🔍 Advanced Power BI Interview Questions & Answers
1️⃣ What is Power BI Aggregations?
Aggregations improve performance by precomputing data at a higher level and storing it in memory. Power BI can automatically redirect queries to aggregated tables when possible.
2️⃣ Explain the concept of Composite Models.
Composite models allow combining Import and DirectQuery data sources in a single report, offering flexibility in performance and real-time access.
3️⃣ What is the difference between Power Query and Power Pivot?
⦁ Power Query: Used for data transformation and loading
⦁ Power Pivot: Used for data modeling and DAX calculations
4️⃣ What is the role of Tabular Model in Power BI?
Power BI uses the Tabular Model (based on SSAS) for in-memory analytics, enabling fast calculations and relationships.
5️⃣ How does Incremental Refresh work?
Incremental Refresh loads only new or changed data during scheduled refreshes, improving efficiency for large datasets.
6️⃣ What is the significance of the VertiPaq engine?
VertiPaq is the in-memory engine that compresses and stores data efficiently, enabling fast query performance in Power BI.
7️⃣ How do you implement dynamic noscripts in Power BI?
Use DAX measures and card visuals to create noscripts that change based on slicer selections or filters.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
⦁ USERNAME() returns the domain\username format
⦁ USERPRINCIPALNAME() returns the email format, preferred for cloud-based RLS
9️⃣ How do you handle circular dependency errors in DAX?
Avoid creating calculated columns/measures that reference each other recursively. Use variables and restructure logic to break the loop.
🔟 What is the use of CALCULATE in DAX?
CALCULATE modifies the context of a calculation by applying filters. It’s essential for dynamic aggregations.
Example:
1️⃣1️⃣ What are Aggregation Tables and when should you use them?
Aggregation tables store pre-summarized data to improve performance on large datasets. Use them when querying detailed data is too slow.
1️⃣2️⃣ How do you implement Role-Level Security (RLS) with dynamic filters?
Create a user table with email addresses and region mappings, then use DAX with USERPRINCIPALNAME() to filter data dynamically.
1️⃣3️⃣ What is the difference between SUM and SUMX in DAX?
⦁ SUM: Adds values from a column
⦁ SUMX: Iterates over a table and evaluates an expression row by row
1️⃣4️⃣ What are Parameters in Power BI and how are they used?
Parameters allow dynamic input into queries or filters. Useful for what-if analysis, dynamic data sources, or user-driven filtering.
1️⃣5️⃣ How do you use Field Parameters in Power BI?
Field Parameters let users dynamically switch dimensions or measures in visuals using slicers—great for interactive dashboards.
1️⃣6️⃣ What is the purpose of the Performance Analyzer in Power BI?
It helps identify slow visuals and DAX queries by showing render times, query durations, and bottlenecks.
1️⃣7️⃣ How do you handle many-to-many relationships in Power BI?
Use a bridge table with unique keys and set relationships as “many-to-one” on both sides, or use DAX functions like TREATAS().
1️⃣8️⃣ What is the difference between SELECTEDVALUE and VALUES in DAX?
⦁ SELECTEDVALUE: Returns a single value if only one is selected, otherwise returns blank or a default
⦁ VALUES: Returns a table of distinct values
1️⃣9️⃣ How do you create a paginated report in Power BI?
Use Power BI Report Builder to design pixel-perfect reports ideal for printing or exporting, especially with large tables.
2️⃣0️⃣ What are the limitations of DirectQuery mode?
⦁ Slower performance due to live queries
⦁ Limited DAX functions
⦁ No support for certain transformations
⦁ Dependency on source system availability
Double Tap ❤️ For More
1️⃣ What is Power BI Aggregations?
Aggregations improve performance by precomputing data at a higher level and storing it in memory. Power BI can automatically redirect queries to aggregated tables when possible.
2️⃣ Explain the concept of Composite Models.
Composite models allow combining Import and DirectQuery data sources in a single report, offering flexibility in performance and real-time access.
3️⃣ What is the difference between Power Query and Power Pivot?
⦁ Power Query: Used for data transformation and loading
⦁ Power Pivot: Used for data modeling and DAX calculations
4️⃣ What is the role of Tabular Model in Power BI?
Power BI uses the Tabular Model (based on SSAS) for in-memory analytics, enabling fast calculations and relationships.
5️⃣ How does Incremental Refresh work?
Incremental Refresh loads only new or changed data during scheduled refreshes, improving efficiency for large datasets.
6️⃣ What is the significance of the VertiPaq engine?
VertiPaq is the in-memory engine that compresses and stores data efficiently, enabling fast query performance in Power BI.
7️⃣ How do you implement dynamic noscripts in Power BI?
