🔖 Sharing a handy cheat sheet on 12 key Git commands — this is the basic set that no developer can do without

tags: #cheatsheet #git

https://t.me/CodeProgrammer 🔗

📊 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.me/DataAnalyticsX 🌟

React 💖 for more amazing content

🎓 OSSU Computer Science — your free Computer Science degree

Open Source Society University (OSSU) is a complete Computer Science curriculum compiled from the best free courses in the world (MIT, Harvard, Princeton). This is not just random videos, but a structured study plan for 2-3 years: from introduction and algorithms to operating systems, databases, and machine learning.

What is important:

➡️ A full Curriculum — the program follows the standard undergraduate CS degree: mathematics, algorithms, architecture, OS, networks, security.

➡️ Top sources — courses from MIT, Stanford, Google, Princeton, and other giants.

➡️ Final Project — at the end of the program, you create a big project to consolidate your knowledge in practice.

🐱 Link to the repository

https://t.me/CodeProgrammer ✉️

🏷 Sections of the «NumPy» library
⬅️ From introductory to advanced


👨🏻‍💻 This is a long-term project to learn Python and NumPy from scratch. The main task is to handle numerical #data and #arrays in #Python using NumPy, and many other libraries are also used.


✏️ This section shows a structured and complete path for learning #NumPy; but the code examples and exercises help to practically memorize the concepts.


⭕️ Introduction to NumPy
🟠 NumPy arrays
⭕️ Introduction to array features
🟠 Basic operations on arrays
⭕️ Functions for statistical and aggregative purposes
🟠 And...

https://t.me/CodeProgrammer ⛈⚡️

If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape.

https://t.me/CodeProgrammer ✅

🚀 #Pandas Cheat Sheet for Everyday Data Work

This covers the essential functions we use in day to day work like inspecting data, selecting rows and columns, cleaning, manipulating and doing quick aggregations.

https://t.me/CodeProgrammer ❤️

𝗜𝗳 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗶𝗻 𝘁𝗼𝘀𝘀𝗲𝘀…

Think again! 🎲

Here’s why it’s a game-changer for anyone in data science, analytics, and decision-making:

➜ Decode Uncertainty

From weather forecasts to financial markets, probability helps us make smarter choices.

➜ Master Essential Distributions

Understand Binomial, Poisson, Normal, and more in the simplest way possible.

➜ Crack Data Science Interviews

#Probability is a key topic in analytics and #machinelearning interviews.

➜ Avoid Common Misconceptions

Learn why "50-50 odds" don’t always mean a fair game.

➜ Visualize Concepts, Not Just Formulas

The best way to learn is through intuitive graphs and real-world examples!

https://t.me/CodeProgrammer ✅

🌟 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.me/CodeProgrammer ⚡️

Good Morning 🌤

Real Python - Pocket Reference (Important)

#python #py #PythonTips #programming

https://t.me/CodeProgrammer 🩵

Machine Learning Fundamentals

A structured Machine Learning Fundamentals guide covering core concepts, intuition, math basics, ML algorithms, deep learning, and real-world workflows.

https://t.me/CodeProgrammer 🎀

I rarely say this, but this is the best repository for mastering Python.

The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).

In this PythonMastery.pdf, all the information is structured
👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf

In the Exercises folder, all the exercises are located
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises

In the Solutions folder — the solutions
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions

👉 @codeprogrammer

The #Python library #PandasAI has been released for simplified data analysis using AI.

You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.

And here's the link 😍

👉 https://t.me/CodeProgrammer

Convert any long article or PDF into a test in a couple of seconds!

Mini-service: we take the text of the article (or extract it from PDF), send it to GPT and receive a set of test questions with answer options and a key.

First, we load the text of the material:

# article_text — this is where we put the text of the article
with open("article.txt", "r", encoding="utf-8") as f:
    article_text = f.read()

# for PDF, you can extract the text in advance with any library (PyPDF2, pdfplumber, etc.)

Next, we ask GPT to generate a test:

prompt = (
    "You are an exam methodologist."
    "Based on this text, create 15 test questions."
    "Each question is in the format:\n"
    "1) Question text\n"
    "A. Option 1\n"
    "B. Option 2\n"
    "C. Option 3\n"
    "D. Option 4\n"
    "Correct answer: <letter>."
    "Do not add explanations and comments, only questions, options, and correct answers."
)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": prompt},
        {"role": "user", "content": article_text}
    ])
print(response.choices[0].message.content.strip())

🔥 Suitable for online courses, educational centers, and corporate training — you immediately get a ready-made bank of tests from any article.

🚪 https://t.me/CodeProgrammer