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A good list Some of the most popular reposingories in GitTub, usually attracts thousands of stars from the community. These selected agencies collect high quality resources, tools, and tutorials on a particular topic, to enable significant references to engineers and students alike.
However, simply add the word “Awesome” on your last name does not guarantee that you will receive many stars automatically. The amazing list is dependent on the quality and usable of its contents, and its appearance within the community. If your good list is legalized or added by the Designer of the Disteduous List, Sindresersiresorhus, can increase your appearance and your reliability. People hope the “terrible” form.
In this article, we will update some popular and most impressive lists of data science. We will evaluate the collections of tools, services, tutorials, guidelines, and learning methods, all are designed to help you increase your learning trip in data science.
1. Python EWESOME: Last Python Resource List
Link: Vinti / awesome-python
Here is a complete list of python structures, libraries, software, and resources that appear at least 10 years and are still actively kept. This must be a bookmark at any data scientist working with Python scientist, which includes everything from data anchor and machinery in the Web Development and Automation.
2. Awareness packages r: rital
Link: qiwf / awesome-r
Finding the best Tools of R can be challenging, as its community is small compared to Python. This collection of high-package r, frameworks, and software provides a single standing store for all types of Rsules of the various charges of various charges. Whether you are interested in deception, visualize, or statistical moderation, this list is your gate on Rosystem Rosystem.
3. Superated public datasets: higher quality data
Link: Awesomedata / Awesoma-Public-Datets
Here is a selected list of high open datasets, organized on topic. It is good for data science projects, mechanical learning tests, and anyone who wants to work with real world data. After Kaggle, this is one of the best sources of free datasets to download and improve your data portfolio.
4. The SQLalchemy in Wesome: Leading Study Tools
Link: Dahlia / Awesoma-sqlalchemy
In a list of tools, extensions, and SQLalchemy services, the most popular Ython Orm Orm. Ready for data scientists and engineers working with information and complex types of data.
5. Wondery data science: Read and use data science
Link: Education / Awesome-Datascence
The last open place helps you learn data science from the beginning and also helps to create a strong portfolio by working on real health problems. Includes Tutorials, lessons, books, and ideas of all levels.
6. Read Data Science: Selected Reading Methods
Link: Siboehm / Awesome-Learn-Datascence
A good list of resources to help you get data science. Find the starting teaching, MOOS, books, and Guidelines for Starting your data trip.
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Link: OXNR / AWESOME-Analytics
The selected list of analysis, software, and tools. It is good for all levels, including non-skills seeking to test the scientific scientific code or social science.
8. Awesome Machine reading: The best ML libraries
Link: JOSEPHMICI / ESOME-MACHINE-LEARNING
A complete and orderly list of mechanical types of study, libraries, and software in all many languages. It also includes free machine textbooks, courses, blogs, newspapers, and local coordination links and communities.
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Link: Jangalkar / Study Machine-Tutorials
A collection of machine learning and deep teaching tutorials, articles and resources. Perfect is the hands – in students who want to deepen their understanding by practical examples.
10. Awesome Python Data Science: Python Data Science Tools selected
Link: Krzjo / Awesome-Python-Data-Science
Carefully cut off the high-scientific python of data science, including different areas such as mechanical learning, deep reading, visual recognition, shipments, and more.
Store
During the modern world of chronic knowledge, a terrible list is true gold-bits for anyone strong by learning and creating real skills. People begin to see that the vibe codes are sweet, but if you want to build a continuous product, you need to read the foundations. This is where these tricky Gitub has entered: help you read the basics, deepen your expertise, and stay up-to-date with the best tools and resources in the field of data science.
So, place a bookmark this page and check the links that correspond to your interests, whether you are learning a new language or in a particular subject.
Abid Awa (@ 1abidaswan) is a certified scientist for a scientist who likes the machine reading models. Currently, focus on the creation of the content and writing technical blogs in a machine learning and data scientific technology. Avid holds a Master degree in technical management and Bachelor degree in Telecommunication Engineering. His viewpoint builds AI product uses a Graph Neural network for students who strive to be ill.