Use DAX measures and card visuals to create noscripts that change based on slicer selections or filters.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
⦁ USERNAME() returns the domain\username format
⦁ USERPRINCIPALNAME() returns the email format, preferred for cloud-based RLS
9️⃣ How do you handle circular dependency errors in DAX?
Avoid creating calculated columns/measures that reference each other recursively. Use variables and restructure logic to break the loop.
🔟 What is the use of CALCULATE in DAX?
CALCULATE modifies the context of a calculation by applying filters. It’s essential for dynamic aggregations.
Example:
Sales West = CALCULATE(SUM(Sales[Amount]), Region = "West")1️⃣1️⃣ What are Aggregation Tables and when should you use them?
Aggregation tables store pre-summarized data to improve performance on large datasets. Use them when querying detailed data is too slow.
1️⃣2️⃣ How do you implement Role-Level Security (RLS) with dynamic filters?
Create a user table with email addresses and region mappings, then use DAX with USERPRINCIPALNAME() to filter data dynamically.
1️⃣3️⃣ What is the difference between SUM and SUMX in DAX?
⦁ SUM: Adds values from a column
⦁ SUMX: Iterates over a table and evaluates an expression row by row
1️⃣4️⃣ What are Parameters in Power BI and how are they used?
Parameters allow dynamic input into queries or filters. Useful for what-if analysis, dynamic data sources, or user-driven filtering.
1️⃣5️⃣ How do you use Field Parameters in Power BI?
Field Parameters let users dynamically switch dimensions or measures in visuals using slicers—great for interactive dashboards.
1️⃣6️⃣ What is the purpose of the Performance Analyzer in Power BI?
It helps identify slow visuals and DAX queries by showing render times, query durations, and bottlenecks.
1️⃣7️⃣ How do you handle many-to-many relationships in Power BI?
Use a bridge table with unique keys and set relationships as “many-to-one” on both sides, or use DAX functions like TREATAS().
1️⃣8️⃣ What is the difference between SELECTEDVALUE and VALUES in DAX?
⦁ SELECTEDVALUE: Returns a single value if only one is selected, otherwise returns blank or a default
⦁ VALUES: Returns a table of distinct values
1️⃣9️⃣ How do you create a paginated report in Power BI?
Use Power BI Report Builder to design pixel-perfect reports ideal for printing or exporting, especially with large tables.
2️⃣0️⃣ What are the limitations of DirectQuery mode?
⦁ Slower performance due to live queries
⦁ Limited DAX functions
⦁ No support for certain transformations
⦁ Dependency on source system availability
Double Tap ❤️ For More
❤8
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.
Here you can find some free Excel books & useful resources: https://news.1rj.ru/str/excel_data
Hope it helps :)
#dataanalyst
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.
Here you can find some free Excel books & useful resources: https://news.1rj.ru/str/excel_data
Hope it helps :)
#dataanalyst
❤5
📈 Data Visualisation Cheatsheet: 13 Must-Know Chart Types ✅
1️⃣ Gantt Chart
Tracks project schedules over time.
🔹 Advantage: Clarifies timelines & tasks
🔹 Use case: Project management & planning
2️⃣ Bubble Chart
Shows data with bubble size variations.
🔹 Advantage: Displays 3 data dimensions
🔹 Use case: Comparing social media engagement
3️⃣ Scatter Plots
Plots data points on two axes.
🔹 Advantage: Identifies correlations & clusters
🔹 Use case: Analyzing variable relationships
4️⃣ Histogram Chart
Visualizes data distribution in bins.
🔹 Advantage: Easy to see frequency
🔹 Use case: Understanding age distribution in surveys
5️⃣ Bar Chart
Uses rectangular bars to visualize data.
🔹 Advantage: Easy comparison across groups
🔹 Use case: Comparing sales across regions
6️⃣ Line Chart
Shows trends over time with lines.
🔹 Advantage: Clear display of data changes
🔹 Use case: Tracking stock market performance
7️⃣ Pie Chart
Represents data in circular segments.
🔹 Advantage: Simple proportion visualization
🔹 Use case: Displaying market share distribution
8️⃣ Maps
Geographic data representation on maps.
🔹 Advantage: Recognizes spatial patterns
🔹 Use case: Visualizing population density by area
9️⃣ Bullet Charts
Measures performance against a target.
🔹 Advantage: Compact alternative to gauges
🔹 Use case: Tracking sales vs quotas
🔟 Highlight Table
Colors tabular data based on values.
🔹 Advantage: Quickly identifies highs & lows
🔹 Use case: Heatmapping survey responses
1️⃣1️⃣ Tree Maps
Hierarchical data with nested rectangles.
🔹 Advantage: Efficient space usage
🔹 Use case: Displaying file system usage
1️⃣2️⃣ Box & Whisker Plot
Summarizes data distribution & outliers.
