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Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data

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Сеть:Data Analytics Индия2 340 Технологии и приложения1 122...

📈 Аналитический обзор Telegram-канала Data Analytics

Канал Data Analytics (@sqlspecialist) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 109 681 подписчиков, занимая 1 122 место в категории Технологии и приложения и 2 340 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 109 681 подписчиков.

Согласно последним данным от 24 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 584, а за последние 24 часа — 71, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.76%. В первые 24 часа после публикации контент обычно набирает 0.68% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 3 024 просмотров. В течение первых суток публикация набирает 743 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 8.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как row, sql, analytic, analyst, visualization.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data”

Благодаря высокой частоте обновлений (последние данные получены 25 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

109 681

Подписчики

+7124 часа

+267 дней

+58430 день

3 024

Просмотры поста

~ 74324 часа

~ 1 04548 часов

2.76%

Коэффициент вовлеченности

~ 2

Постов в день

Ads index

beta

Архив постов

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𝟯𝟬 𝗠𝗼𝘀𝘁 𝗖𝗼𝗺𝗺𝗼𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗬𝗼𝘂 𝗠𝘂𝘀𝘁 𝗞𝗻𝗼𝘄!😍 Are you preparing for a Data Analytics interview?🗣 Hiring managers often ask a mix of technical & problem-solving questions to evaluate your skills in SQL, Python, Excel, data visualization, & case studies🎯 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4hbmjxf Which question do you find the toughest? Drop a comment below!⬇️

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Which of the following aggregate function is used to find smallest value in SQL?

Anonymous voting

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Aggregation Functions in SQL Aggregation functions help summarize data by performing calculations like sum, average, count, and more. These functions are commonly used in data analysis. 1️⃣ Common Aggregation Functions COUNT() → Counts the number of rows SUM() → Calculates the total sum of a numeric column AVG() → Finds the average value MIN() → Returns the smallest value MAX() → Returns the largest value 2️⃣ Using COUNT() to Count Records 🔹 Find the total number of employees SELECT COUNT(*) FROM employees; 🔹 Find the number of employees in the ‘Sales’ department SELECT COUNT(*) FROM employees WHERE department = 'Sales'; 3️⃣ Using SUM() to Calculate Totals 🔹 Find the total salary of all employees SELECT SUM(salary) FROM employees; 🔹 Find the total salary paid to employees in the ‘IT’ department SELECT SUM(salary) FROM employees WHERE department = 'IT'; 4️⃣ Using AVG() to Calculate Averages 🔹 Find the average salary of all employees SELECT AVG(salary) FROM employees; 🔹 Find the average salary of employees in the ‘HR’ department SELECT AVG(salary) FROM employees WHERE department = 'HR'; 5️⃣ Using MIN() and MAX() to Find Extremes 🔹 Find the lowest salary in the company SELECT MIN(salary) FROM employees; 🔹 Find the highest salary in the company SELECT MAX(salary) FROM employees; 🔹 Find the most recently hired employee (latest hire date) SELECT MAX(hire_date) FROM employees; 6️⃣ Using Aggregation Functions with GROUP BY Aggregation functions are often used with GROUP BY to analyze data by categories. 🔹 Find the total salary for each department SELECT department, SUM(salary) FROM employees GROUP BY department; 🔹 Find the average salary for each job title SELECT job_title, AVG(salary) FROM employees GROUP BY job_title; Mini Task for You: Write an SQL query to find the highest salary in each department. You can find free SQL Resources here 👇👇 https://t.me/mysqldata Like this post if you want me to continue covering all the topics! ❤️ Share with credits: https://t.me/sqlspecialist Hope it helps :) #sql

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SQL Interview Questions with detailed answers 1️⃣8️⃣ Write an SQL query to find customers who have placed more than 3 orders. To find customers who have placed more than 3 orders, we can use the GROUP BY and HAVING clauses to count the number of orders per customer.

SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 3;

Explanation: 1️⃣ GROUP BY customer_id groups all orders by each customer. 2️⃣ COUNT(order_id) counts the number of orders per customer. 3️⃣ HAVING COUNT(order_id) > 3 filters only those customers who have placed more than 3 orders. If you also want customer names, you can join this with a customers table:

SELECT c.customer_id, c.customer_name, COUNT(o.order_id) AS total_orders
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.customer_name
HAVING COUNT(o.order_id) > 3;

Top 20 SQL Interview Questions Like this post if you want me to continue this SQL Interview Series♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Topic 2: Filtering & Advanced WHERE Clause in SQL Filtering data efficiently is crucial in data analysis. The WHERE clause helps filter rows based on conditions. Let’s explore some advanced filtering techniques. 1️⃣ Using Comparison Operators in WHERE Clause = → Equal to → Example: WHERE department = 'Sales' != or <> → Not equal to → Example: WHERE salary <> 50000 > and < → Greater than / Less than → Example: WHERE age > 30 >= and <= → Greater than or equal to / Less than or equal to → Example: WHERE experience >= 5 🔹 Example: Get all employees who earn more than $50,000 SELECT * FROM employees WHERE salary > 50000; 2️⃣ Using Logical Operators (AND, OR, NOT) AND → Returns results when both conditions are TRUE SELECT * FROM employees WHERE department = 'IT' AND salary > 70000; OR → Returns results when at least one condition is TRUE SELECT * FROM employees WHERE department = 'IT' OR department = 'HR'; NOT → Excludes results that match the condition

SELECT * FROM employees WHERE NOT department = 'Finance';

3️⃣ Using BETWEEN for Range Filtering BETWEEN → Selects values within a specific range SELECT * FROM employees WHERE salary BETWEEN 40000 AND 80000; BETWEEN can also be used for dates SELECT * FROM employees WHERE hire_date BETWEEN '2020-01-01' AND '2023-12-31'; 4️⃣ Using IN for Multiple Matches IN is used when filtering data that matches multiple values

SELECT * FROM employees WHERE department IN ('IT', 'HR', 'Sales');

Example: Find employees whose job title is either ‘Manager’ or ‘Analyst’ SELECT * FROM employees WHERE job_title IN ('Manager', 'Analyst'); 5️⃣ Using LIKE & Wildcards for Pattern Matching % → Represents zero or more characters _ → Represents exactly one character 🔹 Find employees whose name starts with ‘J’ SELECT * FROM employees WHERE name LIKE 'J%'; 🔹 Find employees whose name ends with ‘son’

SELECT * FROM employees WHERE name LIKE '%son';

🔹 Find employees with ‘an’ anywhere in their name

SELECT * FROM employees WHERE name LIKE '%an%';

Mini Task for You: Write an SQL query to find employees who work in either "Marketing" or "Sales" and earn more than $60,000. You can find free SQL Resources here 👇👇 https://t.me/mysqldata Like this post if you want me to continue covering all the topics! ❤️ Share with credits: https://t.me/sqlspecialist Hope it helps :) #sql

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Which of the following operator is used in a WHERE clause to search for a specified pattern in a column?

Anonymous voting

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SQL Interview Questions with detailed answers 1️⃣7️⃣ What is indexing in SQL, and how does it improve performance? An index in SQL is a data structure that improves query performance by allowing faster data retrieval. It works like an index in a book, helping the database find records quickly instead of scanning the entire table. How Indexing Improves Performance 1️⃣ Speeds Up Searches – Instead of scanning every row, the database uses the index to locate data faster. 2️⃣ Optimizes Joins – Indexed columns in JOIN conditions improve performance. 3️⃣ Enhances Filtering – WHERE clauses execute faster when filtering by an indexed column. 4️⃣ Reduces Sorting Overhead – Indexing helps when using ORDER BY or GROUP BY. Creating an Index CREATE INDEX idx_employee_name ON employees(name); This creates an index on the name column, making searches like WHERE name = 'John' much faster. Types of Indexes ✅ Primary Index – Automatically created for PRIMARY KEY. ✅ Unique Index – Ensures uniqueness of values in a column. ✅ Composite Index – Index on multiple columns. ✅ Full-Text Index – Optimized for searching text data. When Not to Use Indexes ❌ On small tables – Scanning is often faster than using an index. ❌ On frequently updated columns – Index maintenance can slow down INSERT, UPDATE, and DELETE operations. ❌ If the query retrieves most rows – Indexing works best for selective queries, not full table scans. Top 20 SQL Interview Questions Like this post if you want me to continue this SQL Interview Series♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Which of the following is not a DAX Function in Power BI?

