Coding_knowledge

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Every Programmer Must watch These videos 👇 1. https://youtu.be/HbNfCM4ilBQ?si=KqmrHBV9Ea0OJ3I9 2.https://youtu.be/QHdJscEpxaU?si=IGqKLHvUlnoTa5mB 3.https://youtu.be/vA5TTz6BXhY?si=fWtC6x3DBxquZjkG 4.https://youtu.be/m8Icp_Cid5o?si=L2WephZNUb4wET0n 5.https://youtu.be/8mAITcNt710?si=E1AVHKPJWYAfyW64 6. https://youtu.be/T0u5nwSA0w0?si=hBvWLagilYlS8Uh1 7.https://youtu.be/Peq4GCPNC5c?si=I38jttEPTqRcsk5y

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Free courses by Harvard, Stanford & Google 👇 1. Introduction to Computer Science: https://pll.harvard.edu/course/cs50-introduction-computer-science 2. Machine Learning by Stanford : https://online.stanford.edu/courses/cs229-machine-learning 3. AI Essentials by Google : https://grow.google/ai-essentials/

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Complete Data Analyst Interview Guide (0–2 Years of Experience) 🔹 Round 1: SQL + Scenario-Based Questions Q1. Get top 3 products by revenue within each category SELECT * FROM ( SELECT p.product_id, p.category, SUM(o.revenue) AS total_revenue, RANK() OVER(PARTITION BY p.category ORDER BY SUM(o.revenue) DESC) AS rnk FROM products p JOIN orders o ON p.product_id = o.product_id GROUP BY p.product_id, p.category ) ranked WHERE rnk <= 3; Q2. Find users who purchased in January but not in February SELECT DISTINCT user_id FROM orders WHERE MONTH(order_date) = 1 AND user_id NOT IN ( SELECT user_id FROM orders WHERE MONTH(order_date) = 2 ); Q3. Avg. ride time by city + peak hours SELECT city, AVG(DATEDIFF(MINUTE, start_time, end_time)) AS avg_ride_mins FROM trips GROUP BY city; -- For peak hour detection (example logic) SELECT DATEPART(HOUR, start_time) AS ride_hour, COUNT(*) AS ride_count FROM trips GROUP BY DATEPART(HOUR, start_time) ORDER BY ride_count DESC; ⸻ 🔹 Round 2: Python + Data Cleaning Q1. Clean messy CSV with pandas import pandas as pd df = pd.read_csv('data.csv') df.columns = df.columns.str.strip().str.lower() df.drop_duplicates(inplace=True) df['date'] = pd.to_datetime(df['date'], errors='coerce') df.fillna(method='ffill', inplace=True) Q2. Extract domain names from email IDs emails = ['abc@gmail.com', 'xyz@outlook.com'] domains = [email.split('@')[1] for email in emails] Q3. Difference: .loc[] vs .iloc[] • .loc[] → label-based selection • .iloc[] → index-based selection Q4. Handle outliers using IQR Q1 = df['column'].quantile(0.25) Q3 = df['column'].quantile(0.75) IQR = Q3 - Q1 filtered_df = df[(df['column'] >= Q1 - 1.5*IQR) & (df['column'] <= Q3 + 1.5*IQR)] ⸻ 🔹 Round 3: Power BI / Dashboarding Tasks you should know: • Create a dashboard with weekly trends, margins, churn % • Use bookmarks/slicers for KPI toggles • Apply filters to show top 5 items dynamically • Exclude visuals from slicer using “Edit Interactions” → turn off filter icon on card visual 🔗 Try replicating dashboards from Power BI Gallery ⸻ 🔹 Round 4: Business Case + Logic-Based Thinking Q1. Sales dropped last quarter — what to check? • Compare YoY/QoQ data • Identify categories/geos with the biggest drop • Analyze order volume vs. avg. order value • Check marketing spend, discounts, stockouts Q2. App downloads ⬆️, activity ⬇️ — what’s wrong? • Check Day 1/7/30 retention • Is onboarding working? • UI bugs or crashes? • Compare install → sign-up → usage funnel Q3. Returns increasing — how to investigate? • Analyze return % by brand, category, SKU • Check return reasons (defects, sizing, etc.) • Compare returners’ order history • Seasonal impact? ⸻ 🔰 Free Practice Tools: • 🔹 SQL on LeetCode • 🔹 Python on Hackerrank • 🔹 Power BI Gallery

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