🔹 Advantage: Concise data spread representation
🔹 Use case: Comparing exam scores across classes
1️⃣3️⃣ Waterfall Charts / Walks
Visualizes sequential cumulative effect.
🔹 Advantage: Clarifies source of final value
🔹 Use case: Understanding profit & loss components
💡 Use the right chart to tell your data story clearly.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Tap ♥️ for more!
1️⃣ Gantt Chart
Tracks project schedules over time.
🔹 Advantage: Clarifies timelines & tasks
🔹 Use case: Project management & planning
2️⃣ Bubble Chart
Shows data with bubble size variations.
🔹 Advantage: Displays 3 data dimensions
🔹 Use case: Comparing social media engagement
3️⃣ Scatter Plots
Plots data points on two axes.
🔹 Advantage: Identifies correlations & clusters
🔹 Use case: Analyzing variable relationships
4️⃣ Histogram Chart
Visualizes data distribution in bins.
🔹 Advantage: Easy to see frequency
🔹 Use case: Understanding age distribution in surveys
5️⃣ Bar Chart
Uses rectangular bars to visualize data.
🔹 Advantage: Easy comparison across groups
🔹 Use case: Comparing sales across regions
6️⃣ Line Chart
Shows trends over time with lines.
🔹 Advantage: Clear display of data changes
🔹 Use case: Tracking stock market performance
7️⃣ Pie Chart
Represents data in circular segments.
🔹 Advantage: Simple proportion visualization
🔹 Use case: Displaying market share distribution
8️⃣ Maps
Geographic data representation on maps.
🔹 Advantage: Recognizes spatial patterns
🔹 Use case: Visualizing population density by area
9️⃣ Bullet Charts
Measures performance against a target.
🔹 Advantage: Compact alternative to gauges
🔹 Use case: Tracking sales vs quotas
🔟 Highlight Table
Colors tabular data based on values.
🔹 Advantage: Quickly identifies highs & lows
🔹 Use case: Heatmapping survey responses
1️⃣1️⃣ Tree Maps
Hierarchical data with nested rectangles.
🔹 Advantage: Efficient space usage
🔹 Use case: Displaying file system usage
1️⃣2️⃣ Box & Whisker Plot
Summarizes data distribution & outliers.
🔹 Advantage: Concise data spread representation
🔹 Use case: Comparing exam scores across classes
1️⃣3️⃣ Waterfall Charts / Walks
Visualizes sequential cumulative effect.
🔹 Advantage: Clarifies source of final value
🔹 Use case: Understanding profit & loss components
💡 Use the right chart to tell your data story clearly.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Tap ♥️ for more!
❤10
✅ Step-by-Step: Create an HR Analytics Dashboard in Power BI 👩💼📊
🔰 Objective: Track employee headcount, attrition, hiring trends, department-wise breakdowns, and key HR KPIs.
Step 1: Gather & Prepare Data
✔ Collect HR data from Excel/CSV:
⦁ Employee ID, Name
⦁ Department, Gender, Age
⦁ Date of Joining, Resignation Date
⦁ Status (Active/Resigned)
⦁ Monthly Salary
💡 Optional: Use mock data from Mockaroo or Kaggle datasets like the HR Analytics sample.
Step 2: Load Data into Power BI
⦁ Open Power BI Desktop
⦁ Click Get Data → Choose Excel/CSV
⦁ Load your employee dataset
Step 3: Clean & Transform Data (Power Query)
⦁ Format columns (Date, Text, Number)
⦁ Create new columns:
🔸 Tenure = Today() - Date of Joining
🔸 Attrition = IF(Status = "Resigned", 1, 0)
⦁ Remove duplicates, fix nulls
Step 4: Create Measures (DAX)
🔹 Headcount = COUNTROWS(FILTER(EmployeeData, EmployeeData[Status] = "Active"))
🔹 Attrition Rate = DIVIDE(CALCULATE(COUNT(EmployeeData[Attrition]), EmployeeData[Attrition] = 1), [Headcount])
🔹 Average Tenure = AVERAGE(EmployeeData[Tenure])
Step 5: Design the Dashboard
Use visuals like:
⦁ Cards → Headcount, Attrition Rate, Avg Tenure
⦁ Bar Charts → Department-wise headcount
⦁ Pie/Donut → Gender distribution
⦁ Line Chart → Monthly hiring & attrition
⦁ Slicers → Department, Gender, Experience level
🎨 Tip: Use consistent colors for departments/genders
Step 6: Add Interactivity
✔ Use slicers to filter visuals
✔ Enable Drillthrough for department-level deep dive
✔ Use Tooltips to show extra details on hover
Step 7: Publish & Share
⦁ Save and Publish to Power BI Service
⦁ Set up refresh schedule (if needed)
⦁ Share dashboard link with HR/Management
💬 Tap ❤️ for more!
