Muxtor A'zam | Data Analyst

Behuda ishlar bilan band bo'lishimiz Alloh bizdan yuz o'girganining alomatidir Hasanxon Yahyo Abdulmajid

✅ Power BI Interview Questions with Answers 1. What is DAX?  DAX (Data Analysis Expressions) is a formula language in Power BI used to create calculated columns, measures, and tables (e.g., SUM(), CALCULATE(), FILTER()) for business logic and KPIs. 2. What is the difference between Power Query and Power Pivot?  • Power Query: used for data loading, cleaning, and transforming (ETL) before loading into the model. • Power Pivot: in‑memory data model and engine for DAX calculations and relationships (used during/after load). 3. What is the difference between measure vs calculated column?  • Measure: calculated at query time, used in visuals (e.g., summaries, ratios). • Calculated column: computed at refresh time, stored in the model (uses more memory). Prefer measures for aggregations. 4. Explain CALCULATE() function.  CALCULATE() changes the context of a calculation by applying filters.  Example: Total Sales = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "West") computes sum only for West region. 5. What are relationships (1:M, M:M)?  • 1:M (one‑to‑many): one row in the “1” table links to many rows in the “M” table (most common). • M:M (many‑to‑many): handled via an intermediate bridge table with foreign keys on both sides. 6. How do you handle many‑to‑many?  Create a bridge table (junction table) that contains foreign keys to both related tables. Then set 1:M relationships from each original table to the bridge. 7. What is row‑level security (RLS)?  RLS restricts which rows a user can see in a report (e.g., by SalesRegion = “UserRegion”). Defined in the model with DAX filter expressions and applied by user roles. 8. How do you setup incremental refresh?  • Mark your tables as “incrementally refreshable” in the model. • Define a date/time column and ranges (e.g., last 3 years full, last 60 days incremental). • Set refresh schedule in the Power BI service with gateways if needed. 9. What is the difference between filters vs slicers?  • Filters: rules applied behind the scenes (e.g., in page/report level filters) that always apply. • Slicers: interactive controls on the report canvas that users click to change what data is shown. 10. What is a data model?  A data model is the structure in Power BI that holds tables, relationships, calculated columns, measures, and hierarchies, forming the semantic layer for reporting. 11. How do you publish and share reports?  • Publish from Power BI Desktop to a workspace in Power BI Service. • Share via apps, workspaces, or by granting access to specific users/groups; use RLS and sharing permissions to control who sees what. 12. What is Performance Analyzer tool?  Performance Analyzer in Power BI Desktop records how long each visual takes to render and which DAX queries run, helping identify slow visuals or large queries. 13. How do you create month‑on‑month growth DAX?  MoM Growth = VAR CurrentSales = [Total Sales]  VAR PreviousSales = CALCULATE([Total Sales], DATEADD('Date'[Date], -1, MONTH))  RETURN  DIVIDE(CurrentSales - PreviousSales, PreviousSales) 14. How do you use custom visuals?  Download a custom visual from the marketplace, add it to the report in Power BI Desktop or Service, then configure like a native visual (fields, formatting, interactivity). 15. What is gateway for refresh?  An on‑premises gateway connects Power BI Service to data sources behind your firewall (e.g., SQL Server, file shares). It enables scheduled refresh for datasets that pull from those sources. 16. What is a .pbix file?  A .pbix file is the Power BI Desktop project file that contains the report layout, queries, data model, and DAX logic. It can be opened in Power BI Desktop or published to the service. 17. What are quick measures examples?  Quick measures are auto‑generated DAX calculations with a UI. Examples:  • Average of a column.

📈 Pandas Essentials Every Data Analyst Should Know Whether you're cleaning messy datasets, analyzing trends, or preparing data for dashboards, Pandas is one of the most important Python libraries every data analyst should master. This cheat sheet covers the core functions you'll use in real-world projects—from loading data and filtering rows to GroupBy, merging, handling missing values, indexing, and visualization. 💡 Why this is useful: • Build a strong foundation in Pandas • Speed up your data analysis workflow • Prepare for Data Analyst interviews • Practice each function with your own datasets to truly master it 📌 Save this post for quick revision and refer back whenever you're working on a project.

Haqiqiy Data Analyst qanday bo'lishi kerak?

