Irfan Arif

How to improve this line chart in Power BI? What I changed and why it works better Shift focus on current year In the previous version the current and last year lines had equal weights. • Current year line highlighted with color → emphasized primary info • Last year line toned down → deemphasized secondary info • Bold latest current year label to grab more attention Improved comparison I added a dashed line to show the continued trend for last year. It gives an idea what might come. Reduced clutter The previous version was a bit busy and I wanted make it cleaner. • Labels only on last data points • Muted border color and drop shadow • Header deemphasized. The focus should be on the data Modern look To improve the design I rounded the corners and curved the chart lines. It gives a softer, more polished look. Let me know what you think. 👇 #powerbi #dataviz #dashboarddesign #reportdesign

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𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀. The real challenge begins when you have to make them ready for production. I recently attended a session on the Practical Power BI Project Workflow, and it gave me a crystal-clear view of what actually happens when a project moves from client discussion all the way to production. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗽𝗹𝗮𝘆𝘀 𝗼𝘂𝘁: Client discussion → drafting report layout → data gathering → development → internal testing → UAT → feedback → production. And out of all these stages, one stood out the most. User Acceptance Testing (UAT). Because this is where business users decide if your report is really accurate, secure, and usable. It’s not just about charts looking nice. At this stage, everything is checked ✔️ Data accuracy ✔️ Slicers & filters working ✔️ Drill-through navigation ✔️ RLS (Row Level Security) 𝗔𝗻𝗱 𝗼𝗳 𝗰𝗼𝘂𝗿𝘀𝗲, 𝗨𝗔𝗧 𝗼𝗳𝘁𝗲𝗻 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝘂𝗴𝗵𝗲𝘀𝘁 𝗽𝗶𝘁𝗳𝗮𝗹𝗹𝘀: ➪ Wrong data transformations → mismatched results ➪ Large datasets → performance issues ➪ DAX formulas → incorrect KPIs ➪ RLS → not restricting data as expected ➪ Dashboards → too cluttered for end users Even after UAT, the job isn’t done. 📌 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘀𝗶𝗴𝗻-𝗼𝗳𝗳 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹. It means: ✔️ All critical issues are resolved ✔️ Performance is validated ✔️ Stakeholders are aligned ✔️ Documentation is ready for deployment That’s when the project is truly production-ready. And this really hit me , discipline at every stage is what makes a Power BI solution valuable. Not just a set of visuals, but something the business can actually trust and use. 👉 Huge thanks to Trilochan Tripathy for delivering this session with so much clarity. The workflows and practical examples made everything click. Also grateful to NAVEEN S & Codebasics for arranging such a valuable session. 🔹 𝗣𝗦: Follow me as I share what I learn to help you on your data journey. 𝗞𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗵𝘂𝘀𝘁𝗹𝗶𝗻𝗴! 💪 👍 React 💬 Comment ♻️ Share

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🎯 𝗕𝗮𝗰𝗸𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗙𝗼𝗹𝗱 𝗦𝘁𝗿𝗶𝗱𝗲 🔄📆 When evaluating forecasting models, the way you design your backtesting strategy can completely change the insights you get. skforecast now gives you precise control over 𝗵𝗼𝘄 𝗳𝗮𝗿 𝘁𝗵𝗲 𝘁𝗲𝘀𝘁 𝘀𝗲𝘁 𝗺𝗼𝘃𝗲𝘀 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗳𝗼𝗹𝗱𝘀. For example, you might want to forecast the next 𝟯𝟬 𝗱𝗮𝘆𝘀 but generate a new forecast every 𝟳 𝗱𝗮𝘆𝘀. In this setup, each test window is 30 days long, but the stride between folds is one week. This means forecasts 𝗼𝘃𝗲𝗿𝗹𝗮𝗽, the same observations are predicted multiple times, providing a 𝗿𝗶𝗰𝗵𝗲𝗿 𝘃𝗶𝗲𝘄 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 across different forecast origins. 💡 With 𝘧𝘰𝘭𝘥_𝘴𝘵𝘳𝘪𝘥𝘦, backtesting is no longer tied to the number of steps predicted. You can now design evaluations that match your 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗰𝗮𝗱𝗲𝗻𝗰𝗲. 🔗 Code example: https://lnkd.in/dn58Uyj4 🔗 Documentation: https://skforecast.org/ 👏 Credits: Joaquin Amat Rodrigo & Javier Escobar Ortiz Happy forecasting! 📈 #skforecast #timeseries #machinelearning #forecasting #python #backtesting #opensource scikit-learn

