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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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Сеть:📈 Аналитический обзор Telegram-канала Power BI & Tableau Resources
Канал Power BI & Tableau Resources (@powerbi_analyst) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 55 784 подписчиков, занимая 3 070 место в категории Образование и 6 329 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 55 784 подписчиков.
Согласно последним данным от 30 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 320, а за последние 24 часа — 3, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.43%. В первые 24 часа после публикации контент обычно набирает 1.09% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 357 просмотров. В течение первых суток публикация набирает 608 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как dax, visual, dashboard, chart, slicer.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“🆓 Resources to learn Power BI, Tableau & Data Visualisation
Perfect channel to start learning everything about Data Analytics
Admin: @coderfun”
Благодаря высокой частоте обновлений (последние данные получены 30 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
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Посты канала
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| 2 | ✅ 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. | 375 |
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| 4 | Complete SQL road map
👇👇
1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS
2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY
3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN
4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL
5. Data Manipulation
• INSERT
• UPDATE
• DELETE
6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes
7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending
8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX
9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN
10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries
11.Views
• Creating
• Modifying
• Dropping Views
12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT
13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)
14.Triggers
• Trigger Events
• Trigger Execution and Syntax
15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER
16.Optimizations
• Indexing Strategies
• Query Optimization
17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF
18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery
19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences
20. Data Integrity
• Primary Key
• Foreign Key
21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)
22.Full-Text Search
• Full-Text Indexes
• Search Optimization
23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files
24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques
25.Advanced Indexing
• Composite Indexes
• Covering Indexes
26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol
27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization
------------------ END -------------------
Some good resources to learn SQL
1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe
3. Books
• SQL in a Nutshell: https://t.me/DataAnalystInterview/158
4. SQL Interview Questions
https://t.me/sqlanalyst/72?single
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| 6 | Sure! Here’s the text with the asterisks replaced by single asterisks:
🚀 Power BI Essentials Series
🎛️ Topic 6: Filters & Slicers
Filters and Slicers make Power BI reports interactive by allowing users to focus on specific data without changing the underlying dataset.
Instead of creating multiple reports, you can create one dynamic report that users can explore on their own.
🎯 What are Filters?
Filters are used to limit the data displayed in a report or visual.
For example:
• Show only sales from 2026.
• Display data for the North region.
• Show products with sales greater than 10,000.
Filters work in the background and affect what users see.
🎯 What are Slicers?
A Slicer is a visual that allows users to filter report data interactively.
Examples:
• Select a Year
• Select a Region
• Select a Product Category
• Select a Salesperson
As users make selections, all connected visuals update automatically.
📌 Types of Filters
1️⃣ Visual-Level Filter
Affects only one visual.
Example: A bar chart shows sales only for the "North" region, while other visuals remain unchanged.
Best for: Filtering a single chart or table.
2️⃣ Page-Level Filter
Affects every visual on the current report page.
Example: Display only data for the year 2026 across all visuals on that page.
Best for: Page-specific analysis.
3️⃣ Report-Level Filter
Applies the filter to every page in the report.
Example: Show only active customers throughout the entire report.
Best for: Company-wide reporting rules.
📌 Types of Slicers
List Slicer
Displays values as a simple list.
Example: North, South, East, West
Dropdown Slicer
Shows options in a dropdown to save space.
Ideal for reports with many values.
Date Slicer
Allows users to filter by: Year, Quarter, Month, Day, Date Range
Commonly used in sales and finance dashboards.
Numeric Slicer
Filters data based on numbers.
Example: Sales Amount greater than 5,000.
📌 Basic vs Advanced Filtering
Basic Filtering
Users select one or more values.
Example: Region = North or South.
Advanced Filtering
Uses conditions.
Examples:
• Sales > 10,000
• Profit < 500
• Customer Name contains "Tech"
📌 Sync Slicers
Sync Slicers let you use the same slicer across multiple report pages.
Example: If a user selects 2026 on Page 1, the same selection is automatically applied to Page 2 and Page 3.
This creates a consistent reporting experience.
