Sachin Kumar
@Sachintukumar
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Data Analyst || SQL || PowerBI || Excel || Python || Statistics || Ex-UPSC Aspirant || 📩 for Collaboration
Ghaziabad
Joined February 2018
Guys please help me I’m Sachin Kumar — Seeking Data Analyst / Product Analyst/ Business Analyst Roles! ✅ 2+ years in SQL, Python, Power BI & Excel ✅ BI reporting, product analytics & automation experience ✅ Skilled in EDA, A/B testing, KPI tracking & data storytelling
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Ever wondered how to alternate Male-Female rows in SQL? This clever trick is perfect for @Deloitte , Accenture, or any data-heavy interview! Day 57 of SQL Revision A Complete thread
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Day 55 of SQL Revision Series From timestamps to productivity insights: How to calculate daily work hours for multiple employees using #SQL A Complete 🧵
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Step 2: Assign alternating positions Male → rn * 2 - 1 → odd positions (1, 3, 5 …) Female → rn * 2 → even positions (2, 4, 6 …) This ensures that when we sort by sort_order, rows alternate: Male → Female → Male → Female Step 3: Final output #SQLTips #DataAnalytics
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◾ Explanation: Step 1: Split employees by gender & assign row numbers ROW_NUMBER() OVER (ORDER BY id) assigns a sequential number to each employee within their gender
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), pp AS ( SELECT id, EmpName, Gender, rn * 2 - 1 AS sort_order FROM Male UNION ALL SELECT id, EmpName, Gender, rn * 2 AS sort_order FROM Female ) SELECT id, EmpName, Gender FROM pp ORDER BY sort_order;
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◾ Using Row_Number Approach Query: WITH Male AS ( SELECT id, EmpName, Gender, ROW_NUMBER() OVER (ORDER BY id) AS rn FROM Employee WHERE Gender = 'MALE' ), Female AS ( SELECT id, EmpName, Gender, ROW_NUMBER() OVER (ORDER BY id) AS rn FROM Employee WHERE Gender = 'FEMALE'
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Ever wondered how to alternate Male-Female rows in SQL? This clever trick is perfect for @Deloitte , Accenture, or any data-heavy interview! Day 57 of SQL Revision A Complete thread
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Even the greatest go through tough phases But champions aren’t defined by their failures — they’re defined by how they bounce back. 💪 A Lesson from Virat Kohli Sir! 1st Match vs AUS – 0 2nd Match vs AUS – 0 3rd Match vs AUS (today) – Finally, a solid 50+* #Kohli #ViratKohli
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📗Day 53 of SQL Series Sandwich Pattern Problem in SQL Ever noticed a weird pattern in your data — something disappears, changes, and comes back again? That’s what I call the Sandwich Pattern — same value at start and end, but something else in between. A Complete 🧵
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4️⃣ Aggregating Daily Hours Using SUM() and GROUP BY, we get total working hours for each employee per day. This method works for multiple login-logout pairs per day, automatically summing all sessions to calculate the total working hours accurately.
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3️⃣ Calculating Duration in Hours EXTRACT(EPOCH FROM (logout_time - login_time)) / 3600 converts the time difference from seconds to hours. #SQLTips #DataAnalytics #WorkHoursTracking #EmployeeInsights #ProductivityAnalytics
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Partitioning by employee_id and day ensures we calculate durations per employee per day. 2️⃣ Filtering for Logins We only need login events to calculate the duration until the next event (usually logout).
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As a analyst, one common task is to calculating daily working hours for employees using event logs. How it works: 1️⃣ Pairing Login and Logout Events We use a CTE (WITH paired) with LEAD() function to get the next event time for each login
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Day 55 of SQL Revision Series From timestamps to productivity insights: How to calculate daily work hours for multiple employees using #SQL A Complete 🧵
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📗Day 53 of SQL Series Sandwich Pattern Problem in SQL Ever noticed a weird pattern in your data — something disappears, changes, and comes back again? That’s what I call the Sandwich Pattern — same value at start and end, but something else in between. A Complete 🧵
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📗Day 52 of SQL Series When an interviewer asks “How would you clean messy data in #SQL ?” many candidates jump straight to functions: TRIM(), COALESCE(), CAST()… That’s fine — but process matters more than memorized functions. Use this simple, repeatable framework (and say it
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✅ Why It Matters: This pattern logic helps detect: - users who drop off & return - systems that fail → recover → fail again - inventory cycles & repeated events SQL isn’t just about aggregates — it’s a way to find stories in data.
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☑️ SQL Query: SELECT DISTINCT a.NUMB AS sandwich_value FROM NUM a JOIN NUM b ON https://t.co/U8NYYlew0G = https://t.co/Mqwpiag1hp + 1 JOIN NUM c ON https://t.co/ljGWPns5bg = https://t.co/Mqwpiag1hp + 2 WHERE a.NUMB = c.NUMB AND a.NUMB <> b.NUMB; ✅ Output → 4 and 9
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✅Problem Statement: We need to find values forming a pattern x, y, x — same outer values, different middle. ☑️ The Logic Think in triples: (i, i+1, i+2) If a.NUMB = c.NUMB and a.NUMB <> b.NUMB, you’ve found your “sandwich”
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