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🕒 Power BI Time Intelligence DAX Functions – Must-Know for Interviews 🕒 Time Intelligence functions in Power BI help analyze time-based data efficiently. These functions are widely used in real-time projects for reporting and trend analysis. Below are key DAX functions with simple explanations and practical applications: ➡️ TOTALYTD – Calculates the cumulative value from the beginning of the year to the selected date. 🔹 Example: Sales up to today in the current year. 🔹 DAX: TOTALYTD([Sales], 'Date'[Date]) ➡️ TOTALMTD – Calculates the cumulative value from the beginning of the month to the selected date. 🔹 Example: Revenue from the start of the current month. 🔹 DAX: TOTALMTD([Revenue], 'Date'[Date]) ➡️ TOTALQTD – Calculates the cumulative value from the beginning of the quarter to the selected date. 🔹 Example: Expenses from the start of the current quarter. 🔹 DAX: TOTALQTD([Expense], 'Date'[Date]) ➡️ PREVIOUSYEAR – Returns the same date range from the previous year. 🔹 Example: Comparing last year’s sales with the current year. 🔹 DAX: CALCULATE([Sales], PREVIOUSYEAR('Date'[Date])) ➡️ PREVIOUSMONTH – Returns the same date range from the previous month. 🔹 Example: Customer growth trend from last month. 🔹 DAX: CALCULATE([Customers], PREVIOUSMONTH('Date'[Date])) ➡️ PREVIOUSQUARTER – Returns the same date range from the previous quarter. 🔹 Example: Comparing quarterly profit trends. 🔹 DAX: CALCULATE([Profit], PREVIOUSQUARTER('Date'[Date])) ➡️ SAMEPERIODLASTYEAR – Retrieves the same date range from last year. 🔹 Example: Comparing revenue of this month vs. the same month last year. 🔹 DAX: CALCULATE([Revenue], SAMEPERIODLASTYEAR('Date'[Date])) ➡️ PARALLELPERIOD – Shifts a time period forward or backward by a specified number of months, quarters, or years. 🔹 Example: Sales from two months ago. 🔹 DAX: CALCULATE([Sales], PARALLELPERIOD('Date'[Date], -2, MONTH)) ➡️ DATEADD – Moves a date range forward or backward by a specific number of days, months, quarters, or years. 🔹 Example: Sales for the next 3 months. 🔹 DAX: CALCULATE([Sales], DATEADD('Date'[Date], 3, MONTH)) ➡️ STARTOFYEAR / ENDOFYEAR – Returns the first or last date of the current year. 🔹 Example: Find the start and end of the fiscal year. 🔹 DAX: STARTOFYEAR('Date'[Date]), ENDOFYEAR('Date'[Date]) ➡️ STARTOFMONTH / ENDOFMONTH – Returns the first or last date of the current month. 🔹 Example: Find the closing stock for the month. 🔹 DAX: ENDOFMONTH('Date'[Date]) ➡️ STARTOFQUARTER / ENDOFQUARTER – Returns the first or last date of the current quarter. 🔹 Example: Find the quarterly closing balance. 🔹 DAX: ENDOFQUARTER('Date'[Date]) Hope everyone understood 🙏! MADHU THANGELLA connect with me more updates ! Job & Referrals/guidance: :https://lnkd.in/gcw-ziZm Note: Reposting for new-audience. #powerbi #dataanalyst #freshers #interview #datacommence

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Hi Datafam Excel is beyond the spreadsheet as it is a wonderful tool for data query, Modeling and attractive dashboards. This is a report of the Sales of Stores which is indicating the performances of sales overtime. The key performances are the Sales, Costs, Profit, Quantities sold, order, Customers and so on. The report shows high level of increase in sales overtime. #Excel #Dataviz

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Essential Skills Excel for Data Analysts 🚀 1️⃣ Data Cleaning & Transformation Remove Duplicates – Ensure unique records. Find & Replace – Quick data modifications. Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER. Data Validation – Restrict input values. 2️⃣ Data Analysis & Manipulation Sorting & Filtering – Organize and extract key insights. Conditional Formatting – Highlight trends, outliers. Pivot Tables – Summarize large datasets efficiently. Power Query – Automate data transformation. 3️⃣ Essential Formulas & Functions Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH. Logical Functions – IF, AND, OR, IFERROR, IFS. Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA. Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE. 4️⃣ Data Visualization Charts & Graphs – Bar, Line, Pie, Scatter, Histogram. Sparklines – Miniature charts inside cells. Conditional Formatting – Color scales, data bars. Dashboard Creation – Interactive and dynamic reports. 5️⃣ Advanced Excel Techniques Array Formulas – Dynamic calculations with multiple values. Power Pivot & DAX – Advanced data modeling. What-If Analysis – Goal Seek, Scenario Manager. Macros & VBA – Automate repetitive tasks. 6️⃣ Data Import & Export CSV & TXT Files – Import and clean raw data. Power Query – Connect to databases, web sources. Exporting Reports – PDF, CSV, Excel formats. Hope it helps :) #dataanalyst