🚀 Python Collections Cheat Sheet — Bir rasmda hammasi! Python o'rganayotganlar yoki Data Analytics yo'nalishida ishlayotganlar uchun eng muhim 4 ta collectionni bitta cheat sheet ko'rinishida tayyorladim. 📚 Bu rasmda quyidagilar jamlangan: ✅ List ([]) — metodlari, slicing, list comprehension va ishlatilish joylari ✅ Tuple (()) — immutable obyektlar, packing/unpacking, qayerda ishlatiladi ✅ Dictionary ({}) — key-value, eng kerakli metodlar va dictionary comprehension ✅ Set ({}) — unikal elementlar, union/intersection/difference amallari Shuningdek: 📌 Collection Functions 📌 Conversion (list(), tuple(), set(), dict()) 📌 Big-O Complexity 📌 Comparison Table 📌 Interview uchun muhim farqlar 💡 Ushbu cheat sheet Python asoslarini mustahkamlash va texnik suhbatlarga tayyorlanishda qo'l keladi. 📥 Rasmni saqlab oling va kerak bo'lganda foydalaning. https://t.me/muxtorazam

Ko'ring, toki oilalar saodat qo'rg'oniga aylansin! "Alohida mavzu"ning navbatdagi sonida oilaviy hayotning bosqichlari va saodatli oila qurish uchun bosiladigan qadamlar haqida gap boradi. Suhbatimiz mehmonlari esa Mahmud Usmon va Ikrom Sharif ustozlar bo'lishdi. Suhbat davomida siz: -Inson o'ziga juft tanlayotganda nimalarga e'tibor berishi kerak? -Oilada sevgi-muhabbatning roli qanchalik muhim? -Yosh oilalar qanday qilib baxt poydevorini quradilar? -Erkak kishi chinakam rahbarga aylanish sirlari nimalar? -Oilaviy hayot bosqichlari qanday kabi qiziqarli va dolzarb masalalar bo'yicha javobga ega bo'lasiz ❗️Baxtiyor oila qurish uchun kerak bo'ladigan sifatlarni bilib oling! Videoni tomosha qiling: https://youtu.be/mxBgie7pAUE @alohidamavzu

Which month recorded the highest sales? Are sales targets being met? Which category has the highest profit margin? 📌 Step 13: Business Recommendations Based on the insights, provide recommendations. Examples: Increase inventory for top-selling products, Launch promotions in underperforming regions, Focus marketing on high-value customers, Reduce costs for low-margin products A dashboard is valuable when it drives decisions, not just displays data. 📌 Step 14: Documentation Document: Business Problem, Data Sources, Data Cleaning Steps, Data Model, DAX Measures, Dashboard Features, Key Insights, Recommendations 📌 Step 15: Publish Your Portfolio Share your project on: GitHub, LinkedIn Include: Dashboard screenshots, Project description, Technologies used, Key business insights 📌 Common Interview Questions 1. Why did you choose a Star Schema? 2. Which DAX measures were most challenging? 3. How did you optimize performance? 4. Why did you use Power Query? 5. How did you implement RLS? 6. What business insights did your dashboard provide? 7. How did you validate your data? 8. What improvements would you make? 🔥 Top 25 Real-World Power BI Project Ideas 1. Retail Sales Dashboard 2. HR Analytics Dashboard 3. Finance Dashboard 4. Banking Dashboard 5. Healthcare Dashboard 6. Supply Chain Dashboard 7. E-commerce Dashboard 8. Manufacturing Dashboard 9. Marketing Analytics Dashboard 10. Customer Churn Dashboard 11. Call Center Dashboard 12. Insurance Claims Dashboard 13. Hotel Booking Dashboard 14. Education Analytics Dashboard 15. Telecom Dashboard 16. Logistics Dashboard 17. Inventory Management Dashboard 18. Restaurant Analytics Dashboard 19. Real Estate Dashboard 20. Pharmaceutical Dashboard 21. Social Media Analytics Dashboard 22. Website Analytics Dashboard 23. Energy Consumption Dashboard 24. Stock Market Dashboard 25. Executive Business Dashboard Double Tap ❤️ For More