🔰 Objective: Track employee headcount, attrition, hiring trends, department-wise breakdowns, and key HR KPIs.
Step 1: Gather & Prepare Data
✔ Collect HR data from Excel/CSV:
⦁ Employee ID, Name
⦁ Department, Gender, Age
⦁ Date of Joining, Resignation Date
⦁ Status (Active/Resigned)
⦁ Monthly Salary
💡 Optional: Use mock data from Mockaroo or Kaggle datasets like the HR Analytics sample.
Step 2: Load Data into Power BI
⦁ Open Power BI Desktop
⦁ Click Get Data → Choose Excel/CSV
⦁ Load your employee dataset
Step 3: Clean & Transform Data (Power Query)
⦁ Format columns (Date, Text, Number)
⦁ Create new columns:
🔸 Tenure = Today() - Date of Joining
🔸 Attrition = IF(Status = "Resigned", 1, 0)
⦁ Remove duplicates, fix nulls
Step 4: Create Measures (DAX)
🔹 Headcount = COUNTROWS(FILTER(EmployeeData, EmployeeData[Status] = "Active"))
🔹 Attrition Rate = DIVIDE(CALCULATE(COUNT(EmployeeData[Attrition]), EmployeeData[Attrition] = 1), [Headcount])
🔹 Average Tenure = AVERAGE(EmployeeData[Tenure])
Step 5: Design the Dashboard
Use visuals like:
⦁ Cards → Headcount, Attrition Rate, Avg Tenure
⦁ Bar Charts → Department-wise headcount
⦁ Pie/Donut → Gender distribution
⦁ Line Chart → Monthly hiring & attrition
⦁ Slicers → Department, Gender, Experience level
🎨 Tip: Use consistent colors for departments/genders
Step 6: Add Interactivity
✔ Use slicers to filter visuals
✔ Enable Drillthrough for department-level deep dive
✔ Use Tooltips to show extra details on hover
Step 7: Publish & Share
⦁ Save and Publish to Power BI Service
⦁ Set up refresh schedule (if needed)
⦁ Share dashboard link with HR/Management
💬 Tap ❤️ for more!
❤14
✅ Top 10 Power BI Interview Tips (2025) 📊🧠
1) Master the Data Model
Understand star vs snowflake schemas. Use relationships properly. Avoid bi-directional filters unless needed.
2) Use DAX with Confidence
Know how to write measures using CALCULATE, FILTER, ALL, VALUES, and time intelligence functions like YTD, MTD.
3) Practice Real Dashboards
Create projects like Sales, HR, or Finance dashboards using slicers, KPIs, and bookmarks.
4) Know the Visuals
Explain when to use bar, line, pie, matrix, and cards. Justify your choices with business logic.
5) Optimize Performance
Use fewer visuals, limit columns, and use summary tables. Avoid heavy calculated columns when a measure works.
6) Understand Power Query (M)
You may be asked to clean messy data—know how to remove duplicates, unpivot columns, or split data.
7) Explain Row-Level Security (RLS)
Be ready to show how to restrict access based on roles like region or department.
8) Showcase Time Intelligence
Know how to use a proper date table and build dynamic measures like QoQ or YoY growth.
9) Practice Common Use Cases
Be able to analyze sales trends, churn, forecasts, or customer segmentation.
10) Share Your Portfolio
Build and share your dashboards on LinkedIn or GitHub with proper business explanations.
💬 Tap ❤️ for more!
1) Master the Data Model
Understand star vs snowflake schemas. Use relationships properly. Avoid bi-directional filters unless needed.
2) Use DAX with Confidence
Know how to write measures using CALCULATE, FILTER, ALL, VALUES, and time intelligence functions like YTD, MTD.
3) Practice Real Dashboards
Create projects like Sales, HR, or Finance dashboards using slicers, KPIs, and bookmarks.
4) Know the Visuals
Explain when to use bar, line, pie, matrix, and cards. Justify your choices with business logic.
5) Optimize Performance
Use fewer visuals, limit columns, and use summary tables. Avoid heavy calculated columns when a measure works.
6) Understand Power Query (M)
You may be asked to clean messy data—know how to remove duplicates, unpivot columns, or split data.
7) Explain Row-Level Security (RLS)
Be ready to show how to restrict access based on roles like region or department.
8) Showcase Time Intelligence
Know how to use a proper date table and build dynamic measures like QoQ or YoY growth.
9) Practice Common Use Cases
Be able to analyze sales trends, churn, forecasts, or customer segmentation.
10) Share Your Portfolio
Build and share your dashboards on LinkedIn or GitHub with proper business explanations.
💬 Tap ❤️ for more!
