SQL Programming Resources
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Find top SQL resources from global universities, cool projects, and learning materials for data analytics. Admin: @coderfun Useful links: heylink.me/DataAnalytics Promotions: @love_data
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网络:📈 Telegram 频道 SQL Programming Resources 的分析概览
频道 SQL Programming Resources (@sqlanalyst) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 76 618 名订阅者，在 技术与应用 类别中位列第 1 633，并在 印度 地区排名第 4 120 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 76 618 名订阅者。
根据 25 八月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 228，过去 24 小时变化为 8，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 2.19%。内容发布后 24 小时内通常能获得 1.04% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 678 次浏览，首日通常累积 796 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 row, sql, customer_id, logic, desc 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Find top SQL resources from global universities, cool projects, and learning materials for data analytics.
Admin: @coderfun
Useful links: heylink.me/DataAnalytics
Promotions: @love_data”
凭借高频更新（最新数据采集于 26 八月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
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频道帖子
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| 4 | • Customers, Subscriptions, Usage, Billing, Support tickets
• KPIs: Churn Rate, Retention Rate, ARPU, CLV, MRR
Project 3 — Banking Analytics
• Customers, Accounts, Transactions, Loans, Branches
• KPIs: Deposits, Withdrawals, Transaction Volume, Average Balance, Loan Exposure
Project 4 — Marketing Analytics
• Campaigns, Leads, Customers, Conversions, Revenue
• KPIs: Conversion Rate, CAC, CPL, CPA, ROI, Revenue per Channel
🔴 Month 6 — Interview & Job Preparation
Week 21: SQL Interview Fundamentals
• SELECT, WHERE, GROUP BY, HAVING, CASE, Joins, Subqueries
• Target: 50+ questions
Week 22: Advanced Interview Questions
• Window functions, CTEs, Ranking, LAG / LEAD, Running totals, Date calculations, Cohort analysis
• Target: 50+ questions
Week 23: Real-World Scenarios
• Customers who purchased in consecutive months
• Second-highest salary in each department
• Monthly retention
• Top 3 products by revenue for every month
• Customers whose spending increased month over month
🏆 Week 24 — Final SQL Challenge
• Raw Data → Database Design → Data Cleaning → SQL Analysis → Business KPIs → Insights → Dashboard → Business Recommendations
📚 SQL Topics Checklist
Beginner:
• SELECT, DISTINCT, WHERE, ORDER BY, LIMIT, AND / OR, IN, BETWEEN, LIKE, NULL
Intermediate:
• GROUP BY, HAVING, CASE, Aggregate functions, String functions, Date functions, Joins, Subqueries, CTEs
Advanced:
• Window functions, ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, Running totals, Moving averages, Cohort analysis, Retention analysis, Funnel analysis, Gaps & islands, Recursive CTEs
Data Analyst SQL:
• Revenue analysis, Customer analytics, Product analytics, Marketing analytics, Churn analysis, Cohort analysis, RFM analysis, KPI calculations, Business scenario analysis
⏱️ Double Tap ❤️ For Detailed Explanation of each topic
-----
2.13 ₽ · /balance_help | 1 511 |
| 5 | 🚀 Complete SQL Roadmap to Learn SQL in 2026
🗺️ 6-Month SQL Roadmap
🟢 Month 1 — SQL Fundamentals
Start by understanding how relational databases work.
Week 1: Database Basics
• What is SQL?