🚀 End-to-End Power BI Project (Industry Project) Now that you've learned Power BI from beginner to advanced, it's time to combine everything into one real-world project. This project simulates how Power BI is used in an organization—from receiving raw data to delivering a business dashboard. 🎯 Project Goal Build a complete Retail Sales Analytics Dashboard that answers key business questions for management. By the end of this project, you'll apply: SQL, Excel, Power Query, Data Modeling, DAX, Visualization, Power BI Service, Performance Optimization 📌 Step 1: Business Requirement A retail company wants a dashboard to answer: What are total sales and profits? Which products sell the most? Which regions perform best? Which customers generate the most revenue? What are the monthly sales trends? Are sales targets being achieved? 📌 Step 2: Data Sources Use multiple sources to simulate a real project. Excel: Product List, Sales Targets SQL Database: Sales Transactions, Customer Data CSV Files: Regional Information, Store Details 📌 Step 3: Data Collection Import all data into Power BI using Get Data. Expected tables: FactSales, DimProduct, DimCustomer, DimRegion, DimDate, SalesTarget 📌 Step 4: Data Cleaning (Power Query) Perform these transformations: ✅ Remove duplicates ✅ Remove null values ✅ Change data types ✅ Standardize region names ✅ Split customer names ✅ Merge customer data ✅ Append monthly sales files ✅ Remove unnecessary columns 📌 Step 5: Data Modeling Build a Star Schema. DimDate | DimCustomer — FactSales — DimProduct | DimRegion Relationships: DateID → Date, ProductID → Product, CustomerID → Customer, RegionID → Region 📌 Step 6: Create DAX Measures Revenue Revenue = SUM(FactSales[Revenue]) Profit Profit = SUM(FactSales[Profit]) Profit Margin Profit Margin = DIVIDE([Profit],[Revenue]) Total Orders Orders = DISTINCTCOUNT(FactSales[OrderID]) YTD Revenue Revenue YTD = TOTALYTD([Revenue], DimDate[Date]) Previous Year Revenue Revenue PY = CALCULATE([Revenue], SAMEPERIODLASTYEAR(DimDate[Date])) Growth % Growth % = DIVIDE([Revenue]-[Revenue PY], [Revenue PY]) 📌 Step 7: Dashboard Design KPI Cards: Revenue, Profit, Orders, Customers, Profit Margin Charts: Line Chart: Monthly Revenue Trend Column Chart: Sales by Region Bar Chart: Top 10 Products Donut Chart: Category Contribution Map: Sales by Region Matrix: Product × Region 📌 Step 8: Add Interactivity Include: ✅ Slicers: Year, Region, Product, Category ✅ Drill-down ✅ Drill-through ✅ Bookmarks ✅ Custom Tooltips 📌 Step 9: Performance Optimization Apply: ✅ Remove unused columns ✅ Optimize DAX ✅ Reduce visuals ✅ Use Star Schema ✅ Enable Query Folding 📌 Step 10: Security Implement Row-Level Security RLS. Example: North Manager → North Region only, South Manager → South Region only 📌 Step 11: Publish Publish the report to Power BI Service. Then: Create a Workspace, Create a Dashboard, Publish an App, Configure Scheduled Refresh 📌 Step 12: Business Insights The dashboard should answer questions such as: Which region has the highest sales? Which products are most profitable? Which customers contribute the most revenue?

AI is not likely to replace you. But someone who uses AI better than you might.

real life begins when you die...

⚠️ EHTIYOT BO‘LING! Bugun bir necha daqiqa ichida menga ketma-ket 3 ta Eron (+98) va 1 ta Turkiya (+90) raqamidan qo‘ng‘iroq bo‘ldi. Begona xalqaro raqamlar orqali amalga oshiriladigan bunday qo‘ng‘iroqlar ko‘pincha spam yoki firibgarlik bilan bog‘liq bo‘lishi mumkin. ❗️ Nimalarga e'tibor berish kerak? ✅ Noma'lum raqamlarga qayta qo‘ng‘iroq qilmang. ✅ SMS yoki messenjerlarda yuborilgan kodlarni hech kimga bermang. ✅ Bank karta ma'lumotlari va shaxsiy ma'lumotlaringizni ulashmang. ✅ Shubhali raqamlarni bloklang. Telefonni ko‘tarmaganingizning o‘zi bilan hech narsa bo‘lmaydi. Asosiy xavf — firibgarlarga javob berish yoki ularga ma'lumot taqdim etishdir.

