Tom Hockaday

Having been working SQL problems on the Interview Master website for a number of days now, I’ve found that my process reliably breaks down into two very consistent steps: 1. Read the problem and think, “OK, this seems straightforward enough.” 2. Begin working the problem and think, “Oh, wait.” Oftentimes, though, the upshot is discovering a clever new approach — like when I recently needed to calculate the percentages of users who met a certain criterion. In the sample dataset (let’s say it was about TV ownership), the task was to determine what percentage of households had five or more TVs. Which, as I said, seemed pretty simple right up until I actually started trying to figure it out. Now, in my defense, I will note that I did eventually cobble together a query that produced a correct answer. Now, was that query a model of efficiency that 𝘥𝘪𝘥𝘯’𝘵 look like a plate of spaghetti? Well, I’m not on trial here. But here’s something neat that was pointed out to me by my new pal Gemini (ChatGPT & I are currently on a break): “Whenever you want a percentage of rows that meet a condition, remember that an average of 1s and 0s is mathematically identical to a percentage.” Which means — if we assign 1 to every household that has five or more TVs and 0 to every household that doesn’t — and, let's say, one out of every four households has that many TVs (i.e., 1, 0, 0, 0) — that average is 25%. And that series of 0s and 1s is just the sort of thing we can assemble with a CASE statement: 𝐖𝐈𝐓𝐇 tv_count 𝐀𝐒 (𝐒𝐄𝐋𝐄𝐂𝐓 household, 𝐂𝐎𝐔𝐍𝐓(tv_id) AS household_tvs 𝐅𝐑𝐎𝐌 tv_table 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 household) 𝐒𝐄𝐋𝐄𝐂𝐓 𝐀𝐕𝐆(𝐂𝐀𝐒𝐄 𝐖𝐇𝐄𝐍 household_tvs > =5 𝐓𝐇𝐄𝐍 1.0 𝐄𝐋𝐒𝐄 0.0 𝐄𝐍𝐃) * 100 𝐀𝐒 over_five_percentage 𝐅𝐑𝐎𝐌 tv_count Turns out, maybe it wasn't so complicated in the first place — as they say, “The hard part is knowing how”! #dataanalytics #dataanalyticsjourney #sql #interviewmaster

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Another frequently asked SQL interview question is: 👉 "Find the running sales (cumulative sales) for each Sub-Category over time." Here’s how I solved it using Window Functions 👇 📌 Key Steps: Grouped data by Sub-Category and Order Date Calculated total sales using SUM(Sales) Applied SUM() OVER (PARTITION BY Sub-Category ORDER BY Order Date) to calculate running total Ordered results by Sub-Category and date 🚀 SQL concepts used: ✔ Aggregate Functions (SUM) ✔ Window Functions (OVER PARTITION BY) ✔ Running Total / Cumulative Sum ✔ Group By & Order By This query is a great example of how window functions simplify cumulative calculations that would otherwise be complex. Perfect practice for both interviews and real-world reporting! #SQL #DataAnalytics #InterviewPreparation #SQLQueries #LearningSQL #DataAnalyst

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If you’re learning SQL for interviews or real-world analytics, start with this: SQL isn’t about memorizing commands. It’s about understanding how data moves, transforms, and answers questions. Here’s a clean breakdown of the SQL commands you must genuinely master and why they matter. 1. Data Querying Commands These form the backbone of almost every SQL question. Why it matters: Almost 80 percent of SQL questions revolve around querying data with clarity. 2. Aggregation & Grouping Commands Used when you need insights instead of raw data. Why it matters: Every interview has questions like “find highest”, “find frequency”, “get monthly totals”. 3. JOIN Commands Joins decide whether you can think relationally. Complex business data always lies across multiple tables, not one. The Real Skill: Turning Data Into Answers Memorizing commands won’t get you hired. Being able to use them to answer real questions will. Most interviewers evaluate how you think when you: - join, filter, and aggregate across messy data - break logic into steps - avoid overcomplicating queries - explain your reasoning with clarity Follow Supriya Darisa If you master the commands above - deeply, not mechanically you’ll be ahead of 90 percent of SQL candidates