• SQL vs MySQL vs PostgreSQL vs SQL Server
• Database, table, row and column
• Primary keys
• Foreign keys
• Relationships
• NULL values
• Data types
• Relational databases
• Basic database design
Week 2: Basic Queries
Master:
• SELECT
• DISTINCT
• WHERE
• AND / OR
• NOT
• IN
• BETWEEN
• LIKE
• IS NULL / IS NOT NULL
• ORDER BY
• LIMIT
Week 3: SQL Functions
• COUNT(), SUM(), AVG(), MIN(), MAX()
• String functions: CONCAT(), UPPER(), LOWER(), LENGTH(), SUBSTRING()
• Date functions: CURRENT_DATE, DATE_PART / EXTRACT, DATE_TRUNC, DATE_DIFF equivalents
Week 4: GROUP BY & HAVING
• GROUP BY
• HAVING
• Revenue by category
• Employees by department
• Average salary by department
🟡 Month 2 — Intermediate SQL
Week 5: CASE Statements
• CASE WHEN THEN ELSE END
• Customer segmentation
• Salary bands
• Order status classification
• Profit categories
• Age groups
Week 6: Joins
Master:
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL OUTER JOIN
• CROSS JOIN
• Self JOIN
Week 7: Subqueries
• Scalar subqueries
• Multi-row subqueries
• Correlated subqueries
• EXISTS / NOT EXISTS
• IN / NOT IN
Week 8: CTEs
• WITH cte AS (...) SELECT ... FROM cte
• Multi-step revenue analysis
• Customer segmentation
• Funnel analysis
• Cohort analysis
🟠 Month 3 — Advanced SQL
Week 9: Window Functions
• ROW_NUMBER(), RANK(), DENSE_RANK()
• LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE()
Week 10: Advanced Aggregations
• Conditional aggregation
• Multiple aggregations
• DISTINCT aggregation
• Aggregation with CASE
• GROUP BY with multiple dimensions
Week 11: Date & Time Analytics
• Daily / Weekly / Monthly / Quarterly / Yearly metrics
• Month-over-month growth
• Year-over-year growth
• Date differences
• Customer tenure
• Time between events
Week 12: Advanced SQL Patterns
• Top N per group
• Gaps and islands
• Running totals
• Moving averages
• Consecutive records
• Duplicate detection
• Missing records
• First/last record
• Latest record per customer
🔵 Month 4 — SQL for Data Analytics
Week 13: Sales Analytics
• Revenue, Orders, AOV, Product / Category performance, Customer revenue, Monthly growth, Profit margin
• KPIs: Revenue, Orders, AOV, Gross Profit, Profit Margin, Units Sold, Repeat Purchase Rate
Week 14: Customer Analytics
• New / Existing / Repeat customers
• Customer retention / churn
• Customer lifetime value
• RFM analysis
Week 15: Marketing Analytics
• Leads, Campaigns, Conversions, Marketing channels
• CAC, CPL, CPA, Conversion rate, Campaign ROI
• Funnel: Impressions → Clicks → Leads → Signups → Purchases
Week 16: Product Analytics
• DAU, WAU, MAU
• Retention, Churn
• Feature adoption, Activation
• Conversion funnel, Cohort analysis
🟣 Month 5 — Real-World SQL Projects
Build at least 4 complete projects.
Project 1 — E-Commerce Analytics
• Customers, Orders, Products, Revenue, Profit, Discounts
• KPIs: Revenue, AOV, Profit, Margin, Repeat Purchase Rate, CLV
Project 2 — Customer Churn | 1 073 |
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| 7 | 10 Advanced SQL Concepts For Data Analysts
1. Window Functions for Advanced Analytics:
Calculate running totals, ranks, and moving averages without subqueries.
SELECT date, sales, SUM(sales) OVER (ORDER BY date) AS running_total FROM sales_data;
2. Conditional Aggregation with CASE WHEN:
Segment data within a single query, saving time and creating versatile summaries.
SELECT COUNT(CASE WHEN status = 'Completed' THEN 1 END) AS completed_orders FROM orders;
3. CTEs for Modular Queries:
Make complex queries more readable and reusable with CTEs.
WITH filtered_sales AS (SELECT * FROM sales_data WHERE region = 'North')
SELECT product, SUM(sales) FROM filtered_sales GROUP BY product;
4. Optimize with EXISTS vs. IN:
Use EXISTS for better performance in larger datasets.
SELECT * FROM customers c WHERE EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id);
5. Self Joins for Row Comparisons:
Compare rows within the same table, helpful for changes over time.
SELECT a.date, (a.sales - b.sales) AS sales_diff FROM sales_data a JOIN sales_data b ON a.date = b.date + INTERVAL '1' MONTH;
6. UNION vs. UNION ALL:
Combine results from multiple queries; UNION ALL is faster as it doesn’t remove duplicates.
7. Handle NULLs with COALESCE:
Replace NULLs with defaults to avoid calculation issues.
SELECT product, COALESCE(sales, 0) AS sales FROM product_sales;
8. Pivot Data with CASE Statements:
Transform rows into columns for clearer insights.
9. Extract Data with STRING Functions:
Useful for semi-structured data; extract domains, product codes, etc.
SELECT SUBSTRING(email, CHARINDEX('@', email) + 1, LEN(email)) AS domain FROM users;
10. Indexing for Faster Queries:
Indexes speed up data retrieval, especially on frequently queried columns.
Mastering these SQL tricks will optimize your queries, simplify logic, and enable complex analyses.
Here you can find SQL Interview Resources👇
https://t.me/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: https://t.me/sqlspecialist
Hope it helps :) | 1 255 |
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| 9 | The Learning Trap: What Most Beginners Fall Into
When starting out, it's common to feel like you need to master every possible SQL concept. You binge YouTube videos, tutorials, and courses, yet still feel lost in interviews or when given a real dataset.
Common traps:
- Complex subqueries
- Advanced CTEs
- Recursive queries
- 100+ tutorials watched
- 0 practical experience
Reality Check: What You'll Actually Use 75% of the Time
Most data analytics roles (especially entry-level) require clarity, speed, and confidence with core SQL operations. Here’s what covers most daily work:
1. SELECT, FROM, WHERE — The Foundation
SELECT name, age
FROM employees
WHERE department = 'Finance';
This is how almost every query begins. Whether exploring a dataset or building a dashboard, these are always in use.