📌 Cross-Filtering vs Cross-Highlighting
Cross-Filtering
Selecting one visual filters the data shown in other visuals.
Cross-Highlighting
Selecting one visual highlights the related portion in other visuals while keeping the remaining data visible.
Both features help users explore relationships in the data.
📌 Best Practices
✅ Use slicers for fields users frequently filter.
✅ Keep slicers at the top or left side of the report.
✅ Use dropdown slicers when there are many options.
✅ Limit the number of slicers to avoid clutter.
✅ Use Sync Slicers for a consistent experience across pages.
❌ Common Mistakes
❌ Adding too many slicers on one page.
❌ Using report-level filters when page-level filters are sufficient.
❌ Forgetting to sync slicers across pages.
❌ Using long, confusing field names in slicers.
❌ Not testing how filters affect all visuals.
💼 Real-World Example
A Sales Dashboard includes:
• Year Slicer → Select 2025 or 2026.
• Region Slicer → North, South, East, West.
• Product Category Slicer → Electronics, Furniture, Clothing.
When a user selects 2026 and North, every chart, table, and KPI updates instantly to show only the relevant data.
🎯 Interview Questions
1. What is the difference between a Filter and a Slicer?
2. Name the three types of filters in Power BI. | 1 195 |
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| 8 | ✅ Data Visualization Mistakes Beginners Should Avoid
1. Choosing the Wrong Chart
- Pie charts for trends fail
- Line charts for categories confuse
- Use bar for comparison
- Use line for time series
2. Too Much Data in One Chart
- Visual clutter
- Hard to read
- Split into multiple charts
3. Ignoring Axis Scales
- Truncated axes mislead
- Uneven scales distort insight
- Start from zero for bars
4. Poor Color Choices
- Too many colors
- Low contrast
- Red green fails for color blindness
- Use 3 to 5 colors max
5. Missing Labels and Titles
- Viewer guesses meaning
- Low trust
- Always add title, axis labels, units
6. Using 3D Charts
- Distorts perception
- Hides values
- Use flat 2D visuals
7. Sorting Data Incorrectly
- Random order hides pattern
- Sort bars by value
- Keep time data chronological
8. No Context
- Numbers without meaning
- No baseline or target
- Add reference lines or benchmarks
9. Overloading Dashboards
- Too many KPIs
- Decision paralysis
- One dashboard. One question
10. No Validation
- Visual looks right but lies
- Data filters missed
- Always cross-check with raw numbers
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| 11 | 📊 Data Analytics Basics Cheatsheet
1. What is Data Analytics?
Analyzing raw data to find patterns, trends, and insights to support decision-making.
2. Types of Data Analytics:
⦁ Descriptive: What happened?
⦁ Diagnostic: Why did it happen?
⦁ Predictive: What might happen next?
⦁ Prescriptive: What should be done?
3. Key Tools & Languages:
⦁ Excel – Quick analysis & charts
⦁ SQL – Query and manage databases
⦁ Python (Pandas, NumPy, Matplotlib)
⦁ Power BI / Tableau – Dashboards & visualization
4. Data Cleaning Basics:
⦁ Handle missing values
⦁ Remove duplicates
⦁ Convert data types
⦁ Standardize formats
5. Exploratory Data Analysis (EDA):
⦁ Summary stats (mean, median, mode)
⦁ Data distribution
⦁ Correlation matrix
⦁ Visual tools: bar charts, boxplots, scatter plots
6. Data Visualization:
⦁ Use charts to simplify insights
⦁ Choose chart types based on data (line for trends, bar for comparisons, pie for proportions)
7. SQL Essentials:
⦁ SELECT, WHERE, JOIN, GROUP BY, HAVING, ORDER BY
⦁ Aggregate functions: COUNT, SUM, AVG, MAX, MIN
8. Python for Analysis:
⦁ Pandas for dataframes
⦁ Matplotlib/Seaborn for plotting
⦁ Scikit-learn for basic ML models
*9. Metrics to Know:
⦁ Growth %, Conversion rate, Retention rate
⦁ KPIs specific to domain (finance, marketing, etc.)
