Data Analyst Interview Resources

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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 React ❤️ For More

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Let's now understand the above Data Analyst Roadmap in detail: 🧠↗️ 1️⃣ Learn Excel ⭐️ The foundation of data analysis. Learn formulas, pivot tables, charts, VLOOKUP/XLOOKUP, and conditional formatting. It helps in quick data cleaning and presenting insights. Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i 2️⃣ Learn SQL 💻 Essential for working with databases. Focus on SELECT, JOIN, GROUP BY, WHERE, and subqueries to extract and manipulate data from relational databases. SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v 3️⃣ Learn Python 📱 A powerful tool for data manipulation and automation. Master libraries like pandas, numpy, matplotlib, and seaborn for data cleaning and visualization. Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L 4️⃣ Learn Power BI / Tableau 📈 These tools help create interactive dashboards and visual reports. Learn how to import data, create filters, use DAX (Power BI), and design clear visualizations. Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c 5️⃣ Learn Statistics & Probability 🛍 Know about descriptive stats (mean, median, mode), inferential stats, distributions, hypothesis testing, and correlation. Vital for making sense of data trends. Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O 6️⃣ Learn Data Transformation 📈 Learn how to clean, shape, and prepare data for analysis. Use Python (pandas) or Power Query in Power BI, and understand ETL (Extract, Transform, Load) processes. Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27 7️⃣ Learn Machine Learning 🧠 Understand basic concepts like regression, classification, clustering, and decision trees. You don’t need to be an ML expert, just grasp how models work and when to use them. Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O 8️⃣ Build Projects & Portfolio 🏹 Apply what you’ve learned to real datasets—like sales analysis, churn prediction, or dashboard creation. Showcase your work on GitHub or a personal website. Data Analytics Projects: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29 9️⃣ Apply for Jobs 💼 With your skills and portfolio in place, start applying for data analyst roles. Tailor your resume using keywords from job descriptions and prepare to answer SQL and Excel tasks in interviews. Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 Share with credits: https://t.me/sqlspecialist Double Tap ♥️ for more

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