🏷 "Statistics for Data Science" Notes


👨🏻‍💻 In these notes, everything is structured and neatly organized from the basics of statistics to advanced tips. Each concept is explained with examples, formulas, and charts to make learning easy

🛑 What is statistics and why is it important?
🛑 Basic concepts
🛑 Descriptive statistics
🛑 Inferential statistics
🛑 Discrete and continuous distributions
🛑 And many other topics

🌐 #Data_Science #DataScience

https://t.iss.one/CodeProgrammer ✅

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📕 #pandas Cheat Sheet


👨🏻‍💻 To easily read, inspect, clean, and manipulate data however you want, you need to master pandas!

✏️ To make learning and using pandas easier, this #cheatsheet covers almost all the important features you need for data-driven projects.

✔️ Reading and writing data
✔️ Data inspection
✔️ Data transformation and cleaning
✔️ Grouping and summarizing
✔️ Combining datasets

🌐 #DataScience #DataScience

https://t.iss.one/DataAnalyticsX 🏐

📊 A comprehensive summary of the «Seaborn Library»

👨🏻‍💻 One of the best choices for any data scientist to convert data into clear and beautiful charts, so that they can better understand what the data is saying and also be able to present the results correctly and clearly to others, is the Seaborn library.

✅ A very user-friendly library for creating professional charts with minimal coding. It is built on top of Matplotlib but is simpler and easier to use than that.

✏️ With this summary, you will learn the syntax, see many examples and real applications of #Seaborn, and ultimately help you elevate your #datavisualization skills by several levels.

🌐 #Data_Science #DataScience

https://t.iss.one/DataAnalyticsX 🌟

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🌟 A new and comprehensive book "Mastering pandas"

👨🏻‍💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.

⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.

🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:

🔹 How to clean and prepare large amounts of data for analysis,

🔹 How to analyze real business data and draw conclusions,

🔹 How to automate repetitive tasks with a few lines of code,

🔹 And improve the speed and accuracy of your analyses significantly.

🌐 #DataScience #DataScience #Pandas #Python

https://t.iss.one/CodeProgrammer ⚡️

💛 Top 10 Best Websites to Learn Machine Learning ⭐️
by [@codeprogrammer]

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🧠 Google’s ML Course
🔗 https://developers.google.com/machine-learning/crash-course

📈 Kaggle Courses
🔗 https://kaggle.com/learn

🧑‍🎓 Coursera – Andrew Ng’s ML Course
🔗 https://coursera.org/learn/machine-learning

⚡️ Fast.ai
🔗 https://fast.ai

🔧 Scikit-Learn Documentation
🔗 https://scikit-learn.org

📹 TensorFlow Tutorials
🔗 https://tensorflow.org/tutorials

🔥 PyTorch Tutorials
🔗 https://docs.pytorch.org/tutorials/

🏛️ MIT OpenCourseWare – Machine Learning
🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/

✍️ Towards Data Science (Blog)
🔗 https://towardsdatascience.com

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💡 Which one are you starting with? Drop a comment below! 👇
#MachineLearning #LearnML #DataScience #AI

https://t.iss.one/CodeProgrammer 🌟

🐱 5 of the Best GitHub Repos
🔃 for Data Scientists

👨🏻‍💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.

▶️ But doing projects was like water on fire and helped me a lot to build my skills.

〰 Repo Awesome Data Analysis

🏷 A complete treasure trove of everything you need to start: SQL, Python, AI, data analysis, and more... In short, if you want to start from zero and strengthen your foundation, start here first.

                   ➖ ➖ ➖

〰 Repo Data Scientist Handbook

🏷 A concise handbook that tells you what you need to learn and what you can ignore for now.

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〰 Repo Cookiecutter Data Science

🏷 A standard project template used by professionals. With this template, you can structure your data analysis and AI projects like a pro.

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〰 Repo Data Science Cookie Cutter

🏷 This is also a very clean project template that teaches you how to build a data project that won’t fall apart tomorrow and can be easily updated. Meaning your projects will be useful in the real world from the start.

                   ➖ ➖ ➖

〰 Repo ML From Scratch

🏷 Here, the main AI algorithms are implemented from scratch in simple language. It’s great for understanding how models really work and for explaining them well in your interviews.

🌐 #Data_Science #DataScience

Data scientists are in high demand right now: there's just too much data to analyze.

In this course, Tatev and Vae teach #Python for #DataScience.

You'll be doing projects and exploring EDA, A/B testing, BI, and more.

https://t.iss.one/Python53 🌟

🤖 Best GitHub repositories to learn AI from scratch in 2026

If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:

1) Karpathy – Neural Networks: Zero to Hero 
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero

2) Hugging Face Transformers 
The main library of modern NLP/LLM: models, tokenizers, fine-tuning 
https://github.com/huggingface/transformers

3) FastAI – Fastbook 
Practical DL training through projects and experiments 
https://github.com/fastai/fastbook

4) Made With ML 
ML as an engineering system: pipelines, production, deployment, monitoring 
https://github.com/GokuMohandas/Made-With-ML

5) Machine Learning System Design (Chip Huyen) 
How to build ML systems in real business: data, metrics, infrastructure 
https://github.com/chiphuyen/machine-learning-systems-design

6) Awesome Generative AI Guide 
A collection of materials on GenAI: from basics to practice 
https://github.com/aishwaryanr/awesome-generative-ai-guide

7) Dive into Deep Learning (D2L) 
One of the best books on DL + code + assignments 
https://github.com/d2l-ai/d2l-en

Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.

#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS

https://t.iss.one/CodeProgrammer