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𝗦𝘁𝗶𝗹𝗹 𝗼𝗽𝗲𝗻𝗶𝗻𝗴 𝘁𝗶𝗰𝗸𝗲𝘁𝘀 𝗷𝘂𝘀𝘁 𝘁𝗼 𝗴𝗲𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮? 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 lets teams ask questions in plain language and instantly receive accurate, traceable insights - no SQL required. From sales analytics to operational intelligence, it brings structured and unstructured data together in one conversational interface. 𝗢𝘂𝗿 𝗹𝗮𝘁𝗲𝘀𝘁 𝗴𝘂𝗶𝗱𝗲 𝗰𝗼𝘃𝗲𝗿𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄: ✔ Features and architecture explained ✔ Real-world enterprise use cases ✔ Pricing and cost considerations ✔ Limitations to watch out for ✔ How to build your first AI agent in minutes 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗴𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲: https://shorturl.at/qaStm #Snowflake #AI #DataAnalytics #BusinessIntelligence #DigitalTransformation Snowflake Helical IT Solutions (DBT, Databricks, Snowflake, Glue, Quicksight, Talend, PowerBI, Spark, Gen AI) Snowflake Consulting Snowflake Consulting Group LLC Snowflake Educational Services Snowflake TechnologiesZA Big data ETL in Chennai India- Redshift, Snowflake, Bigquery, AWS glue lambda Python Developer team SNOWFLAKE COMPUTING UK LTD Snowflake Technologies

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If I had to restart my analytics career from scratch, I would: Month 1–2: SQL + data fundamentals Month 3: Solve real business case studies Month 4: Study one product deeply (metrics, funnels, trade-offs) Month 5: Mock interviews weekly No random courses. No tool hopping. Just structured progression. Most people fail because they consume. Very few practice deliberately. If you’re starting out, this roadmap will save you months. Follow Raghav Bakshi for more structured guidance.

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Throughout this problem set at Interview Master, I utilized several important SQL skills, including: ❇️ Joining tables to combine supplier information with delivery data. ❇️Filtering data by date ranges using WHERE clauses. ❇️Aggregating data with SUM() and grouping results using GROUP BY. ❇️Ordering results and limiting output with ORDER BY and LIMIT. ❇️Using window functions like ROW_NUMBER() to rank data within partitions. ❇️Writing subqueries and using NOT IN to filter out specific records. 🔎 Insights Question 1: The most active suppliers in the month of October 2024 Beta Electronics4300 Gamma Materials3700 Alpha Components2700 Delta Manufacturing2500 Eta Technologies2300 Question 2: North America, Asia, and Europe have the highest components of 7300, 5100, and 2,800, respectively. In question 3, the following suppliers did not supply to the Asia region in the month of December 2024: Alpha Components Gamma Materials Delta Manufacturing Epsilon Systems Zeta Solutions Theta Innovations Iota Processing Kappa Supplies and Lambda Corp #sql #windowfunctions

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Hiring a Tableau developer in 2026 is harder than it looks. The same job title covers four different profiles: visualization developers, analytics engineers, Tableau Server admins, and Salesforce-Tableau specialists. Each one requires a different job description, different interview, and a different offer. Mid-level Tableau developers cost $115K to $135K this year. Senior engineers run $140K to $175K. Salesforce-native specialists command $165K to $195K, and that pool is genuinely thin. If your search isn't moving, you might be hiring for the wrong profile. KORE1's full breakdown of all four profiles, salary benchmarks, and screening questions is here: https://lnkd.in/dvEDtXcR #Tableau #DataAnalytics #ITHiring

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Every data professional forgets which statistical test to use. Here's the fix. (Bookmark it. Seriously.) I've been there: ↳ Staring at two datasets wondering which test to run ↳ Googling "t-test vs ANOVA" for the 10th time ↳ Second-guessing myself in an interview Choosing the wrong statistical test can invalidate your findings and lead to flawed conclusions. Here's your quick reference guide: 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐌𝐞𝐚𝐧𝐬: ↳ 2 independent groups → Independent t-Test ↳ Same group, before/after → Paired t-Test ↳ 3+ groups → ANOVA 𝐍𝐨𝐧-𝐍𝐨𝐫𝐦𝐚𝐥 𝐃𝐚𝐭𝐚: ↳ 2 groups → Mann-Whitney U Test ↳ Paired samples → Wilcoxon Signed-Rank Test ↳ 3+ groups → Kruskal-Wallis Test 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬: ↳ Linear relationship → Pearson Correlation ↳ Ranked/non-linear → Spearman Correlation ↳ Two categorical variables → Chi-Square Test 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: ↳ Continuous outcome → Linear Regression ↳ Binary outcome (yes/no) → Logistic Regression 𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞: ↳ Compare spread between groups → Levene's Test / F-Test Here are 5 resources to help you: 1. Khan Academy Statistics: https://lnkd.in/statistics-khan 2. StatQuest YouTube Channel: https://lnkd.in/statquest-yt 3. Seeing Theory (Visual Stats): https://lnkd.in/seeing-theory 4. Statistics by Jim Blog: https://lnkd.in/stats-jim 5. OpenIntro Statistics (Free Textbook): https://lnkd.in/openintro-stats 📚 𝐆𝐞𝐭 𝟏𝟓𝟎+ 𝐫𝐞𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐰𝐢𝐭𝐡 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 & 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬) 𝐢𝐧 𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩 𝐁𝐨𝐨𝐤: https://lnkd.in/dyzXwfVp ♻️ Share this with a data professional who needs this guide! 𝐏.𝐒. I share data storytelling insights and career tips in my free newsletter. Join 25,000+ readers → https://lnkd.in/dUfe4Ac6

