Power BI & Tableau Resources
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🆓 Resources to learn Power BI, Tableau & Data Visualisation Perfect channel to start learning everything about Data Analytics Admin: @coderfun
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الشبكة:📈 نظرة تحليلية على قناة تيليجرام Power BI & Tableau Resources
تُعد قناة Power BI & Tableau Resources (@powerbi_analyst) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 55 607 مشتركاً، محتلاً المرتبة 3 076 في فئة التعليم والمرتبة 6 316 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 55 607 مشتركاً.
بحسب آخر البيانات بتاريخ 18 يوليو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 169، وفي آخر 24 ساعة بمقدار 17، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 2.07%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.09% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 153 مشاهدة. وخلال اليوم الأول يجمع عادةً 605 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل dax, visual, dashboard, chart, slicer.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“🆓 Resources to learn Power BI, Tableau & Data Visualisation
Perfect channel to start learning everything about Data Analytics
Admin: @coderfun”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 19 يوليو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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| 2 | Complete Power BI Topics for Data Analysts 👇👇
1. Introduction to Power BI
- Overview and architecture
- Installation and setup
2. Loading and Transforming Data
- Connecting to various data sources
- Data loading techniques
- Data cleaning and transformation using Power Query
3. Data Modeling
- Creating relationships between tables
- DAX (Data Analysis Expressions) basics
- Calculated columns and measures
4. Data Visualization
- Building reports and dashboards
- Visualization best practices
- Custom visuals and formatting options
5. Advanced DAX
- Time intelligence functions
- Advanced DAX functions and scenarios
- Row context vs. filter context
6. Power BI Service
- Publishing and sharing reports
- Power BI workspaces and apps
- Power BI mobile app
7. Power BI Integration
- Integrating Power BI with other Microsoft tools (Excel, SharePoint, Teams)
- Embedding Power BI reports in websites and applications
8. Power BI Security
- Row-level security
- Data source permissions
- Power BI service security features
9. Power BI Governance
- Monitoring and managing usage
- Best practices for deployment
- Version control and deployment pipelines
10. Advanced Visualizations
- Drillthrough and bookmarks
- Hierarchies and custom visuals
- Geo-spatial visualizations
11. Power BI Tips and Tricks
- Productivity shortcuts
- Data exploration techniques
- Troubleshooting common issues
12. Power BI and AI Integration
- AI-powered features in Power BI
- Azure Machine Learning integration
- Advanced analytics in Power BI
13. Power BI Report Server
- On-premises deployment
- Managing and securing on-premises reports
- Power BI Report Server vs. Power BI Service
14. Real-world Use Cases
- Case studies and examples
- Industry-specific applications
- Practical scenarios and solutions
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| 5 | ✅ Business Intelligence (BI) Acronyms You Should Know 📊💡
BI → Business Intelligence
ETL → Extract, Transform, Load
ELT → Extract, Load, Transform
DWH → Data Warehouse
OLAP → Online Analytical Processing
OLTP → Online Transaction Processing
KPI → Key Performance Indicator
SLA → Service Level Agreement
SCD → Slowly Changing Dimension
CDC → Change Data Capture
MDM → Master Data Management
EAV → Entity Attribute Value
FACT → Fact Table
DIM → Dimension Table
STAR → Star Schema
SNOWFLAKE → Snowflake Schema
MTD → Month To Date
QTD → Quarter To Date
YTD → Year To Date
MoM → Month over Month
YoY → Year over Year
ROI → Return on Investment
TAT → Turn Around Time
💡Don’t just expand acronyms — explain where they’re used (ETL in pipelines, KPIs in dashboards, OLAP in analysis).
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| 7 | 🚀 Power BI Essentials Series
🔄 Topic 2: Power Query (Complete Beginner's Guide)
Before creating charts and dashboards, your data needs to be clean, consistent, and ready for analysis.
That's where Power Query comes in.
Power Query is one of the most important features in Power BI because real-world data is rarely perfect.
🎯 What is Power Query?
Power Query is Power BI's ETL (Extract, Transform, Load) tool.
It helps you:
✅ Import data
✅ Clean data
✅ Transform data
✅ Combine multiple data sources
✅ Prepare data for analysis
No coding is required for most transformations.
📌 Why is Power Query Important?