*10. Real-World Use Cases:
⦁ Customer segmentation
⦁ Sales trend analysis
⦁ A/B testing
⦁ Forecasting demand
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| 13 | 🚀 Complete Tableau Roadmap 📊🔥
🧠 STEP 1: Learn Tableau Basics
✔ Tableau Interface
✔ Connecting Data Sources
✔ Worksheets & Dashboards
✔ Basic Charts & Graphs
🛠 Tools to Learn:
✔ Tableau
✔ Microsoft Excel
📊 STEP 2: Learn Data Preparation
✔ Data Cleaning
✔ Handling Missing Values
✔ Data Types
✔ Data Blending & Joins
🛠 Concepts to Learn:
✔ Extract vs Live Connection
✔ Data Interpreter
✔ Relationships & Joins
📈 STEP 3: Learn Data Visualization
✔ Bar & Line Charts
✔ Pie & Donut Charts
✔ Maps & Geo Visuals
✔ Heatmaps & Treemaps
✔ Scatter Plots
🛠 Visualization Skills:
✔ Formatting Dashboards
✔ Interactive Filters
✔ Tooltips
✔ Highlight Actions
⚡ STEP 4: Learn Calculations & Analytics
✔ Calculated Fields
✔ Table Calculations
✔ Parameters
✔ Sets & Groups
✔ LOD Expressions
🛠 Functions to Learn:
✔ IF Statements
✔ CASE Statements
✔ WINDOW_SUM()
✔ RANK()
✔ DATE Functions
📊 STEP 5: Learn Dashboard Design
✔ KPI Dashboards
✔ Storytelling with Data
✔ Interactive Reports
✔ Mobile-Friendly Dashboards
🛠 Design Skills:
✔ Layout Containers
✔ Dynamic Dashboards
✔ Navigation Buttons
☁️ STEP 6: Learn Tableau Server & Cloud
✔ Publishing Dashboards
✔ Sharing Reports
✔ Permissions & Security
✔ Scheduled Refresh
🛠 Platforms to Learn:
✔ Tableau Server
✔ Tableau Cloud
🔄 STEP 7: Learn Advanced Features
✔ Dashboard Optimization
✔ Row-Level Security
✔ Performance Tuning
✔ Advanced Analytics Integration
🛠 Advanced Skills:
✔ Python Integration
✔ R Integration
✔ Extensions & APIs
🔥 STEP 8: Build Real Tableau Projects
✔ Sales Dashboard
✔ HR Analytics Dashboard
✔ Financial Performance Dashboard
✔ Customer Segmentation Report
✔ Executive KPI Dashboard
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👉 Connect Data → Create Visuals → Build Dashboards → Share Insights
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| 15 | 📌 Formatting Visuals
Power BI allows you to customize visuals by:
Changing colors
Adding titles
Formatting labels
Showing data labels
Adjusting fonts
Sorting values
Applying themes
A consistent design improves readability.
📌 Best Practices
✅ Choose the right visual for the data.
✅ Keep dashboards simple and uncluttered.
✅ Use consistent colors throughout the report.
✅ Highlight important KPIs using Cards.
✅ Limit the number of visuals on a page.
✅ Add meaningful titles and labels.
❌ Common Mistakes
❌ Using too many colors.
❌ Overloading a page with visuals.
❌ Using pie charts with many categories.
❌ Ignoring proper labels and titles.
❌ Choosing the wrong chart type for the data.
💼 Real-World Example
A Sales Dashboard might include:
Card → Total Sales
Line Chart → Monthly Sales Trend
Bar Chart → Sales by Region
Treemap → Sales by Product Category
Map → Sales by Country
Matrix → Product-wise Sales Details
Gauge → Sales Target Achievement
This combination provides executives with a complete overview of business performance.
🎯 Interview Questions
1. What is Data Visualization?
2. Which chart is best for showing trends over time?
3. When would you use a Matrix instead of a Table?
4. What is the purpose of a Card visual?
5. When should you avoid using a Pie Chart?
6. What is the difference between a Bar Chart and a Column Chart?
7. Which visual is suitable for geographical analysis?
8. What are some best practices for designing Power BI dashboards?
📝 Key Takeaways
✅ Data Visualization turns raw data into actionable insights.