2. JOINs — Combining Data From Multiple Tables
SELECT e.name, d.department_name
FROM employees e
JOIN departments d ON e.department_id = d.id;
You’ll often join tables like employee data with department, customer orders with payments, etc.
3. GROUP BY — Summarizing Data
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;
Used to get summaries by categories like sales per region or users by plan.
4. ORDER BY — Sorting Results
SELECT name, salary
FROM employees
ORDER BY salary DESC;
Helps sort output for dashboards or reports.
5. Aggregations — Simple But Powerful
Common functions: COUNT(), SUM(), AVG(), MIN(), MAX()
SELECT AVG(salary)
FROM employees
WHERE department = 'IT';
Gives quick insights like average deal size or total revenue.
6. ROW_NUMBER() — Adding Row Logic
SELECT *
FROM (
SELECT *, ROW_NUMBER() OVER(PARTITION BY customer_id ORDER BY order_date DESC) as rn
FROM orders
) sub
WHERE rn = 1;
Used for deduplication, rankings, or selecting the latest record per group.
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| 12 | WITH sales_summary AS (
SELECT customer_id,
SUM(amount) AS total_sales
FROM sales
GROUP BY customer_id
)
SELECT *
FROM sales_summary
WHERE total_sales > 10000;
This makes your SQL easier to read and debug.
📌 16. Don't Memorize Interview Queries
Instead of memorizing:
"Query to find the second-highest salary"
Understand the underlying concept:
Ranking → Ordering → Selecting the required rank.
This allows you to solve variations of the same problem.
📌 17. Practice Real Business Scenarios
-----
2.12 ₽ · /balance_help | 1 321 |
| 13 | 🗄️ SQL Important Tips for Beginners — Part 2
If you're learning SQL for Data Analytics, don't just memorize syntax. Focus on understanding how to write correct queries and how SQL processes your data.
📌 1. Always Understand the Question First
Before writing SQL, identify:
• What information is required?
• Which table contains the data?
• Which columns are needed?
• Do you need filtering?
• Do you need grouping?
• Do you need a JOIN?
Understanding the problem first makes writing the query much easier.
📌 2. Use WHERE to Filter Rows
WHERE is used to filter individual records.
SELECT *
FROM employees
WHERE department = 'IT';
Think:
WHERE → Which rows do I need?
📌 3. Remember WHERE vs HAVING
This is one of the most common SQL interview questions.
WHERE → Filters rows before grouping
HAVING → Filters groups after aggregation
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 10;
📌 4. Be Very Careful with JOINs
JOINs are extremely important for Data Analysts.
Before joining tables, understand:
• Primary key
• Foreign key
• One-to-one relationship
• One-to-many relationship
• Many-to-many relationship
A wrong JOIN can produce incorrect results and duplicate records.
📌 5. Understand INNER JOIN vs LEFT JOIN
Remember the basic idea:
INNER JOIN → Returns matching records from both tables.
LEFT JOIN → Returns all records from the left table and matching records from the right table.
This simple concept will help you solve many interview questions.
📌 6. Always Check for Duplicate Rows After a JOIN
If you expected 1,000 rows but your JOIN produces 10,000 rows, don't immediately use DISTINCT.
First investigate whether the JOIN relationship is causing multiple matches.
📌 7. Master GROUP BY
GROUP BY is essential for data analysis.
SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department;
Think:
GROUP BY → How do I want to summarize my data?
📌 8. Learn Aggregate Functions Properly
Master these functions:
• COUNT()
• SUM()
• AVG()
• MIN()
• MAX()
Practice them with GROUP BY and HAVING.
📌 9. Don't Forget NULL
NULL means missing or unknown value.
Incorrect:
WHERE salary = NULL
Correct:
WHERE salary IS NULL
Also learn:
• COALESCE()
• NULLIF()
📌 10. Learn CASE WHEN
CASE WHEN is extremely useful for creating business categories.
CASE
WHEN salary >= 100000 THEN 'High'
WHEN salary >= 50000 THEN 'Medium'
ELSE 'Low'
END
You'll use it frequently in real-world analytics.
📌 11. Don't Overuse DISTINCT
DISTINCT removes duplicate results.
But if you're using DISTINCT because your JOIN unexpectedly created duplicates, investigate the JOIN instead.
📌 12. Learn Date Functions
Data Analyst interviews frequently involve dates.
Practice questions involving:
• Year
• Month
• Quarter
• Date difference
• Month-over-month growth
• Year-over-year growth
• Rolling periods
Date-based SQL problems are extremely common in analytics.
