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Amazing letters This is one of the most popular repositories at Github, often attracting thousands of stars from the community. These selected lists accumulate high -quality resources, tools and tutorials on a specific topic, which makes them valuable references for programmers and students.
However, simply adding the word “amazing” to the name of the repository does not guarantee that you will automatically receive many stars. The popularity of the amazing list depends on the quality and usefulness of its content, as well as its visibility in the community. If your amazing list is officially verified or concluded by the original Amazing creator of the list, SindresorhusIt can significantly enhance the visibility and credibility of the repository. People trust the “amazing” brand.
In this article, we will review some of the most popular and impressive data lists. We will examine the sets of tools, resources, tutorials, guides and learning paths, all designed with a view to maximizing travel travel.
1. Awesome Python: The Ultimate Python List
To combine: Vinta/Awesome-Python
Here is a comprehensive list of RAM Python, libraries, software and resources that have existed for at least 10 years and are still actively maintained. It is an crucial tab for every scientist with Python, covering everything, from data analysis and machine learning to creating websites and automation.
2. Awesome R: Crucial r Packages and tools
To combine: Qinwf/Awesome-R
Finding the best R tools can be challenging because its community is relatively petite compared to Python. This collection of the best R packages, frames and software provides a comprehensive store to discover all types of R packages for various apply cases. Regardless of whether you are interested in data manipulation, visualization or statistical modeling, this list is your gate to the R.
3. Amazing public data sets: high -quality open data
To combine: Awesomedata/Awesome-Public-Datasets
Here is a selected list of high quality open data sets organized according to the topic. It is ideal for scientific projects, machine learning experiments and anyone who wants to work with data in the real world. After Kaggle, this is one of the best sources of free data sets for download and improving the data portfolio.
4. Awesome Sqlalchemy: Tools for the leading ORM Python
To combine: Dahlia/Awesome-Sqlalchemy
It is a list of tools, extensions and resources for Sqlalchemy, the most popular ORM Python. Ideal for scientists and data engineers working with databases and complicated data models.
5. Amazing data learning: they learn and apply data education
To combine: Akademicka/Awesome-Datasciience
Open Source repository, which from the very beginning helps learn to learn about data, and also helps in building a powerful portfolio by working on real problems. It includes tutorials, courses, books and ideas for projects at all levels.
6. Awesome Learn Data Science: Turked learning paths
To combine: Siboehm/awesome-learn-datasciience
An elderly list of resources that will lend a hand you start with data learning. Find genial for beginner tutorials, MOOC, books and guides to start a journey to learn data.
7. Amazing analyzes: the best tools and analytical frames
To combine: Oxnr/Awesome-Analytics
A specific list of analytical frameworks, software and tools. Ideal for all levels, including non -technical people who want to examine tools without coding for learning data or social media analysis.
8. Amazing machine learning: the best ml libraries
To combine: Josephmisiti/Awesome-Machine Learning
A comprehensive and organized list of machine learning framework, libraries and software in many languages. It also includes free books, courses, blogs, newsletters and links to local meetings and communities.
9. Amazing machine learning tutorials: practical guides and articles
To combine: WydwornKarn/-Machine Learning-W-Toutorials
A collection of tutorials of machine learning and deep learning, articles and resources. Ideal for practical students who want to deepen their understanding through practical examples.
10. Awesome Python Data Science: Python data tuned tools
To combine: Krzjoa/Awesome-Python-Data-Science
A carefully selected list of the best Python packages for data learning, including various areas, such as machine learning, deep learning, visualization, implementation and many others.
Application
In today’s world of endless information, amazing lists are real gold mines for everyone earnest in learning and building real skills. People are starting to realize that climate coding is fun, but if you want to build a balanced product, you need to learn the basics. This is where these selected GitHub repositories appear: they lend a hand in learning the basics, deepen specialist knowledge and are up to date with the best tools and resources in the field of data science.
Add this page to the bookmarks and examine the links that match your interests, regardless of whether you learn a modern language or dive on a specific topic.
Abid Ali Awan (@1abidaliawan) is a certified scientist who loves to build machine learning models. Currently, it focuses on creating content and writing technical blogs on machine learning and data learning technologies. ABID has a master’s degree in technology management and a bachelor’s title in the field of telecommunications engineering. His vision is to build AI with a neural network for students struggling with mental illness.