Data Analytics
29K subscribers
497 photos
15 videos
46 files
290 links
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
Forwarded from Github Top Repositories
🔥 Trending Repository: LearnRust

📝 Description: Rust Learning Resources

🔗 Repository URL: https://github.com/ImplFerris/LearnRust

🌐 Website: https://implrust.com

📖 Readme: https://github.com/ImplFerris/LearnRust#readme

📊 Statistics:
🌟 Stars: 1.8K stars
👀 Watchers: 32
🍴 Forks: 188 forks

💻 Programming Languages: Not available

🏷️ Related Topics:
#rust #learning #programming #rust_lang #rust_tutorial #learn_rust #rust_programming


==================================
🧠 By: https://xn--r1a.website/DataScienceN
1
Cheat sheet for working with data in Python (Data Science) 🐍📊

🔹 importing NumPy and pandas libraries — basic tools for data processing 🛠️

🔹 text files — reading/writing plain text and working via context manager 📄

🔹 tabular CSV/flat files — loading and processing structured data into DataFrame 📊

🔹 Excel files — working with sheets and tables 📑

🔹 SAS/Stata files — importing statistical formats 📉

🔹 HDF5 and Pickle — saving and loading complex data structures 💾

🔹 MATLAB files — reading .mat via SciPy 🧮

🔹 relational databases (SQL) — connecting, querying, and converting results into DataFrame 🗄️

🔹 Python dictionaries — accessing keys, values, and nested structures 🔑

🔹 data exploration (NumPy arrays and pandas DataFrames) — viewing types, sizes, and basic statistics 🔍

🔹 file system navigation — magic commands and os module for working with files and directories 📂

#Python #DataScience #Coding #Programming #Tech #Learning

https://xn--r1a.website/DataAnalyticsX
Please open Telegram to view this post
VIEW IN TELEGRAM
3
Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚

This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊

It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖

Free public repository on GitHub. 💻

https://github.com/dair-ai/Mathematics-for-ML

#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
4