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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 71 041 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, 71 041 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.
71 041
Obunachilar
+624 soatlar
+237 kunlar
-5430 kunlar
Postlar arxiv
71 041
📖 Data Pipelines Overview.
Data pipelines are a fundamental component of managing and processing data efficiently within modern systems. These pipelines typically encompass 5 predominant phases: Collect, Ingest, Store, Compute, and Consume.
1. Collect:
Data is acquired from data stores, data streams, and applications, sourced remotely from devices, applications, or business systems.
2. Ingest:
During the ingestion process, data is loaded into systems and organized within event queues.
3. Store:
Post ingestion, organized data is stored in data warehouses, data lakes, and data lakehouses, along with various systems like databases, ensuring post-ingestion storage.
4. Compute:
Data undergoes aggregation, cleansing, and manipulation to conform to company standards, including tasks such as format conversion, data compression, and partitioning. This phase employs both batch and stream processing techniques.
5. Consume:
Processed data is made available for consumption through analytics and visualization tools, operational data stores, decision engines, user-facing applications, dashboards, data science, machine learning services, business intelligence, and self-service analytics.
The efficiency and effectiveness of each phase contribute to the overall success of data-driven operations within an organization.
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71 041
+3
📖 Big Data Analytics tools
Big Data Analytics tools like Hadoop and Spark enable fast processing of massive datasets, while platforms like Tableau and Power BI help visualize insights. These tools empower businesses to make data-driven decisions in real-time.
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🔅 Data Engineering: dbt for SQL
📝 Learn how you can use dbt (data build tool) to make managing your SQL code simpler and faster.
🌐 Author: Vinoo Ganesh
🔰 Level: Advanced
⏰ Duration: 1h 31m
📋 Topics: Data Build Tool, Data Engineering, SQL
🔗 Join Data Analysis for more courses
71 041
Here are five of the most commonly used SQL queries in data science:
1. SELECT and FROM Clauses
- Basic data retrieval:
SELECT column1, column2 FROM table_name;
2. WHERE Clause
- Filtering data: SELECT * FROM table_name WHERE condition;
3. GROUP BY and Aggregate Functions
- Summarizing data: SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1;
4. JOIN Operations
- Combining data from multiple tables:
SELECT a.column1, b.column2
FROM table1 a
JOIN table2 b ON a.common_column = b.common_column;
SELECT column1
FROM table_name
WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);
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📖 SQL execution order
A SQL query executes its statements in the following order:
1) FROM / JOIN
2) WHERE
3) GROUP BY
4) HAVING
5) SELECT
6) DISTINCT
7) ORDER BY
8) LIMIT / OFFSET
The techniques you implement at each step help speed up the following steps. This is why it’s important to know their execution order. To maximize efficiency, focus on optimizing the steps earlier in the query.
With that in mind, let’s take a look at some optimization tips:
1) Maximize the WHERE clause
This clause is executed early, so it’s a good opportunity to reduce the size of your data set before the rest of the query is processed.
2) Filter your rows before a JOIN
Although the FROM/JOIN occurs first, you can still limit the rows. To limit the number of rows you are joining, use a subquery in the FROM statement instead of a table.
3) Use WHERE over HAVING
The HAVING clause is executed after WHERE & GROUP BY. This means you’re better off moving any appropriate conditions to the WHERE clause when you can.
4) Don’t confuse LIMIT, OFFSET, and DISTINCT for optimization techniques
It’s easy to assume that these would boost performance by minimizing the data set, but this isn’t the case. Because they occur at the end of the query, they make little to no impact on its performance.
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🔅 Distributed Databases with Apache Ignite
📝 Deep dive into learning about and creating distributed databases with Apache Ignite.
🌐 Author: Janani Ravi
🔰 Level: Intermediate
⏰ Duration: 1h 55m
📋 Topics: Apache Ignite, Distributed Databases
🔗 Join Data Analysis for more courses
71 041
📖 Master the Art of Data Storytelling
Data visualization isn’t just about making charts—it’s about telling a story that drives decisions. Here are 15 essential tips to create impactful, clear, and engaging visualizations that your audience will actually understand and remember:
✅ Ask the right questions to uncover meaningful insights
✅ Choose the right chart to match your story
✅ Keep it simple—remove distracting fonts and elements
✅ Use consistent colors and make labels clear and visible
✅ Design for comprehension, not confusion
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Stop Cleaning Data Manually 🛑
Most data scientists spend the majority of their time fighting with messy CSVs and inconsistent formats.
But the pros don’t do it manually. They build pipelines.
A data pipeline is your "set it and forget it" system for data preprocessing.
By using tools like Pandas for manipulation, Scikit-learn for chaining steps, and Dask for scaling, you can slash your manual workload by up to 70%.
Why you need this:
Speed: Go from raw data to insights in seconds.
Reliability: Eliminate human error in the cleaning process.
Reproducibility: Run the same logic on new data without rewriting code.
In a recent healthcare case study, automating this process helped a team predict patient readmission faster and more accurately than ever before.
Which tool is a permanent part of your toolkit?
1. Pandas 🐼
2. Scikit-learn ⚙️
3. Dask ☁️
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🔰 Explaining PostgreSQL
PostgreSQL is a powerful and versatile open-source relational database management system. It offers advanced features, such as support for complex data types, robust concurrency control, and extensive query optimization. With its scalability, reliability, and flexibility, PostgreSQL is an excellent choice for managing and organizing your data efficiently.