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🚀 Data Analyst vs Business Analyst, not the same job, not the same mindset A lot of people think they’re interchangeable. They’re not. 📊 Data Analysts ask: “What is the data telling us?” They live in SQL, dashboards, trends, and numbers. 📈 Business Analysts ask: “What should we do with this insight?” They live in strategy, communication, and decision-making. Both work with data. Both create impact. Just in very different ways. 💭 Love numbers, patterns, and deep analysis? → Data Analyst 💬 Love solving business problems and talking to stakeholders? → Business Analyst Neither is better. The best role is the one that fits how your brain works. 👇 Comment DATA or BUSINESS Let’s see which side wins today 👀 #DataAnalytics #BusinessAnalysis #DataAnalyst #BusinessAnalyst #CareersInTech #TechCareers #Analytics #CareerGrowth #CareerAdvice #LinkedInCommunity

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🚨 Why Every Analyst Interview Includes ETL Questions 🚨 You apply for an analyst role. But instead of “Tell me about your favorite dashboard,” the interviewer asks: 👉 Walk me through an ETL pipeline. 👉 How would you automate a data refresh? 👉 How do you merge CSVs, APIs, and SQL data into one clean source? To you it may sound odd. But here’s the truth: 💡 Analysis doesn’t exist without data movement. Dashboards only live if pipelines feed them. KPIs only make sense if the tables behind them are structured. Even a “simple” SQL query depends on how data entered into the warehouse. So what are interviewers really checking? ✅ Do you know where data comes from? ✅ Do you understand what happens before you query it? ✅ Can you partner with engineers—not just build charts? Here’s how to answer with confidence: 🔹 Ingestion → APIs, CSVs, databases, cloud storage (S3, ADLS) 🔹 Transformation → cleaning, joins, deduplication, enrichment 🔹 Automation → workflows, scheduling, daily refreshes 🔹 Collaboration → bridging analysts & engineers ✨ A concise ETL example often impresses more than the flashiest dashboard. Because companies don’t just want report builders. They want analysts who see the bigger picture: how raw data becomes decisions. 🔥 Pro tip: Practice explaining ETL in plain English—because clarity wins interviews.

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I love how everyone wants to “get into data” but nobody wants to actually… do data things. Everyone’s plan looks like this: • Watch 47 YouTube videos • Save 12 SQL playlists • Bookmark 6 courses • Rewrite resume for the 9th time • Apply to 300 jobs • Refresh inbox • Blame the job market Meanwhile, learning SQL is like: “Write the query.” “No.” “Still no.” “Oh… that worked.” That’s it. That’s the job. Just typing things until the database stops yelling at you. The people getting hired aren’t smarter. They just practiced when it was inconvenient. Lunch breaks. Evenings. Random Tuesdays. Unsexy reps win. -- Analyst Hive Where people who are serious about learning data tend to hang out

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Recruiters don’t hire resumes. They hire proof. You can write Power BI in your skills section. Almost everyone does. What really makes a data analyst profile stand out in 2026 is simple: 👉 What have you actually built with Power BI? If you’re a fresher or early-career analyst, the right projects can speak louder than years of experience. Here are 11 real-world Power BI project ideas that can make your portfolio strong, clear, and job-ready 👇 📊 Business & Sales • Sales Performance Dashboard – Track growth, regions, and key metrics • Global Superstore Analysis – Sales, profit, and customer patterns • E-commerce Analytics – Orders, returns, and customer behavior • Automobile Sales Performance – Model-wise sales and dealer insights 💰 Finance & Operations • Financial Budgeting Report – Budget vs actual, expenses, trends • Supply Chain Logistics – Delivery time, costs, supplier performance • Healthcare Patient Dashboard – Patient flow and resource usage • HR Attrition Analysis – Employee exits, patterns, and risk areas 📣 Marketing & Engagement • Social Media Campaign Insights – Reach, engagement, and results • Marketing Campaign Analysis – Leads, conversions, and channels 🎓 Education • Student Academic Performance – Grades, attendance, subject trends Why do these projects work? ▶️ They show how you think, not just what tool you know ▶️ Recruiters can see your problem-solving and decision support skills ▶️ They reflect real business situations, not dummy dashboards For freshers, these projects help bridge the “no experience” gap. For early-career analysts, they show growth beyond basic reports. If you’re serious about a data analyst role in 2026, your portfolio matters more than your certificate. Follow Mohith Reddy P for more tech insights and updates. Ankit Bansal Shakra Shamim Munna Das #DataAnalytics #PowerBI #DataAnalyst #AnalyticsCareers

