Data Science & Machine Learning Resources
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free Admin: @love_data Buy ads: https://telega.io/c/datalemur
Ko'proq ko'rsatish📈 Telegram kanali Data Science & Machine Learning Resources analitikasi
Data Science & Machine Learning Resources (@datalemur) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 20 509 obunachidan iborat bo'lib, Taʼlim toifasida 9 818-o'rinni va Hindiston mintaqasida 21 395-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 20 509 obunachiga ega bo‘ldi.
16 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 211 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.15% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 442 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent |--, learning, insidead, database, sql kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
Admin: @love_data
Buy ads: https://telega.io/c/datalemur”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 17 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
Ma'lumot yuklanmoqda...
| Sana | Obunachilarni jalb qilish | Esdaliklar | Kanallar | |
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| 2 | SQL vs Python Programming: Quick Comparison ✍
📌 SQL Programming
• Query data from databases
• Filter, join, aggregate rows
Best fields
• Data Analytics
• Business Intelligence
• Reporting and MIS
• Entry-level Data Engineering
Job titles
• Data Analyst
• Business Analyst
• BI Analyst
• SQL Developer
Hiring reality
• Asked in most analyst interviews
• Used daily in analyst roles
India salary range
• Fresher: 4–8 LPA
• Mid-level: 8–15 LPA
Real tasks
• Monthly sales report
• Top customers by revenue
• Duplicate removal
📌 Python Programming
• Clean and analyze data
• Automate workflows
• Build models
Where you work
• Notebooks
• Scripts
• ML pipelines
Best fields
• Data Science
• Machine Learning
• Automation
• Advanced Analytics
Job titles
• Data Scientist
• ML Engineer
• Analytics Engineer
• Python Developer
Hiring reality
• Common in mid to senior roles
• Strong demand in AI teams
India salary range
• Fresher: 6–10 LPA
• Mid-level: 12–25 LPA
Real tasks
• Churn prediction
• Report automation
• File handling CSV, Excel, JSON
⚔️ Quick comparison
• Data source
SQL stays inside databases
Python pulls data from anywhere
• Speed
SQL runs fast on large tables
Python slows with raw big data
• Learning
SQL is beginner-friendly
Python needs coding basics
🎯 Role-based choice
• Data Analyst
SQL required
Python adds value
• Data Scientist
Python required
SQL used to fetch data
• Business Analyst
SQL works for most roles
Python helps automate work
• Data Engineer
SQL for pipelines
Python for processing
✅ Best career move
• Learn SQL first for entry
• Add Python for growth
• Use both in real projects
Which one do you prefer?
SQL 👍
Python ❤️
Both 🙏
None 😮 | 626 |
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| 4 | 📊 Data Science Roadmap 🚀
📂 Start Here
∟📂 What is Data Science & Why It Matters?
∟📂 Roles (Data Analyst, Data Scientist, ML Engineer)
∟📂 Setting Up Environment (Python, Jupyter Notebook)
📂 Python for Data Science
∟📂 Python Basics (Variables, Loops, Functions)
∟📂 NumPy for Numerical Computing
∟📂 Pandas for Data Analysis
📂 Data Cleaning & Preparation
∟📂 Handling Missing Values
∟📂 Data Transformation
∟📂 Feature Engineering
📂 Exploratory Data Analysis (EDA)
∟📂 Descriptive Statistics
∟📂 Data Visualization (Matplotlib, Seaborn)
∟📂 Finding Patterns & Insights
📂 Statistics & Probability
∟📂 Mean, Median, Mode, Variance
∟📂 Probability Basics
∟📂 Hypothesis Testing
📂 Machine Learning Basics
∟📂 Supervised Learning (Regression, Classification)
∟📂 Unsupervised Learning (Clustering)
∟📂 Model Evaluation (Accuracy, Precision, Recall)
📂 Machine Learning Algorithms
∟📂 Linear Regression
∟📂 Decision Trees & Random Forest
∟📂 K-Means Clustering
📂 Model Building & Deployment
∟📂 Train-Test Split
∟📂 Cross Validation
∟📂 Deploy Models (Flask / FastAPI)
📂 Big Data & Tools
∟📂 SQL for Data Handling
∟📂 Introduction to Big Data (Hadoop, Spark)
∟📂 Version Control (Git & GitHub)
📂 Practice Projects
∟📌 House Price Prediction
∟📌 Customer Segmentation
∟📌 Sales Forecasting Model
📂 ✅ Move to Next Level
∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch)
∟📂 NLP (Text Analysis, Chatbots)
∟📂 MLOps & Model Optimization
Data Science Resources: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
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| 6 | ChatGPT Prompts Book
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| 7 | Most people who have valuable knowledge never turn it into a course
Not because they can’t — but because it feels too complicated
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