📌 13. Start Learning Window Functions
Once you're comfortable with basic SQL, learn:
• ROW_NUMBER()
• RANK()
• DENSE_RANK()
• LAG()
• LEAD()
• SUM() OVER()
• AVG() OVER()
These are extremely important for Data Analyst interviews.
📌 14. Understand RANK vs DENSE_RANK
For example, if salaries are:
100000
100000
90000
80000
RANK() gives:
1
1
3
4
DENSE_RANK() gives:
1
1
2
3
This difference is frequently tested in interviews.
📌 15. Use CTEs for Complex Queries
Instead of writing one huge query, break the logic into smaller steps using a CTE. | 1 071 |
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| 15 | 12. Master Window Functions
Once your basics are strong, learn:
• ROW_NUMBER()
• RANK()
• DENSE_RANK()
• LAG()
• LEAD()
• SUM() OVER()
• AVG() OVER()
These are especially important for Data Analyst interviews.
13. Don't just memorize queries
Instead of memorizing: "This is the query to find the second-highest salary."
Understand the problem: "I need to rank salaries and identify the second position."
Then decide whether DENSE_RANK(), ROW_NUMBER(), a subquery, or another approach is appropriate.
14. Practice with business problems
Don't practice only: Find employees, Find salaries, Find departments
Practice realistic problems:
• Find customers who haven't purchased in 90 days
• Find the top 3 products in each category
• Calculate month-over-month sales growth
• Find duplicate transactions
• Identify customers whose spending increased
• Calculate employee retention
• Find the second-highest salary in each department
15. Learn to read execution plans later
Once you're comfortable with SQL, start learning:
• Indexes
• Query execution plans
• Table scans
• Index scans
• Query optimization
You don't need this on day one, but it's important as you progress.
🔥 Most important tip: Don't just watch SQL tutorials. Write SQL every day. Even 5–10 problems daily will build your confidence much faster than passive learning.
Double Tap ❤️ For More
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2.17 ₽ · /balance_help | 1 431 |
| 16 | 🗄️ SQL Important Tips for Beginners
If you're starting SQL for Data Analytics, don't try to memorize hundreds of queries. Focus on understanding how SQL thinks and practice consistently.
1. Master the basic SQL order
Learn these clauses first:
• SELECT
• FROM
• WHERE
• GROUP BY
• HAVING
• ORDER BY
• LIMIT
Understand what each one does before moving to advanced SQL.
2. Understand the logical execution order
SQL doesn't logically execute a query in the same order you write it.
A simplified order is:
• FROM
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY
• LIMIT
This helps explain many SQL interview questions.
3. Get comfortable with filtering
Master:
• WHERE
• AND / OR / NOT
• IN
• BETWEEN
• LIKE
• IS NULL / IS NOT NULL
Note: use IS NULL, not = NULL.
4. Learn aggregate functions properly
You should be comfortable with:
• COUNT()
• SUM()
• AVG()
• MIN()
• MAX()
Example:
SELECT department, AVG(salary)
FROM employees
GROUP BY department;
5. Understand GROUP BY vs HAVING
• WHERE → filters rows before grouping
• HAVING → filters groups after aggregation
Example:
SELECT department, COUNT(*) AS employees
FROM employees
GROUP BY department
HAVING COUNT(*) > 10;
6. Master JOINs
For Data Analyst interviews, JOINs are extremely important. Learn:
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL OUTER JOIN
• CROSS JOIN
• SELF JOIN
Most importantly, understand why rows are included or excluded in each JOIN.
7. Always understand your keys
Know the difference between:
• Primary Key
• Foreign Key
• Composite Key
• Unique Key
Understanding relationships between tables will make JOINs much easier.
8. Don't ignore NULL
NULL does not mean:
• 0
• Empty string
• False
Learn how NULL behaves with: IS NULL, IS NOT NULL, COALESCE(), NULLIF()
9. Learn CASE WHEN early
CASE is one of the most useful SQL features for analytics.
SELECT employee,
salary,
CASE
WHEN salary >= 100000 THEN 'High'
WHEN salary >= 50000 THEN 'Medium'
ELSE 'Low'
END AS salary_category
FROM employees;
10. Practice subqueries
Understand queries inside queries:
SELECT *
FROM employees
WHERE salary > (
SELECT AVG(salary)
FROM employees
);
Then move toward correlated subqueries.
11. Learn CTEs
CTEs make complex SQL easier to read and maintain.
WITH sales_summary AS (
SELECT customer_id, SUM(amount) AS total_sales
FROM sales
GROUP BY customer_id
)
SELECT *
FROM sales_summary
WHERE total_sales > 10000; | 1 165 |
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| 18 | If you are interested to learn SQL for data analytics purpose and clear the interviews, just cover the following topics
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources👇
https://t.me/DataSimplifier
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