datases to download and enhance your data science portfolio.
4. Amazing Scalcheti: Tools for the leading Orm of Azigar
Link: Delhi
It is a list of Skilkimi, Azigar’s most famous Orm tools tools, extensions and resources. Ideal for data scientists and engineers working with a database and complex data model.
5. Draw Data Science: Learn Data Science and apply it
Link: Academic/terrible figures
An open source storage that helps you learn data science from the beginning and also helps you make strong portfolio by working on real -life issues. This includes lessons, courses, books, and project ideas for each level.
6. Learn Data Science: Curates Learning Way
Link: Sebhem
A handpeaking list of Resources Resources Resource Resources to Start with Data Science. Find an early friendly lesson, MOOC, books, and guides to start your data science journey.
7. Drawful Analytics: Analytical Tools and Framework
Link: Oxin r/awesome analytics
A list of analytics framework, software and tools. Great for every level, including non -technical people who want to find nine code tools for data science or social media analytics.
8. Amazing Machine Learning: The best ML Libraries
Link: Josephymysi/awesome machine Learning
A comprehensive and systematic list of machine learning framework, libraries and software in several languages. It also includes free machine learning books, courses, blogs, newsletters, and links to local matters and communities.
9. Great Machine Learning Lesson: Practical Leaders and Subjects
Link: Ujwarkarn/Machine Learning-Tutorels
Machine Learning and Deep Learning A collection of lessons, articles, and resources. Perfect for hands -on learners who want to deepen their understanding through practical examples.
10. Amazing Data Science: Curates Data Science Tools
Link: Krzjoa/terrible Uzar-Data Science
Data Science’s TOPS is a carefully produced list of high -pitched packages, which includes different fields such as machine learning, deep learning, concept, deployment, and more.
Conclusion
In the world of today’s endless information, there are the right gold ears for everyone to learn amazing lists and build real skills. People are starting to understand that vibing coding is fun, but if you want to produce sustainable products, you need to learn the basics. At the same place, these developed gut hubs come to frightening storage: they help you to learn the basics, deepen your skills, and stay latest with the best tools and resources in the field of data science.
Therefore, bookmark this page and discover links that are similar to your interests, whether you are learning a new language or diving in a particular topic.
Abid Ali Owan For,,,,,,,,,, for,, for,,,, for,,,, for,,, for,,,, for,,,, for,,,, for,,, for,,, for,,, for,,, for,,,, for,,, for,,, for,,,, for,,, for,,,, for,,, for,,, for,,,, for,,, for,,, for,,,, for,,, for,,,, for,,, for,,,, for,,, for,,,, for,,, for,,,, for,,,, for,,,, for,,,, for,,,, for,,,, for,,,, for,,, for,,, for,,, for,,, for,,,,, for,,,, for,,,, for,,,, for,, for,.@1abidaliawan) A certified data scientist is a professional who loves to create a machine learning model. Currently, he is focusing on creating content and writing technical blogs on machine learning and data science technologies. Abid has a master’s degree in technology management and a bachelor’s degree in telecommunications engineering. Its vision is to create AI products using a graph neural network for students with mental illness.