This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Python53

Donate us using ⭐️ emoji

📚 Think Stats, 3rd Edition: Master Probability & Statistics with Python! 🐍

#Python #DataScience #Statistics #MachineLearning #DataAnalysis #JupyterNotebooks #OpenSource #LearnToCode #DataVisualization #FreeBook

❤️ Why Grab This Book?
- Hands-On Learning: Transform raw data into insights using Python. Perfect for coders who want to dive into stats without drowning in formulas!
- Real-World Case Studies: Analyze datasets from the National Institutes of Health and more. Learn by doing, not just theorizing!
- Jupyter Notebooks Included: Every chapter comes with interactive notebooks—run code, tweak examples, and solve exercises seamlessly. Ideal for Colab or local setups!
- Cutting-Edge Topics: Explore regression, time series analysis, survival analysis, and Bayesian methods. Level up your data science toolkit!
- Free & Open Source: Licensed under Creative Commons (CC BY-NC-SA). Download the PDF, fork the GitHub repo, and start learning today!

💡 Perfect For:
- Python devs eager to add stats/data skills.
- Data enthusiasts craving practical, code-driven analysis.
- Educators seeking a modern, interactive stats resource.

🚀 Get It Now:
https://allendowney.github.io/ThinkStats/

📂 GitHub Repo: https://github.com/AllenDowney/ThinkStats2

#DataDriven #CodeFirst #StatsInPython #FreeLearning — Turn data into wisdom, one line of Python at a time! ⭐️📊

https://t.iss.one/CodeProgrammer ✈️

Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .

The guide also addresses ChatGPT’s limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.

Tags:
#ChatGPT #DataScience #CheatSheet #2025Edition #DataCamp #Python #MachineLearning #DataAnalysis #Automation #NLP #SQL

https://t.iss.one/CodeProgrammer ⭐️