Imagine you receive an Excel file with:
❌ Blank rows
❌ Duplicate records
❌ Incorrect date formats
❌ Missing values
❌ Extra spaces
Instead of fixing these manually every month, Power Query automates the process.
Simply click Refresh, and all the cleaning steps run automatically.
📂 How to Open Power Query?
1. Open Power BI Desktop
2. Click Home → Transform Data
3. The Power Query Editor opens
This is where you'll clean and prepare your data.
📌 Power Query Interface
• Queries Pane: Displays all imported tables
• Data Preview: Shows your dataset
• Applied Steps: Records every transformation you perform
• Ribbon: Contains commands for cleaning and transforming data
📌 Common Data Cleaning Tasks
1️⃣ Remove Duplicates
Used when the same record appears multiple times.
Example: Customer ID 101, 101, 102 → 101, 102
2️⃣ Remove Blank Rows
Blank rows can affect calculations and visuals. Always remove unnecessary empty rows.
3️⃣ Change Data Types
Assign the correct data type.
Examples: Date → Date, Sales → Decimal Number, Quantity → Whole Number, Customer Name → Text
Incorrect data types can cause errors in reports.
4️⃣ Rename Columns
Replace generic names like Column1, Column2
With meaningful names: Customer Name, Order Date, Revenue
5️⃣ Filter Rows
Keep only the data you need.
Examples: Sales > 1000, Region = North, Year = 2025
Filtering early can improve performance.
6️⃣ Replace Values
Quickly replace incorrect or outdated values.
Example: Replace "Mum" → "Mumbai" for consistency.
📌 Data Transformation Features
• Split Column: Separate one column into multiple. John Smith → First Name: John, Last Name: Smith
• Merge Columns: Combine columns. John + Smith → John Smith
• Merge Queries: Combine data from two tables based on a common column. Like SQL JOINs. Sales Table + Customer Table
• Append Queries: Add rows from one table to another. January Sales + February Sales
• Group By: Summarize data. Sales by Region: North → ₹5,00,000, South → ₹4,20,000
• Pivot Column: Convert row values into columns
• Unpivot Columns: Convert multiple columns into rows. Super useful for reporting
📌 Applied Steps
Every transformation is automatically recorded:
Source → Changed Type → Removed Duplicates → Filtered Rows → Renamed Columns
If the source data changes, simply click Refresh and all steps run again.
📌 Best Practices
✅ Remove unnecessary columns first
✅ Filter rows early
✅ Use meaningful query names
✅ Verify data types
✅ Keep transformation steps organized
❌ Common Mistakes
❌ Cleaning data manually in Excel every month
❌ Loading unnecessary columns
❌ Ignoring incorrect data types
❌ Creating duplicate queries
❌ Skipping data validation
💼 Real-World Example
A company receives a monthly sales file.
Using Power Query:
1. Import the Excel file
2. Remove blank rows
3. Remove duplicate records
4. Convert Order Date to Date format
5. Merge Customer details
6. Append monthly sales files
7. Load the cleaned data into Power BI
Next month: replace the file → click Refresh → done. | 822 |
| 8 | 📊 Frequently Asked Power BI Interview Questions (Intermediate Level)
1️⃣ What is the difference between a Measure and a Calculated Column?
💡 Answer:
Measure → Calculated at query time based on the current filter context.
Calculated Column → Calculated during data refresh and stored in the data model.
2️⃣ What is the purpose of the CALCULATE() function?
💡 Answer:
CALCULATE() changes the filter context before evaluating an expression.
3️⃣ What is the difference between Import Mode and DirectQuery?
💡 Answer:
Import Mode → Stores data inside Power BI for faster performance.
DirectQuery → Queries the source database in real time without importing the data.
4️⃣ What is the difference between SUM() and SUMX()?
💡 Answer:
SUM() → Adds the values of a single column.
SUMX() → Evaluates an expression for each row and then sums the results.
❤️ React for more Power BI interview questions! | 1 262 |
| 9 | 🚀 Power BI Essentials Series
📥 Topic 1: Get Data in Power BI (Beginner's Guide)
Every Power BI report starts with one step—connecting to your data.
Power BI can connect to hundreds of data sources, making it easy to analyze data from different systems in one place.
🎯 What is "Get Data"?