❤3🔥1
✅ Power BI Interview Mini-Challenge! 📊
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: Create a DAX measure to calculate total sales by region, including regions with zero sales.
𝗠𝗲: Here’s my DAX solution using SUMX and CROSSJOIN for complete coverage:
✔ Why it works:
– CROSSJOIN generates all region combinations to include zeros.
– SUMX iterates for accurate totals per region.
– Handles sparse data perfectly for visuals like bar charts!
🔎 Bonus Insight:
Master DAX iterators like SUMX vs. SUM— they shine in complex scenarios. In Power BI, always test measures in reports to catch edge cases with filters.
💬 Tap ❤️ if this helped you!
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: Create a DAX measure to calculate total sales by region, including regions with zero sales.
𝗠𝗲: Here’s my DAX solution using SUMX and CROSSJOIN for complete coverage:
Total Sales by Region =
SUMX(
CROSSJOIN(VALUES(Regions[RegionName]), VALUES(Sales[Date])),
CALCULATE(SUM(Sales[Amount]),
FILTER(Sales, Sales[RegionName] = EARLIER(Regions[RegionName]))
)
)
✔ Why it works:
– CROSSJOIN generates all region combinations to include zeros.
– SUMX iterates for accurate totals per region.
– Handles sparse data perfectly for visuals like bar charts!
🔎 Bonus Insight:
Master DAX iterators like SUMX vs. SUM— they shine in complex scenarios. In Power BI, always test measures in reports to catch edge cases with filters.
💬 Tap ❤️ if this helped you!
❤9
✅ Power BI Scenario-Based Question & Answer 📊💻
Scenario:
You have a sales dataset with multiple regions and want to show total sales per region. Some regions have no sales data.
Question:
Create a report that shows total sales per region. If sales are missing, show it as 0.
Answer:
1️⃣ Load your dataset into Power BI (assuming table name is SalesData).
2️⃣ Create this DAX measure in the Modeling tab:
3️⃣ Add a Table or Bar Chart visual:
⦁ Axis/Rows: Region
⦁ Values: Total Sales by Region
Explanation:
⦁ COALESCE returns the first non-blank value (Sales if available, else 0).
⦁ SUMX iterates row-by-row to handle NULLs properly and aggregates per region.
⦁ This ensures all regions appear, even with zero sales, for complete reporting.
Result:
💬 Tap ❤️ for more!
Scenario:
You have a sales dataset with multiple regions and want to show total sales per region. Some regions have no sales data.
| Region | Sales |
| ------ | ----- |
| North | 10000 |
| South | 8000 |
| East | NULL |
| West | 12000 |
Question:
Create a report that shows total sales per region. If sales are missing, show it as 0.
Answer:
1️⃣ Load your dataset into Power BI (assuming table name is SalesData).
2️⃣ Create this DAX measure in the Modeling tab:
Total Sales by Region =
SUMX(
SalesData,
COALESCE(SalesData[Sales], 0)
)
3️⃣ Add a Table or Bar Chart visual:
⦁ Axis/Rows: Region
⦁ Values: Total Sales by Region
Explanation:
⦁ COALESCE returns the first non-blank value (Sales if available, else 0).
⦁ SUMX iterates row-by-row to handle NULLs properly and aggregates per region.
⦁ This ensures all regions appear, even with zero sales, for complete reporting.
Result:
| Region | Total Sales |
| ------ | ----------- |
| North | 10000 |
| South | 8000 |
| East | 0 |
| West | 12000 |
💬 Tap ❤️ for more!
❤17
🧑💼 Interviewer: What's the difference between RANKX() and DENSE_RANKX() in Power BI?
👨💻 Me: Here's a quick example using sales data in DAX—assuming a table with salespeople and their totals:
✔ Key Differences:
– RANKX(): Skips ranks on ties (e.g., two at #1, next is #3)—default behavior for competitions or where gaps reflect position.
– DENSE_RANKX(): No skips—consecutive ranks (e.g., two at #1, next is #2)—use the
📌 Example:
If two salespeople tie at top sales:
⦁ RANKX() → 1, 1, 3
⦁ DENSE_RANKX() → 1, 1, 2
💡 Use DENSE_RANKX() for consistent tiers in reports, like regional sales rankings—add filters or ALL() for context!
💬 Tap ❤️ for more!
👨💻 Me: Here's a quick example using sales data in DAX—assuming a table with salespeople and their totals:
Sales Rank =
RANKX(
ALL(Sales[Salesperson]),
SUM(Sales[Amount]),,
DESC
)
Dense Sales Rank =
RANKX(
ALL(Sales[Salesperson]),
SUM(Sales[Amount]),,
DESC,
Dense
)
✔ Key Differences:
– RANKX(): Skips ranks on ties (e.g., two at #1, next is #3)—default behavior for competitions or where gaps reflect position.
