Data Science
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases
Ko'proq ko'rsatish📈 Telegram kanali Data Science analitikasi
Data Science (@sql_databases) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 70 805 obunachidan iborat bo'lib, Taʼlim toifasida 2 274-o'rinni va Hindiston mintaqasida 4 582-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 70 805 obunachiga ega bo‘ldi.
25 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -309 ga, so‘nggi 24 soatda esa -33 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 12.05% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.78% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 8 533 marta ko‘riladi; birinchi sutkada odatda 1 972 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 0 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent database, learning, linkedin, udemy, 029k| kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 26 Avgust, 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.
Most beginners rush to build models, but seasoned experts know the real work happens before the training starts. If your data is messy, your advanced algorithms are useless.This guide breaks down the two biggest enemies of clean data: 1⃣ Missing Values: These create gaps and bias. You can fix them by removing rows or filling them with the mean, median, or mode. 🔢 Outliers: Extreme values that distort reality. Detect them using Z-scores or IQR, then cap or transform them. The Payoff: In real-world projects, proper cleaning can boost model accuracy by 8% or more.
💡 Pro Tip: Always visualize your data first. A simple plot often reveals issues that raw numbers hide.
Python isn’t just a programming language; it’s a powerhouse for data analytics. With the right tools and a bit of Python magic, your data will become more than just numbers; it’s the story of your success waiting to be told.