Get Data is the feature used to import or connect data from various sources into Power BI Desktop.
You can find it on the Home tab.
Once connected, you can clean, transform, model, and visualize the data.
📂 Common Data Sources
📊 Excel
The most common source for beginners.
Examples: Sales Reports, Employee Data, Budget Files, Inventory Lists
Supported formats: .xlsx, .xls
📄 CSV (Comma-Separated Values)
CSV files are lightweight and commonly used for data exchange.
Examples: Website exports, Sales transactions, Customer lists
🗄️ SQL Server
Used by most organizations to store business data.
Examples: Customer database, Orders, Products, Transactions
Power BI can connect directly to SQL Server databases.
🌐 Web
Import data directly from web pages or APIs.
Examples: Public datasets, Exchange rates, Weather information, REST APIs
☁️ SharePoint
Many organizations store Excel files and lists in SharePoint.
Power BI can connect directly to: SharePoint Lists, SharePoint Folders, SharePoint Online
📁 Folder
Instead of importing files one by one, connect to an entire folder.
Useful when: Daily reports are saved in one folder, Monthly CSV files need to be combined
Power BI can automatically combine files with the same structure.
🔗 Connection Modes
1. Import
Data is copied into Power BI.
Advantages:
✅ Fast performance
✅ Best for dashboards
✅ Full DAX support
Best for: Small to medium datasets
2. DirectQuery
Power BI queries the database whenever a user interacts with the report.
Advantages:
✅ Near real-time data
✅ No data stored in Power BI
Limitations: Slower than Import, Some DAX functions are restricted
Best for: Large enterprise databases that require up-to-date information
3. Live Connection
Power BI connects to an existing semantic model or analysis service without importing data.
Advantages:
✅ Single source of truth
✅ Centralized data model
Best for: Enterprise reporting environments
📥 Steps to Import Data
1. Open Power BI Desktop
2. Click Home → Get Data
3. Select a data source (Excel, CSV, SQL Server, etc.)
4. Browse and select the file or enter the server details
5. Preview the data
6. Choose the required tables
7. Click Load or Transform Data
📌 Load vs Transform Data
Load: Imports data directly into Power BI. Choose this when your data is already clean.
Transform Data: Opens Power Query Editor.
Choose this when you need to: Remove duplicates, Rename columns, Change data types, Filter rows, Clean data
Most real-world projects require transforming data before loading it.
📋 Best Practices
✅ Import only the tables you need
✅ Remove unnecessary columns
✅ Verify data types after loading
✅ Give tables meaningful names
✅ Use Transform Data instead of cleaning data manually in Excel whenever possible
❌ Common Mistakes
❌ Importing every table from a database
❌ Loading unnecessary columns
❌ Ignoring incorrect data types
❌ Loading duplicate data
❌ Cleaning data manually in Excel every time instead of using Power Query
💼 Real-World Example
A retail company receives a monthly Sales.xlsx file.
Workflow: Connect to the Excel file using Get Data → Open Transform Data → Remove blank rows → Fix date formats → Remove duplicate records → Load the cleaned data into Power BI → Build reports and dashboards
This simple workflow is used in many organizations. | 1 331 |
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| 11 | 🚀 Power BI Essentials Every Beginner Must Learn
If you're starting with Power BI, focus on these core concepts before moving to advanced topics.
📥 1. Get Data
Learn how to connect Power BI to:
Excel, CSV, SQL Server, Web APIs, SharePoint
🔄 2. Power Query
Learn to:
Clean data, Remove duplicates, Handle null values, Merge & Append queries, Change data types
⭐ 3. Data Modeling
Understand:
Star Schema, Fact Tables, Dimension Tables, Relationships, Cardinality
🧮 4. DAX Data Analysis Expressions
Master:
Measures, Calculated Columns, CALCULATE(), FILTER(), IF(), Time Intelligence
📊 5. Data Visualization
Create:
Bar Charts, Line Charts, Pie Charts, Tables, Matrix, KPI Cards, Maps
🎛️ 6. Filters & Slicers
Learn:
Visual Filters, Page Filters, Report Filters, Slicers, Drill-down, Drill-through
☁️ 7. Power BI Service
Understand:
Publishing Reports, Workspaces, Dashboards, Apps, Sharing Reports
🔐 8. Security
Learn:
Row-Level Security RLS, User Permissions, Workspace Roles
⚡ 9. Performance Optimization
Know how to:
Reduce model size, Optimize DAX, Use Query Folding, Improve report performance
📂 10. Real-World Projects
Build dashboards for:
Sales Analytics, HR Analytics, Finance, Inventory, Marketing
Projects are the best way to apply what you've learned.