– DENSE_RANKX(): No skips—consecutive ranks (e.g., two at #1, next is #2)—use the
Dense tiebreaker for seamless leaderboards.📌 Example:
If two salespeople tie at top sales:
⦁ RANKX() → 1, 1, 3
⦁ DENSE_RANKX() → 1, 1, 2
💡 Use DENSE_RANKX() for consistent tiers in reports, like regional sales rankings—add filters or ALL() for context!
💬 Tap ❤️ for more!
❤3
✅8-Week Beginner Roadmap to Learn Data Analysis 📊
🗓️ Week 1: Excel & Data Basics
Goal: Master data organization and analysis basics
Topics: Excel formulas, functions, PivotTables, data cleaning
Tools: Microsoft Excel, Google Sheets
Mini Project: Analyze sales or survey data with PivotTables
🗓️ Week 2: SQL Fundamentals
Goal: Learn to query databases efficiently
Topics: SELECT, WHERE, JOIN, GROUP BY, subqueries
Tools: MySQL, PostgreSQL, SQLite
Mini Project: Query sample customer or sales database
🗓️ Week 3: Data Visualization Basics
Goal: Create meaningful charts and graphs
Topics: Bar charts, line charts, scatter plots, dashboards
Tools: Tableau, Power BI, Excel charts
Mini Project: Build dashboard to analyze sales trends
🗓️ Week 4: Data Cleaning & Preparation
Goal: Handle messy data for analysis
Topics: Handling missing values, duplicates, data types
Tools: Excel, Python (Pandas) basics
Mini Project: Clean and prepare real-world dataset for analysis
🗓️ Week 5: Statistics for Data Analysis
Goal: Understand key statistical concepts
Topics: Denoscriptive stats, distributions, correlation, hypothesis testing
Tools: Excel, Python (SciPy, NumPy)
Mini Project: Analyze survey data & draw insights
🗓️ Week 6: Advanced SQL & Database Concepts
Goal: Optimize queries & explore database design basics
Topics: Window functions, indexes, normalization
Tools: SQL Server, MySQL
Mini Project: Complex query for sales and customer analysis
🗓️ Week 7: Automating Analysis with Python
Goal: Use Python for repetitive data tasks
Topics: Pandas automation, data aggregation, visualization noscripting
Tools: Jupyter Notebook, Pandas, Matplotlib
Mini Project: Automate monthly sales report generation
🗓️ Week 8: Capstone Project + Reporting
Goal: End-to-end analysis and presentation
Project Ideas: Customer segmentation, sales forecasting, churn analysis
Tools: Tableau/Power BI for visualization + Python/SQL for backend
Bonus: Present findings in a polished report or dashboard
💡 Tips:
⦁ Practice querying and analysis on public datasets (Kaggle, data.gov)
⦁ Join data challenges and community projects
💬 Tap ❤️ for the detailed explanation of each topic!
🗓️ Week 1: Excel & Data Basics
Goal: Master data organization and analysis basics
Topics: Excel formulas, functions, PivotTables, data cleaning
Tools: Microsoft Excel, Google Sheets
Mini Project: Analyze sales or survey data with PivotTables
🗓️ Week 2: SQL Fundamentals
Goal: Learn to query databases efficiently
Topics: SELECT, WHERE, JOIN, GROUP BY, subqueries
Tools: MySQL, PostgreSQL, SQLite
Mini Project: Query sample customer or sales database
🗓️ Week 3: Data Visualization Basics
Goal: Create meaningful charts and graphs
Topics: Bar charts, line charts, scatter plots, dashboards
Tools: Tableau, Power BI, Excel charts
Mini Project: Build dashboard to analyze sales trends
🗓️ Week 4: Data Cleaning & Preparation
Goal: Handle messy data for analysis
Topics: Handling missing values, duplicates, data types
Tools: Excel, Python (Pandas) basics
Mini Project: Clean and prepare real-world dataset for analysis
🗓️ Week 5: Statistics for Data Analysis
Goal: Understand key statistical concepts
Topics: Denoscriptive stats, distributions, correlation, hypothesis testing
Tools: Excel, Python (SciPy, NumPy)
Mini Project: Analyze survey data & draw insights
🗓️ Week 6: Advanced SQL & Database Concepts
Goal: Optimize queries & explore database design basics
Topics: Window functions, indexes, normalization
Tools: SQL Server, MySQL
Mini Project: Complex query for sales and customer analysis
🗓️ Week 7: Automating Analysis with Python
Goal: Use Python for repetitive data tasks
Topics: Pandas automation, data aggregation, visualization noscripting
Tools: Jupyter Notebook, Pandas, Matplotlib
Mini Project: Automate monthly sales report generation
🗓️ Week 8: Capstone Project + Reporting
Goal: End-to-end analysis and presentation
Project Ideas: Customer segmentation, sales forecasting, churn analysis
Tools: Tableau/Power BI for visualization + Python/SQL for backend
Bonus: Present findings in a polished report or dashboard
💡 Tips:
⦁ Practice querying and analysis on public datasets (Kaggle, data.gov)
⦁ Join data challenges and community projects
💬 Tap ❤️ for the detailed explanation of each topic!
