Data Analytics

If you want to become a Data Analyst or Data Scientist, these are the Python concepts you should master first: 🧹 Data Cleaning ✔️ dropna() → remove missing values ✔️ fillna() → handle nulls properly ✔️ astype() → fix data types ✔️ unique() → explore categories 📊 Exploratory Data Analysis (EDA) ✔️ describe() → quick statistics ✔️ groupby() → analyze patterns ✔️ corr() → find relationships ✔️ hist() & scatter() → visualize distributions 📈 Data Visualization ✔️ bar() → compare categories ✔️ sns.barplot() → statistical plots ✔️ sns.lineplot() → trends over time ✔️ plotly.express.scatter() → interactive charts

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ChatGPT vs Claude vs Gemini https://t.me/DataAnalyticsX

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🔖 10 Stanford courses on AI and ML — with official pages and all materials ▶️ CS221: Artificial Intelligence ▶️ CS229: Machine Learning ▶️ CS229M: Theory of Machine Learning ▶️ CS230: Deep Learning ▶️ CS234: Reinforcement Learning ▶️ CS224N: Natural Language Processing ▶️ CS231N: Deep Learning for Computer Vision ▶️ CME295: Large Language Models ▶️ CS236: Deep Generative Models ▶️ CS336: Modeling Language from Scratch They cover the entire spectrum: classic ML, LLM, and generative models — with theory and practice. tags: #python #ML #LLM #AI ➡ https://t.me/MachineLearning9

🚨 26 Claude Code slash commands you should know already. Save this post ! Most Claude Code users are only using 10% of what it can do Here are the shortcuts that change how you work : 📌 Session control: → /compact [instructions]: compresses your conversation, you control what stays → /resume [session]: pick up exactly where you left off → /branch [name]: explore an alternative path without touching your main session → /rewind: roll back to an earlier point in code or conversation 📌 Cost and usage: → /cost: see token usage for the session → /usage: check your plan limits and rate limit status → /extra-usage: keep working even when rate limits hit 📌 Project setup: → /init: creates a CLAUDE.md guide for your project → /memory: edits CLAUDE.md files, enables or disables auto-memory → /add-dir <path>: adds a working directory for file access 📌 Code quality: → /diff: interactive diff viewer, uncommitted changes and per-turn diffs → /security-review: scans pending changes for vulnerabilities → /plan [description]: enters plan mode before Claude writes a single line 📌 Advanced control: → /permissions: manages allow, ask, and deny rules for tool use → /agents: configures sub-agent behavior → /mcp: manages MCP server connections and OAuth → /skills: lists all available skills, built-in and custom Most people open Claude Code and just start typing The ones shipping faster are using these before they write the first prompt Which one did you not know about? ♻️ Repost if this helped someone on your team

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🚀 Python isn’t just a programming language… it’s a career accelerator. From data science to web development, automation to AI — Python is powering it all. Here’s the reality 👇 When you combine Python with the right tools, you unlock entire industries: ▪️ Python + Pandas → Data manipulation ▪️ Python + Scikit-learn → Machine learning ▪️ Python + TensorFlow → Deep learning ▪️ Python + Matplotlib / Seaborn → Data visualization ▪️ Python + BeautifulSoup → Web scraping ▪️ Python + Selenium → Browser automation ▪️ Python + FastAPI → High-performance APIs ▪️ Python + SQLAlchemy → Database access ▪️ Python + Flask / Django → Web development ▪️ Python + OpenCV → Computer vision ▪️ Python + Pygame → Game development 💡 One language. Endless possibilities.

Pandas Cheatsheet for Data Analysis

𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗰𝗿𝗮𝗰𝗸 𝗔𝗜/𝗠𝗟 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀. 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮 𝘀𝗵𝗮𝗿𝗽 𝗽𝗿𝗲𝗽 𝘀𝘁𝗮𝗰𝗸. Most people consume content. Top candidates train with the right repos. Here’s a tight 9-repo stack that actually moves the needle: 𝟭 ⤷ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗿𝗶𝗺𝗲𝗿 (344k ⭐️) https://lnkd.in/grVdK9X5 ↳ Scale, trade-offs, real architecture thinking ↳ What you say in design rounds starts here 𝟮 ⤷ 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 (220k ⭐️) https://lnkd.in/gdeUgjsi ↳ Clean implementations ↳ Sharpens fundamentals for coding + ML logic 𝟯 ⤷ 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 (139k ⭐️) https://lnkd.in/gmwpV865 ↳ End-to-end prep: DSA + design + behavioral ↳ Use it to structure your weeks 𝟰 ⤷ 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗠𝗟 (72.3k ⭐️) https://lnkd.in/gSGVvmBB ↳ Curated ML resources, papers, tools ↳ Fill gaps fast without noise 𝟱 ⤷ 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (76.6k ⭐️) https://lnkd.in/gt3R-nhm ↳ Real systems, real trade-offs ↳ Turns theory into production thinking 𝟲 ⤷ 𝟭𝟬𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗠𝗟 (50.7k ⭐️) https://lnkd.in/gyBQF3dv ↳ Daily reps → consistency ↳ Concepts + code together 𝟳 ⤷ 𝗠𝗮𝘁𝗵 𝗳𝗼𝗿 𝗠𝗟 (15.4k ⭐️) https://lnkd.in/g_TYTu5J ↳ Linear algebra, stats, optimization ↳ Clears “I kinda get it” confusion 𝟴 ⤷ 𝗠𝗟 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 (8.1k ⭐️) https://lnkd.in/dTt3syu2 ↳ Focused ML interview questions ↳ Practice how you’ll actually be evaluated 𝟵 ⤷ 𝟲𝟱 𝗠𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘀 (4.6k ⭐️) https://lnkd.in/dcT9EksV ↳ Fast revision set ↳ Perfect before interviews 𝟭𝟬 ⤷ 𝗛𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆 Fundamentals → Algorithms + Math ▼ System Design → Scalability ▼ ML Concepts → Practice Questions ▼ Daily Coding → Consistency No jumping. No chaos. Just progression. 𝟭𝟭 ⤷ 𝗪𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝘁𝗼𝗽 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 ↳ They don’t collect resources ↳ They reuse a small, high-signal stack ↳ They practice like it’s a system, not a mood That’s the edge. 𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝟱𝟬 𝗿𝗲𝗽𝗼𝘀. 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝟵 𝗮𝗻𝗱 𝗮 𝗽𝗹𝗮𝗻.

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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.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

🔖 3 websites with tasks for improving ML skills A good selection for those who want to improve their skills in practice, rather than just reading theory: ▶️ Deep-ML — a complete stack from matrices to neural networks; ▶️ Tensorgym — practical exercises in ML; ▶️ NeetCode ML — the ML section from the authors of a well-known platform for preparing for interviews. tags: #ML #DataScience #DataAnalysis ➡ https://t.me/CodeProgrammer