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| 14 | 🚀 Power BI Tools Every Developer Should Know
Power BI isn't just one application—it's a complete ecosystem of tools for building, sharing, securing, and managing business intelligence solutions.
🖥️ 1. Power BI Desktop
The primary development tool used to:
✅ Connect to data sources
✅ Transform data with Power Query
✅ Build data models
✅ Write DAX
✅ Create reports and dashboards
Best For: Report development
☁️ 2. Power BI Service
The cloud platform used to:
✅ Publish reports
✅ Share dashboards
✅ Schedule data refresh
✅ Manage Workspaces
✅ Configure Row-Level Security (RLS)
Best For: Collaboration and report sharing
📱 3. Power BI Mobile
Allows users to access reports on: Android, iPhone, Tablets
Features:
✅ View dashboards
✅ Receive alerts
✅ Monitor KPIs
Best For: Business users on the go
🚪 4. On-Premises Data Gateway
Securely connects Power BI Service to on-premises data sources.
Used for:
✅ SQL Server
✅ Oracle
✅ Excel files
✅ Local databases
Best For: Scheduled refresh of on-premises data
🔄 5. Power Query
The built-in ETL tool in Power BI.
Used for:
✅ Cleaning data
✅ Removing duplicates
✅ Merging tables
✅ Appending data
✅ Changing data types
Best For: Data preparation
📊 6. DAX (Data Analysis Expressions)
The formula language in Power BI.
Used to create:
✅ Measures
✅ Calculated Columns
✅ Calculated Tables
✅ KPIs
Best For: Business calculations
🧩 7. Power BI Report Builder
Used to create Paginated Reports.
Ideal for:
✅ Invoices
✅ Financial Statements
✅ Operational Reports
✅ Printable Reports
Best For: Pixel-perfect reporting
📦 8. Power BI Dataflows
Reusable cloud-based ETL.
Benefits:
✅ Centralized data preparation
✅ Reusable transformations
✅ Shared datasets
Best For: Enterprise data preparation
🏢 9. Power BI Workspace
A collaborative area used to store: Reports, Dashboards, Semantic Models, Dataflows
Used by teams to collaborate on BI projects.
Best For: Team collaboration
📲 10. Power BI Apps
Apps package reports and dashboards into a single experience for business users.
Benefits:
✅ Easy distribution
✅ Centralized updates
✅ Better user experience
Best For: Sharing reports across an organization
🎯 Power BI Ecosystem at a Glance
Tool: Power BI Desktop — Primary Purpose: Report Development
Tool: Power BI Service — Primary Purpose: Cloud Collaboration & Sharing
Tool: Power BI Mobile — Primary Purpose: Mobile Report Access
Tool: On-Premises Data Gateway — Primary Purpose: Connect Local Data Sources
Tool: Power Query — Primary Purpose: Data Cleaning & Transformation
Tool: DAX — Primary Purpose: Business Calculations
Tool: Power BI Report Builder — Primary Purpose: Paginated Reports
Tool: Power BI Dataflows — Primary Purpose: Reusable Data Preparation
Tool: Power BI Workspace — Primary Purpose: Team Collaboration
Tool: Power BI Apps — Primary Purpose: Report Distribution
Double Tap ❤️ For More
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| 15 |  𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗲𝘀𝗲 𝗛𝗶𝗴𝗵-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯𝘀 🔥
This guide highlights 3 powerful skills that are opening doors to high-paying roles across tech and business .🎓
Perfect For
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🚀 Start learning today. Build in-demand skills. Position yourself for better opportunities and bigger career growth. | 1 022 |
| 16 | Data Analyst Interview Preparation Roadmap ✅
Technical skills to revise
- SQL
Write queries from scratch.
Practice joins, group by, subqueries.
Handle duplicates and NULLs.
Window functions basics.
- Excel
Pivot tables without help.