❤16
✅ Complete Roadmap to Learn Power BI in 2025-26 📊🚀
Phase 1: Power BI Basics (1-2 Weeks)
🔹 Understand Power BI Desktop interface
🔹 Learn to connect to different data sources (Excel, CSV, databases)
🔹 Import & transform data using Power Query Editor
Phase 2: Data Modeling (1-2 Weeks)
🔹 Create relationships between tables
🔹 Understand star and snowflake schemas
🔹 Learn DAX basics — calculated columns, measures, basic functions
Phase 3: Data Visualization (2-3 Weeks)
🔹 Build reports using charts, tables, maps, slicers
🔹 Customize visuals and format reports
🔹 Use bookmarks and drill-through features
Phase 4: Advanced DAX & Analytics (2-3 Weeks)
🔹 Master advanced DAX functions (time intelligence, filter functions)
🔹 Create dynamic reports with variables and context
🔹 Use What-If parameters and forecasting
Phase 5: Power BI Service & Sharing (1-2 Weeks)
🔹 Publish reports to Power BI Service
🔹 Set up dashboards and workspaces
🔹 Share reports and collaborate with teams
🔹 Schedule data refresh and manage permissions
Phase 6: Integration & Automation (Optional)
🔹 Integrate Power BI with Excel, Teams, and SharePoint
🔹 Automate workflows using Power Automate
Phase 7: Real-World Projects & Certification
🔹 Build dashboards from real datasets
🔹 Prepare for Microsoft Power BI certification (DA-100 / PL-300)
💬 Tap ❤️ for the detailed explanation!
Phase 1: Power BI Basics (1-2 Weeks)
🔹 Understand Power BI Desktop interface
🔹 Learn to connect to different data sources (Excel, CSV, databases)
🔹 Import & transform data using Power Query Editor
Phase 2: Data Modeling (1-2 Weeks)
🔹 Create relationships between tables
🔹 Understand star and snowflake schemas
🔹 Learn DAX basics — calculated columns, measures, basic functions
Phase 3: Data Visualization (2-3 Weeks)
🔹 Build reports using charts, tables, maps, slicers
🔹 Customize visuals and format reports
🔹 Use bookmarks and drill-through features
Phase 4: Advanced DAX & Analytics (2-3 Weeks)
🔹 Master advanced DAX functions (time intelligence, filter functions)
🔹 Create dynamic reports with variables and context
🔹 Use What-If parameters and forecasting
Phase 5: Power BI Service & Sharing (1-2 Weeks)
🔹 Publish reports to Power BI Service
🔹 Set up dashboards and workspaces
🔹 Share reports and collaborate with teams
🔹 Schedule data refresh and manage permissions
Phase 6: Integration & Automation (Optional)
🔹 Integrate Power BI with Excel, Teams, and SharePoint
🔹 Automate workflows using Power Automate
Phase 7: Real-World Projects & Certification
🔹 Build dashboards from real datasets
🔹 Prepare for Microsoft Power BI certification (DA-100 / PL-300)
💬 Tap ❤️ for the detailed explanation!
❤23🔥1
If I need to teach someone data analytics from the basics, here is my strategy:
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
❤14
The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
❤3
Tableau Cheat Sheet ✅
This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.
1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).
2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.
3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.
4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.
5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.
6. Calculated Fields
- Create calculated fields to derive new data:
- Example:
7. Parameters
- Use parameters to allow user input and control measures dynamically.
8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.
9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.
10. Story Points
- Create a story to guide users through insights with narrative and visualizations.
11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.
12. Export Options
- Export to PDF or image for offline use.
13. Keyboard Shortcuts
- Show/Hide Sidebar:
- Duplicate Sheet:
- Undo:
- Redo:
14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.
1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).
2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.
3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.
4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.
5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.
6. Calculated Fields
- Create calculated fields to derive new data:
- Example:
Sales Growth = SUM([Sales]) - SUM([Previous Sales])7. Parameters
- Use parameters to allow user input and control measures dynamically.
8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.
9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.
10. Story Points
- Create a story to guide users through insights with narrative and visualizations.
11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.
12. Export Options
- Export to PDF or image for offline use.