✅ Selecting the right visual makes reports easier to understand.
✅ Keep dashboards clean, simple, and focused on business goals.
✅ A good dashboard helps users identify trends, compare performance, and make informed decisions quickly.
🚀 Effective visualizations don't just display data—they tell a story that drives better business decisions.
Double Tap ❤️ For More | 1 166 |
| 16 | 🚀 Power BI Essentials Series
📊 Topic 5: Data Visualization (Complete Beginner's Guide)
Data is valuable only when people can understand it.
Power BI transforms raw data into interactive and meaningful visualizations, helping businesses make faster and better decisions.
🎯 What is Data Visualization?
Data Visualization is the process of presenting data using charts, graphs, maps, and other visuals to make information easy to understand.
Instead of reading thousands of rows of data, users can identify trends and insights at a glance.
📌 Why is Data Visualization Important?
A well-designed dashboard helps answer questions like:
Which region has the highest sales?
Which product generates the most revenue?
How are sales changing over time?
Who are the top-performing employees?
Which category has the highest profit?
Visualizations make it easier to identify patterns, trends, and outliers.
📂 Types of Visualizations in Power BI
📊 Bar Chart
Used to compare values across different categories.
Best For:
Sales by Region
Revenue by Product
Profit by Department
📈 Line Chart
Shows trends over time.
Best For:
Monthly Sales
Website Traffic
Stock Prices
Customer Growth
📉 Area Chart
Similar to a line chart but fills the area below the line.
Best For:
Showing overall growth over time
Comparing cumulative trends
🥧 Pie Chart
Displays each category's contribution to the whole.
Best For:
Market Share
Sales by Category
Customer Segments
Use pie charts only when there are a few categories.
🍩 Donut Chart
A variation of the pie chart with a hole in the center.
Useful for displaying percentages while leaving space for a KPI in the middle.
📋 Table
Displays detailed records in rows and columns.
Best For:
Transaction Details
Customer Lists
Invoice Data
📑 Matrix
An advanced version of a table that supports grouping and drill-down.
Best For:
Sales by Region and Product
Financial Reports
Pivot-style analysis
🔢 Card
Displays a single important value.
Examples:
Total Sales
Total Profit
Number of Customers
Revenue
Cards are commonly used for KPIs.
🎯 KPI Visual
Shows whether a metric is improving or declining against a target.
Examples:
Sales Target Achievement
Monthly Revenue
Profit Growth
🌍 Map
Displays geographic data.
Best For:
Sales by Country
Customers by State
Store Locations
📏 Gauge Chart
Shows progress toward a target.
Examples:
Sales Target
Budget Utilization
Project Completion
🌳 Treemap
Displays hierarchical data using rectangles.
Best For:
Sales by Category
Product Hierarchies
Budget Distribution
📌 Scatter Chart
Shows the relationship between two numerical variables.
Best For:
Profit vs Sales
Price vs Quantity
Customer Age vs Spending
📌 Choosing the Right Visual
Requirement | Recommended Visual
Compare categories | Bar Chart
Show trends | Line Chart
Display percentages | Pie/Donut Chart
Show KPIs | Card
Detailed data | Table
Hierarchical analysis | Matrix
Geographic analysis | Map
Compare two measures | Scatter Chart | 1 026 |
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| 18 | 📌 Understanding Filter Context
Filter Context is one of the most important DAX concepts.
If you place Total Sales in a report:
• Without filters → Total sales for all records.
• Filter by Region → Sales for that region only.
• Filter by Year → Sales for that year only.
The same measure returns different results based on the filters applied.
📌 Measure vs Calculated Column
Measure: Calculated on demand | Doesn't increase model size | Dynamic | Best for reports and KPIs
Calculated Column: Calculated during data refresh | Increases model size | Static until refresh | Best for row-level calculations
📌 Best Practices
✅ Prefer Measures over Calculated Columns whenever possible.
✅ Write meaningful measure names.