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Here's a SQL interview cheat sheet: 1. Clarify the problem first "Just to confirm, we're looking for customers who..." 2. Outline your approach before coding "I'll tackle this by first joining these tables, then..." 3. Think aloud as you code "I'm using a CTE here to make the logic clearer..." 4. Explain your choices "I chose LEFT JOIN over INNER JOIN because..." 5. Address edge cases "We should consider how to handle NULL values..." 6. Discuss scalability "For larger datasets, we might want to optimize by..." 7. Connect to business outcomes "This analysis would help the marketing team..." 8. Suggest improvements "With more time, I'd add error handling for..." The candidates who get hired aren't just those who write working code. They're those who demonstrate they think beyond the code.

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Explore North Carolina's economic vitality through a new dashboard from #LEADNC! Last month, LEAD introduced the County Economic Vitality Index — a composite measure of the economy for all 100 North Carolina counties. With the new County EVI Dashboard, you can explore development over time, compare area profiles, and view trends to understand how local conditions are improving or weakening compared to national norms. Learn more and explore LEAD's County EVI Dashboard: https://lnkd.in/e_wM2P5R #LEADFeed

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👋 Hey everyone! I’ve been getting a lot of new connections lately (thank you!) and a few messages asking how to land a data or business analyst role. I’m currently on contract with Seraph, and I totally get how challenging (and sometimes discouraging) the job hunt can be. Here are a few things that really helped me 👇 💬 1️⃣ Network—especially in person. My current role came through a personal recommendation. One genuine connection can open a door you didn’t expect. 💻 2️⃣ Build projects outside your degree or certificate. Employers care less about how many certs you have and more about what you can do and how you think. A few portfolio projects can make your profile stand out big time. 🎯 3️⃣ Focus on industries you actually care about. “Data analyst” can mean healthcare, finance, marketing, non-profit, and much more. Pick an area that excites you—it helps you stay motivated and grow faster. 💪 4️⃣ Don’t give up! Everyone’s path looks different. Some land jobs right away, others take months. I was definitely in the second group—but persistence pays off.🙌 Keep going—you’ve got this! If you’re job hunting in data or business analysis, drop a comment or DM me. Always happy to share what I’ve learned. (picture below of a fun quote I saw etched in downtown Adelaide saying "Kurraka warble their iconic music) #DataAnalytics #BusinessAnalysis #JobSearchTips #CareerAdvice #Networking #DataCareers #LinkedInCommunity #KeepGoing

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If I had to fix one thing in most Data Analyst resumes in the US… It wouldn’t be SQL. It wouldn’t be Power BI. It would be this: They don’t show impact. I’ve seen resumes where people clearly put in the effort. Courses done. Projects built. Tools learned. But when you read it… It feels empty. Because everything sounds like: • “Used SQL” • “Created dashboard” • “Worked with Excel” Technically correct. But it doesn’t tell me anything. Now compare that with something simple like: • “Analyzed sales data and found which products were driving most of the revenue” • “Built a dashboard to track regional performance and identify trends” Same work. But now I understand the value. And in a market like the US, where recruiters scan quickly… That difference matters a lot more than people think. Because they’re not asking: “Do you know SQL?” They’re thinking: “Can this person help us make better decisions?” If your resume doesn’t answer that in a few seconds… You get skipped. Not rejected. Just… skipped. And that’s why many people feel stuck. If you're applying right now and not getting responses: Don’t just apply more. Try this instead: Pick one project from your resume and rewrite it in terms of: • what problem you looked at • what you found • why it matters You might be surprised how much that changes things. Be honest 👇 What do you think is the biggest issue right now? A) Resume not strong enough B) Not getting interview calls C) Not confident in skills D) Don’t know what to improve Comment A / B / C / D If you want help with this, I do work 1-to-1 with people on: • resume improvement • real-world projects • interview prep • clear roadmap If you’re interested, just comment MENTOR or DM me. #DataAnalytics #DataCareers #SQL #PowerBI #CareerGrowth

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