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 71 033 obunachidan iborat bo'lib, Taʼlim toifasida 2 273-o'rinni va Hindiston mintaqasida 4 764-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 71 033 obunachiga ega bo‘ldi.
05 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -54 ga, so‘nggi 24 soatda esa 6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 12.21% 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 8 672 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 06 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 | |
| 05 Iyun | +7 | |||
| 04 Iyun | +15 | |||
| 03 Iyun | 0 | |||
| 02 Iyun | 0 | |||
| 01 Iyun | +14 |
| 2 | @LearnPython3 - Python Data Science Handbook 2nd ed.pdf | 4 079 |
| 3 | 🔰 📙 Python Data Science Handbook 2nd Edition | 4 003 |
| 4 | 📱Data Science
📱Decision Intelligence: Data Stories | 4 842 |
| 5 | 🔅 Decision Intelligence: Data Stories
📝 Learn how to use key lessons from famous data stories around the world to improve decision-making, interpret data effectively, and communicate insights responsibly.
🌐 Author: Franz Buscha
🔰 Level: Beginner
⏰ Duration: 45m
📋 Topics: Data Science, Decision Sciences, Data-driven Decision Making
🔗 Join Data Science for more courses | 4 737 |
| 6 | 🖥 8 Common database types explained | 5 616 |
| 7 | 📖 Learn Database
Databases power everything from websites and apps to enterprise systems. Here’s a learning map that can help you master databases:
1 - Database Fundamentals
This includes topics like “What is a database”, RDBMS, SQL vs NoSQL, ACID vs BASE, OLTP vs OLAP, Transactions, and Isolation Levels.
2 - Data Models and Types
Consists of topics like Relational Databases, Non-Relational Databases, and Data Types (Integer, String, Boolean, Date, JSON, etc).
3 - Querying and Language
This includes topics like SQL Basics (SELECT, INSERT, etc), Advanced SQL (Views, Indexes, CTEs, etc), and NoSQL Querying (Aggregation and Key-Value Lookups).
4 - Indexing and Optimization
Consists of topics like Indexing (B-Tree, Hash, and Bitmaps), Query Execution Plans, Denormalization vs Normalization, Sharding, Connecting Pooling, and Query Batching.
5 - Security, Backups, and Scaling
This includes topics like User Roles, Permissions, Encryption, SQL Injection, High Availability (Replication and Failover), Horizontal vs Vertical Scaling.
6 - Tools and Ecosystem
Consists of topics like Popular SQL Databases, NoSQL Database, GUI Tools, ORMs, Cloud DB services (RDS, DynamoDB, Google Cloud SQL, etc.) | 7 315 |
| 8 | Do you know the real difference between Data Engineering vs. Data Scientists vs. Data Analysts? | 6 696 |
| 9 | 📱Data Science
📱The 80/20 Rule of Data Science | 8 720 |
| 10 | 🔅 The 80/20 Rule of Data Science
📝 Explore the core concepts of the 80/20 rule for data science and how to get most of the value with minimal effort.
🌐 Author: Howard Friedman
🔰 Level: Intermediate
⏰ Duration: 1h 26m
📋 Topics: Data Science, Project Engineering, Team Management
🔗 Join Data Science for more courses | 8 574 |
| 11 | 📖 What are DDL Commands in SQL?
They don’t touch your data — they shape where your data lives.
Use CREATE, ALTER, and DROP to define and change your database structure.
💡 Powerful, essential — and should be used with care! | 8 951 |
| 12 | 🖥 Tableau vs. Power BI | 9 251 |
| 13 | 🖥 4 main database types | 11 203 |
| 14 | 📱Data Science
📱How To Be a Lead Data Scientist | 12 304 |
| 15 | 🔅 How To Be a Lead Data Scientist
📝 Build a foundation and develop skills for seasoned data scientists to level up from model builders to AI leaders.
🌐 Author: Matthew Blasa
🔰 Level: Advanced
⏰ Duration: 1h 6m
📋 Topics: Data Science, Team Management
🔗 Join Data Science for more courses | 11 747 |
| 16 | 😉 A list of the best YouTube videos
✅ To learn data science
1️⃣ SQL language
⬅️ Learning
💰 4-hour SQL course from zero to one hundred
💰 Window functions tutorial
⬅️ Projects
📎 Starting your first SQL project
💰 Data cleansing project
💰 Restaurant order analysis
⬅️ Interview
💰 How to crack the SQL interview?
➖➖➖
2️⃣ Python
⬅️ Learning
💰 12-hour Python for Data Science course
⬅️ Projects
💰 Python project for beginners
💰 Analyzing Corona Data with Python
⬅️ Interview
💰 Python interview golden tricks
💰 Python Interview Questions
➖➖➖
3️⃣ Statistics and machine learning
⬅️ Learning
💰 7-hour course in applied statistics
💰 Machine Learning Training Playlist
⬅️ Projects
💰 Practical ML Project
⬅️ Interview
💰 ML Interview Questions and Answers
💰 How to pass a statistics interview?
➖➖➖
4️⃣ Product and business case studies
⬅️ Learning
💰 Building strong product understanding
💰 Product Metric Definition
⬅️ Interview
💰 Case Study Analysis Framework
💰 How to shine in a business interview? | 10 013 |
| 17 | 🖥 Data Analyst Roadmap | 10 371 |
| 18 | 📖 Merging and Joining Data
Working with multiple datasets? Combine them just like SQL:
# Inner join (default)
merged = pd.merge(df_sales, df_customers, on='customer_id')
# Left join
pd.merge(df_sales, df_customers, on='customer_id', how='left')
# Concatenate vertically
all_data = pd.concat([df_2023, df_2024], ignore_index=True)
# Join on index
df1.join(df2, on='date')
This wraps up our Data Manipulation Using Pandas Series. | 12 399 |
| 19 | 📱Data Science
📱Ethical Hacking: SQL Injection | 11 127 |
| 20 | 🔅 Ethical Hacking: SQL Injection
📝 Learn about the SQL command language and SQL injections. Examine SQL injections in MySQL, SQL Server, and Oracle XE, and discover how attackers defeat web application firewalls.
🌐 Author: Malcolm Shore
🔰 Level: Intermediate
⏰ Duration: 1h 45m
📋 Topics: Ethical Hacking, SQL Injection
🔗 Join Data Science for more courses | 12 116 |
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