✅ Reuse existing measures instead of duplicating logic.
✅ Keep DAX formulas simple and readable.
✅ Test calculations with different filters.
❌ Common Mistakes
❌ Creating too many Calculated Columns.
❌ Writing duplicate measures.
❌ Ignoring filter context.
❌ Using complex formulas when a simple function would work.
❌ Not organizing measures into folders.
💼 Real-World Example
A retail company wants to track business performance.
Measures created: Total Sales, Total Profit, Total Orders, Average Order Value, Profit Margin %, Year-to-Date Sales, Previous Year Sales, Sales Growth %
These measures power KPI cards, charts, and executive dashboards.
🎯 Interview Questions
1. What is DAX?
2. What is the difference between a Measure and a Calculated Column?
3. What is CALCULATE() used for?
4. What is Filter Context?
5. Why is DIVIDE() preferred over the / operator?
6. What does RELATED() do?
7. When would you use a Calculated Table?
8. Name some commonly used DAX functions.
📝 Key Takeaways
✅ DAX is the calculation engine of Power BI.
✅ Measures are generally preferred because they are dynamic and efficient.
✅ Understanding Filter Context is essential for writing accurate DAX formulas.
✅ Master functions like SUM(), CALCULATE(), FILTER(), IF(), and DIVIDE() to build powerful dashboards.
🚀 Strong DAX skills transform a basic Power BI report into a powerful business intelligence solution.
Double Tap ❤️ For More | 1 471 |
| 19 | 🚀 Power BI Essentials Series
🧮 Topic 4: DAX (Data Analysis Expressions)
Once your data is cleaned and modeled, the next step is to perform calculations. This is where DAX comes in.
DAX is the formula language used in Power BI to create custom calculations, measures, calculated columns, and calculated tables.
🎯 What is DAX?
DAX is a collection of functions, operators, and constants that help you analyze and calculate data.
With DAX, you can:
✅ Calculate Total Sales
✅ Find Profit Margin
✅ Calculate Year-to-Date (YTD) Sales
✅ Compare Current vs Previous Year
✅ Rank Products
✅ Create KPIs
📌 Why is DAX Important?
Without DAX, Power BI can only display your existing data.
With DAX, you can answer business questions like:
• What are total sales this month?
• Which product generated the highest profit?
• How much did sales grow compared to last year?
• What is the average order value?
📂 Types of DAX Calculations
1️⃣ Measures
Measures are dynamic calculations.
They are calculated based on the current filter context.
Example:
Total Sales = SUM(Sales[Sales Amount])
Use Measures for: KPIs, Charts, Cards, Tables, Dashboards
Measures do not store values in the model, making them more efficient.
2️⃣ Calculated Columns
Calculated Columns create a new column in a table.
Example:
Profit = Sales[Sales Amount] - Sales[Cost]
Use them when you need a value for every row.
Unlike Measures, Calculated Columns increase the model size because values are stored.
3️⃣ Calculated Tables
Create entirely new tables using DAX.
Example:
TopCustomers = FILTER(Customers, Customers[Sales] > 100000)
Useful for advanced reporting scenarios.
📌 Most Common DAX Functions
SUM()
Adds all values in a column.
Total Sales = SUM(Sales[Sales Amount])
AVERAGE()
Returns the average value.
Average Sales = AVERAGE(Sales[Sales Amount])
COUNT()
Counts numeric values.
Total Orders = COUNT(Sales[Order ID])
DISTINCTCOUNT()
Counts unique values.
Example: Number of unique customers.
IF()
Performs logical tests.
Profit Status = IF([Profit] > 0, "Profit", "Loss")
CALCULATE()
One of the most powerful DAX functions.
It changes the filter context before performing a calculation.
North Sales =
CALCULATE(
[Total Sales],
Sales[Region] = "North"
)
FILTER()
Returns rows that meet a condition. Often used inside CALCULATE().
RELATED()
Fetches values from a related table. Useful when working with relationships.
DIVIDE()
Safely performs division and avoids divide-by-zero errors.
Profit Margin = DIVIDE([Profit], [Sales]) | 1 298 |
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