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
Mostrar más📈 Análisis del canal de Telegram Data Science
El canal Data Science (@sql_databases) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 70 803 suscriptores, ocupando la posición 2 261 en la categoría Educación y el puesto 4 562 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 70 803 suscriptores.
Según los últimos datos del 26 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -310, y en las últimas 24 horas de -15, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 11.21%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.74% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 7 934 visualizaciones. En el primer día suele acumular 1 943 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
- Intereses temáticos: El contenido se centra en temas clave como database, learning, linkedin, udemy, 029k|.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
70 803
Suscriptores
-1524 horas
-1277 días
-31030 días
Archivo de publicaciones
70 803
🔅 Advanced Hands-On Python: Working with Excel and Spreadsheet Data
📝 This course demonstrates ways to use Python to work with Excel and spreadsheet data, such as reading, writing, and converting content and working with Excel workbooks, sheet data, and formulas.
🌐 Author: Joe Marini
🔰 Level: Intermediate
⏰ Duration: 2h 45m
📋 Topics: Pandas, Data Analysis, Microsoft Excel
🔗 Join Data Analysis for more courses
70 803
+3
📖 SQL Joins - Part 1
📍Types of joins used very often includes -
✔️LEFT JOIN - All data from the left table but common data from the right table
✔️RIGHT JOIN - All data from right table and common data from the left table
✔️INNER JOIN - Only common data from both the tables
✔️OUTER JOIN - All the data from both the tables keeping null values with no common keys
✔️UNION - Stack table data on top of one another
✔️CROSS JOIN - All possible combinations of data from both the tables
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📖 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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📖 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
70 803
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.