Anonymous voting

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Which of the following tool is not used for data analytics?

Anonymous voting

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SQL Interview Questions with detailed answers: 1️⃣6️⃣ How do you optimize a slow SQL query? Optimizing SQL queries is essential for improving database performance. Here are key techniques to speed up slow queries: 1️⃣ Use Indexing Indexes help the database retrieve data faster. Adding an index on frequently used columns can improve performance.

CREATE INDEX idx_employee_id ON employees(employee_id);

2️⃣ Avoid SELECT * Fetching unnecessary columns slows down queries. Select only required columns instead of using SELECT *.

SELECT employee_id, name FROM employees;

3️⃣ Use EXISTS Instead of IN EXISTS is faster than IN when dealing with subqueries because it stops checking once it finds a match.

SELECT name FROM employees e WHERE EXISTS (SELECT 1 FROM departments d WHERE d.department_id = e.department_id);

4️⃣ Optimize Joins Use appropriate join types (INNER JOIN, LEFT JOIN, etc.) and ensure the joined columns are indexed.

SELECT e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.department_id;

5️⃣ Use LIMIT for Large Datasets If you only need a subset of data, use LIMIT to fetch fewer rows.

SELECT * FROM employees LIMIT 100;

6️⃣ Partition Large Tables Partitioning helps divide large tables into smaller chunks, improving query performance.

CREATE TABLE employees_2024 PARTITION OF employees FOR VALUES FROM ('2024-01-01') TO ('2024-12-31');

7️⃣ Analyze and Use Query Execution Plans Use EXPLAIN ANALYZE to understand how a query is executed and find bottlenecks.

EXPLAIN ANALYZE SELECT * FROM employees WHERE salary > 50000;

Optimizing queries depends on the database structure and data size. Top 20 SQL Interview Questions Like this post if you want me to continue this SQL Interview Series♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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If you want to Excel in Data Science and become an expert, master these essential concepts: Core Data Science Skills: • Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn • SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions • Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates • Exploratory Data Analysis (EDA) – Visualizing data trends Machine Learning (ML): • Supervised Learning – Linear Regression, Decision Trees, Random Forest • Unsupervised Learning – Clustering, PCA, Anomaly Detection • Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC • Hyperparameter Tuning – Grid Search, Random Search Deep Learning (DL): • Neural Networks – TensorFlow, PyTorch, Keras • CNNs & RNNs – Image & sequential data processing • Transformers & LLMs – GPT, BERT, Stable Diffusion Big Data & Cloud Computing: • Hadoop & Spark – Handling large datasets • AWS, GCP, Azure – Cloud-based data science solutions • MLOps – Deploy models using Flask, FastAPI, Docker Statistics & Mathematics for Data Science: • Probability & Hypothesis Testing – P-values, T-tests, Chi-square • Linear Algebra & Calculus – Matrices, Vectors, Derivatives • Time Series Analysis – ARIMA, Prophet, LSTMs Real-World Applications: • Recommendation Systems – Personalized AI suggestions • NLP (Natural Language Processing) – Sentiment Analysis, Chatbots • AI-Powered Business Insights – Data-driven decision-making Like this post if you need a complete tutorial on essential data science topics! 👍❤️

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If you want to Excel at Tableau and become a data visualization expert, master these essential features: • Calculated Fields – Create custom metrics • LOD Expressions – FIXED, INCLUDE, EXCLUDE for advanced aggregations • Table Calculations – RANK(), WINDOW_SUM(), RUNNING_TOTAL() • Data Blending vs. Joins – Combine data efficiently • Parameters – Create interactive dashboards • Dual-Axis & Combined Charts – Advanced visual storytelling • Filters & Context Filters – Optimize performance • Dashboard Actions – Make reports interactive • Storytelling with Data – Present insights effectively • Performance Optimization – Speed up slow dashboards Free Tableau Resources: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t Like it if you need a complete tutorial on all these topics! 👍❤️

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