XLOOKUP and IF confidently.
Data cleaning steps.
- Power BI or Tableau
Explain data model.
Write basic DAX.
Explain one dashboard end to end.
- Statistics
Mean vs median.
Standard deviation meaning.
Correlation vs causation.
- Python. If required
Pandas basics.
Groupby and filtering.
Interview question types
- SQL questions
Top N per group.
Running totals.
Duplicate records.
Date based queries.
- Business case questions
Why did sales drop.
Which metric matters most and why.
- Dashboard questions
Explain one KPI.
How users will use this report.
- Project questions
Data source.
Cleaning logic.
Key insight.
Business action.
Resume preparation
- Must have Tools section.
- One strong project.
- Metrics driven points.
Example: Improved reporting time by 30 percent using Power BI.
Mock interviews
- Practice explaining out loud.
- Time your answers.
- Use real datasets.
Daily prep plan
1 SQL problem.
1 dashboard review.
10 interview questions.
- Common mistakes
Memorizing queries.
No project explanation.
Weak business reasoning.
- Final task
- Prepare one project story.
- Prepare one SQL solution on paper.
- Prepare one business metric explanation.
Double Tap ♥️ For More | 1 053 |
| 17 |  𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀🎓
Offers a wide range of free learning resources through Microsoft Learn, helping students, freshers, and professionals build job-ready skills at their own pace.
✅ 100% FREE self-paced learning modules
✅ Official learning platform from Microsoft
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Explore Microsoft’s free resources. Build in-demand skills and make your profile stronger. | 1 042 |
| 18 | 🚀 Power BI A–Z Terms Every Beginner Should Know (Part 2)
A — Append Queries
Combines two or more tables by adding rows vertically in Power Query.
B — Bi-Directional Filtering
Allows filters to flow in both directions between related tables. Use carefully to avoid ambiguity.
C — Composite Model
A data model that combines Import and DirectQuery tables in the same report.
D — Dashboard
A single-page view in Power BI Service that displays key visuals and KPIs.
E — Export Data
Allows users to export data from visuals to Excel or CSV (subject to permissions).
F — Filter Context
The set of filters applied to a calculation through slicers, visuals, or report filters.
G — Group By
A Power Query transformation used to summarize and aggregate data.
H — Home Ribbon
The main toolbar in Power BI Desktop for importing data, refreshing, publishing, and managing reports.
I — Inactive Relationship
An inactive relationship that exists in the model but isn't used unless activated with USERELATIONSHIP().
J — Join
Combines data from multiple tables in Power Query using:
• Inner Join
• Left Join
• Right Join
• Full Outer Join
K — Key Column
A unique column used to create relationships between tables.
L — Lakehouse
A Microsoft Fabric storage architecture that combines the benefits of Data Lakes and Data Warehouses.
M — Matrix Visual
A table-like visual that supports hierarchical rows, columns, and subtotals.
N — Navigation Buttons
Interactive buttons used to move between report pages or bookmarks.
O — On-Premises Data Gateway
A gateway that securely connects Power BI Service to on-premises data sources.
P — Parameter
A dynamic value in Power Query used to make data sources and queries more flexible.
Q — Q&A Visual
An AI-powered visual that lets users ask questions in natural language to generate charts.
R — Report
A collection of interactive pages containing visuals built in Power BI Desktop.
S — Slicer
An interactive filter that allows users to filter report data easily.
T — Tooltip
A popup that displays additional information when hovering over a visual.
U — Unpivot
Converts multiple columns into rows, making data suitable for analysis.
V — Visual-Level Filter
A filter that affects only one specific visual on the report page.
W — Waterfall Chart
Shows how positive and negative values contribute to a final total.
X — X-Axis
The horizontal axis used in charts to display categories or time.
Y — YAML Theme
A structured format sometimes used for managing report themes and configurations in advanced workflows.
Z — Z-Order
Controls the stacking order of visuals, determining which visual appears in front of another.
Double Tap ❤️ For More
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| 19 |  GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model
The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.
What’s inside:
🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘Two MTP heads, enabling up to 2.2x faster generation;
🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘A new online RL stage after SFT and DPO.
Results:
🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.
The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.
➡️ HuggingFace | 1 051 |
| 20 |  𝗔𝗜 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 😍
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