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Data Science
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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 71 067 obunachidan iborat bo'lib, Taʼlim toifasida 2 281-o'rinni va Hindiston mintaqasida 4 735-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 71 067 obunachiga ega bo‘ldi.
06 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -16 ga, so‘nggi 24 soatda esa 26 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 11.78% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.97% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 8 369 marta ko‘riladi; birinchi sutkada odatda 2 110 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 08 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.
71 067
Obunachilar
+2624 soatlar
+477 kunlar
-1630 kunlar
Postlar arxiv
71 064
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🌐 Author: David Gassner
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📋 Topics: SQL
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📊 9 Key Database Types
🌍 Spatial: Stores and queries location data (PostGIS, MongoDB Spatial).
🔗 Blockchain: Secure, immutable ledgers (BigchainDB, IBM Blockchain).
🌐 Distributed: Scales across servers (Cassandra, Amazon DynamoDB).
⚡️ In-Memory: Lightning-fast access (Redis, Memcached, H2).
🗂 NoSQL: Flexible, schema-free (MongoDB, Couchbase, HBase).
📋 Relational: Structured with tables & SQL (MySQL, PostgreSQL, Oracle).
🧩 Object-Oriented: Models complex objects (db4o, Object DB).
🕸 Graph: Perfect for relationships (Neo4j, Amazon Neptune).
⏱️ Time-Series: Optimized for timestamps (InfluxDB, Prometheus).
Pick the right tool for your data challenge.
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+3
Here’s a practical, code-first playbook for exploring numerical data 👇
How we approach EDA (with Python code + outputs):
- Basics: shape, dtypes, and missing values.
- Descriptives: mean/median, variance, and percentiles for quick sanity checks.
- Distributions: histograms, boxplots, density to spot skew and spread.
- Relationships: scatter plots and a correlation heatmap to find signals.
- Outliers: z-scores/IQR to flag anomalies worth investigating.
- Scaling: MinMax vs Z-score depending on the model and metric.
- Segments: groupby comparisons to surface patterns you miss in globals.
- Decisions: tie insights back to the question and next steps.
What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.
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📁 Master Excel like a pro
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