📊 Data Analyst Roadmap: 0 → Junior → Ish Data analyst bo‘lish — bu bitta toolni o‘rganish emas, bosqichma-bosqich rivojlanish. Quyidagi yo‘l eng to‘g‘ri va amaliy roadmap hisoblanadi: 🔹 0-bosqich – Tayyorgarlik Analitik fikrlashni shakllantirish, asosiy matematika va ingliz tilini mustahkamlash. Bu bosqichsiz keyingi bilimlar tushunarsiz bo‘ladi. 🔹 1-bosqich – Excel Ma’lumot bilan ishlashni o‘rganish: hisoblash, tozalash, pivotlar va oddiy tahlil. Ko‘p kompaniyalarda birinchi ish quroli — Excel. 🔹 2-bosqich – SQL Database’dan to‘g‘ri va tez data olish. Real ishda data analystning eng ko‘p ishlatadigan tili — SQL. 🔹 3-bosqich – Python Avtomatlashtirish va chuqur tahlil. Katta data, murakkab hisob-kitoblar va takroriy ishlarni yengillashtiradi. 🔹 4-bosqich – Power BI Olingan natijani biznesga tushunarli ko‘rinishda ko‘rsatish. Dashboard va KPI’lar aynan shu bosqichda yaratiladi. 🔹 5-bosqich – Portfolio + Ish Real loyihalar, GitHub, dashboardlar va resume orqali ish topish bosqichi. Obuna bo'ling

Assalomu alaykum! Sizni va oila a’zolaringizni kirib kelayotgan muborak Qurbon hayiti bilan chin qalbimdan tabriklayman. Ushbu ulug‘ ayyom xonadoningizga tinchlik, baraka, sihat-salomatlik va cheksiz quvonch olib kelsin. Qilgan ibodat va ezgu niyatlaringiz ijobat bo‘lsin. Hayit ayyomingiz muborak bo‘lsin!😊

https://youtu.be/UkcZebkT5-o?si=lzN1jcCmo3Bk33nY

Teaching is not just a profession. It is a responsibility to shape people’s futures. Over the past 3–4 years, I have been involved in teaching and mentoring, including 1.5 years dedicated to teaching English while preparing for IELTS myself. That journey taught me something deeper than language — it taught me patience, discipline, communication, and the power a mentor can have in someone’s life. Today, I work as a Data Analytics mentor at MAAB Academy, and with every student I meet, I realize that teaching is far more than explaining concepts or delivering lessons. Students come with different stories. Some come with ambition. Some come with fear and self-doubt. And some come simply looking for one person who believes in them. That is why mentoring is such a meaningful and responsible path. A mentor does not only transfer knowledge. A mentor builds confidence, changes mindsets, and sometimes even changes the direction of a person’s life. People often see the visible side of teaching — the lessons, the results, the achievements. But behind it, there are long hours of preparation, emotional responsibility, patience, and silent struggles that many never notice. Yet despite all the challenges, there is something incredibly powerful about seeing a student grow. Watching someone who once doubted themselves begin to believe in their potential is one of the greatest rewards a mentor can experience. I believe teaching is one of the most honorable professions in the world because knowledge never stops with one person. What you teach today can continue impacting lives for years through the people you helped. To every teacher and mentor out there: your work matters more than you think. Because strong societies are not built by buildings or technology alone — they are built by educators who dedicate themselves to guiding others.

🤖 “AI Data Analystlarni almashtiradi” deyayotganlar bir narsani unutmoqda… Sun’iy intellekt juda tez ishlaydi. Ma’lumot topadi. Grafik chizadi. Hisobot ham tayyorlaydi. Lekin… ❌ AI biznesni tushunmaydi ❌ AI to‘g‘ri savol bera olmaydi ❌ AI mijozning asl muammosini his qilmaydi ❌ AI strategik qaror uchun javobgarlik olmaydi Data Analyst — bu shunchaki dashboard yasovchi emas. Yaxshi analyst: 📌 ma’lumot ortidagi muammoni ko‘radi 📌 noto‘g‘ri datani sezadi 📌 biznesga foyda keltiradigan insight topadi 📌 rahbariyatga qaror qabul qilishda yordam beradi AI esa — shunchaki kuchli instrument. Excel kalkulyatorni yo‘q qilmagani kabi, AI ham haqiqiy analystlarni yo‘q qila olmaydi. Lekin bir haqiqat bor: ⚠️ AI ishlata olmaydigan analystlarni, AI ishlata oladigan analystlar almashtiradi. Shuning uchun qo‘rqish emas, AI bilan ishlashni o‘rganish kerak .