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✅ From Formulas to Frameworks — Reflecting on the Journey Before diving deep into Power BI dashboards, DAX functions, and data modeling, Excel was where it all began for me. 🧩 I recently built this Retail Sales Dashboard in Excel, applying: 🔹 Lookup formulas – VLOOKUP to map customer age groups and product categories from raw tables 🔹 Date manipulation – TEXT, MONTH, YEAR, and WEEKDAY to extract time-based insights for trend analysis 🔹 Data cleaning, error handling & interactivity with slicers for dynamic filtering across segments While Power BI is my preferred platform today for interactive, scalable visualizations, this project was a great reminder: 👉 Excel still delivers — especially when you need speed, control, and flexibility. It might not scale like BI tools, but it’s unmatched for building something fast from scratch. As someone currently learning and growing in data analytics, I’ve realized that mastering both tools gives me a stronger foundation — helping me think more critically about when and how to apply them. 🧠📈 Because at the end of the day, it’s not just about the tool — it’s about how you extract insights, tell stories, and drive decisions from data. 💡 #ExcelDashboard #PowerBI #DataAnalytics #LearningJourney #DashboardDesign #ExcelVsPowerBI #AnalyticsTools #InsightDriven

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Anyone can make a dashboard. But the best analysts don't just display data. They transform it into actionable intelligence. Effective data analysts think critically about every number before it reaches decision-makers. Here are 16 signs of a data analyst who thinks critically 👇 1. They question the data source before analyzing ↳ "Where did this data come from?" is always their first question 2. They investigate outliers instead of removing them ↳ Treat anomalies as insights, not inconveniences 3. They consider what's missing in the dataset ↳ Pay attention to the silent gaps—they often speak volumes 4. They challenge their own assumptions first ↳ Actively seek contradictory evidence to test hypotheses 5. They distinguish correlation from causation ↳ Never jump to "X causes Y" without proper evidence 6. They present multiple interpretations of the same data ↳ Share alternative explanations before conclusions 7. They acknowledge the limitations of their analysis ↳ Transparently communicate what the data cannot tell you 8. They translate technical findings into business language ↳ Convert complex patterns into actionable recommendations 9. They ask "so what?" after every insight ↳ Relentlessly connect findings to concrete business value 10. They design visualizations that reveal, not decorate ↳ Choose clarity over complexity in every chart 11. They test against historical context ↳ Compare findings against past patterns before declaring trends 12. They consider second-order effects ↳ Look beyond immediate impacts to downstream consequences 13. They anticipate stakeholder questions ↳ Prepare answers for the questions not yet asked 14. They recognize when more data won't help ↳ Know when to stop collecting and start deciding 15. They highlight risks alongside opportunities ↳ Present the complete picture, not just the favorable view 16. They revisit past analyses to validate predictions ↳ Close the loop by testing if insights actually delivered value Data analysis isn't about having all the answers. It's about asking the right questions. Which of these skills are you developing as a data analyst? ♻️ Repost to help your network build critical thinking skills 🔔 Follow for daily insights on data-driven decision making

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What people think Power BI is vs what it actually is 😅 Expectation: • Drag → Drop → Dashboard ready in 10 minutes • Fancy visuals = instant job offer • No coding, no effort, just clicks Reality: • Cleaning 15 messy Excel files • Writing 50+ DAX measures • Debugging refresh failures at midnight • Answering “Why does this number not match my Excel?” every week Lesson: Power BI is not just about building dashboards. It’s about solving business problems — and that’s where the real value (and real money) lies ⸻ If you’re in data — what’s the funniest “expectation vs reality” moment you’ve had? ⸻ Hi, I am Akshay Kamble. I guide people aspiring for #DataAnalytics and #PowerBI job roles. Let’s connect : ) #PowerBI #DataAnalytics #Freelancing #ClientWork #ProVisualizers #biacademyin

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