13. Keyboard Shortcuts
- Show/Hide Sidebar:
Ctrl+Alt+T- Duplicate Sheet:
Ctrl + D- Undo:
Ctrl + Z- Redo:
Ctrl + Y14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤8
✅ Top Data Analyst Interview Q&A 🎯
1. How do you handle messy or incomplete data in a real project
Answer:
I start by profiling the dataset to identify missing values, duplicates, and inconsistent formats. Depending on the context, I may impute missing values using mean/median, flag them for review, or exclude them if they’re not critical. For example, in an HR dataset, I used pandas to standardize date formats and fill missing department fields based on role noscripts.
2. Describe a time you built a dashboard that influenced a business decision
Answer:
At my previous role, I built a Power BI dashboard to track churn across customer segments. It revealed that users from a specific region had a 30% higher churn rate. This insight led the marketing team to launch a targeted retention campaign, reducing churn by 12% in the next quarter.
3. How do you approach a vague business question like “Why are sales dropping”
Answer:
I break it down by segmenting data—region, product, time period—and look for anomalies or trends. I compare current vs. previous periods, analyze customer behavior, and check for external factors. In one case, I discovered that a drop in sales was due to a discontinued product line that hadn’t been flagged in reporting.
4. What’s your process for analyzing an A/B test
Answer:
I define the hypothesis, ensure randomization, and check sample sizes. Then I compare metrics like conversion rate between control and test groups using statistical tests (e.g., t-test or chi-square). I also calculate p-values and confidence intervals to determine significance. I once helped a product team validate a new checkout flow that increased conversions by 8%.
5. How do you ensure your analysis is understandable to non-technical stakeholders
Answer:
I focus on clarity—use simple language, clean visuals, and highlight key takeaways. I avoid jargon and always tie insights to business impact. For example, instead of saying “standard deviation,” I might say “variation in customer spending.”
6. What tools do you use for forecasting and how do you validate your predictions
Answer:
I use Excel for quick models and Python’s statsmodels or Prophet for more robust forecasting. I validate predictions using historical data and metrics like RMSE or MAPE. In a recent project, I forecasted monthly sales and helped the inventory team reduce overstock by 15%.
7. How do you automate repetitive reporting tasks
Answer:
I use Python noscripts with scheduled jobs or Power BI’s refresh features. In one case, I automated a weekly sales report using Google Sheets + Apps Script, saving 5 hours of manual work per week.
8. How do you prioritize multiple data requests from different teams
Answer:
I assess urgency, business impact, and effort required. I communicate clearly with stakeholders and use frameworks like ICE (Impact, Confidence, Effort) to align priorities. I also maintain a request tracker to manage expectations.
Double Tap ♥️ For More
1. How do you handle messy or incomplete data in a real project
Answer:
I start by profiling the dataset to identify missing values, duplicates, and inconsistent formats. Depending on the context, I may impute missing values using mean/median, flag them for review, or exclude them if they’re not critical. For example, in an HR dataset, I used pandas to standardize date formats and fill missing department fields based on role noscripts.
2. Describe a time you built a dashboard that influenced a business decision
Answer:
At my previous role, I built a Power BI dashboard to track churn across customer segments. It revealed that users from a specific region had a 30% higher churn rate. This insight led the marketing team to launch a targeted retention campaign, reducing churn by 12% in the next quarter.
3. How do you approach a vague business question like “Why are sales dropping”
Answer:
I break it down by segmenting data—region, product, time period—and look for anomalies or trends. I compare current vs. previous periods, analyze customer behavior, and check for external factors. In one case, I discovered that a drop in sales was due to a discontinued product line that hadn’t been flagged in reporting.
4. What’s your process for analyzing an A/B test
Answer:
I define the hypothesis, ensure randomization, and check sample sizes. Then I compare metrics like conversion rate between control and test groups using statistical tests (e.g., t-test or chi-square). I also calculate p-values and confidence intervals to determine significance. I once helped a product team validate a new checkout flow that increased conversions by 8%.
5. How do you ensure your analysis is understandable to non-technical stakeholders
Answer:
I focus on clarity—use simple language, clean visuals, and highlight key takeaways. I avoid jargon and always tie insights to business impact. For example, instead of saying “standard deviation,” I might say “variation in customer spending.”
6. What tools do you use for forecasting and how do you validate your predictions
Answer:
I use Excel for quick models and Python’s statsmodels or Prophet for more robust forecasting. I validate predictions using historical data and metrics like RMSE or MAPE. In a recent project, I forecasted monthly sales and helped the inventory team reduce overstock by 15%.
7. How do you automate repetitive reporting tasks
Answer:
I use Python noscripts with scheduled jobs or Power BI’s refresh features. In one case, I automated a weekly sales report using Google Sheets + Apps Script, saving 5 hours of manual work per week.
8. How do you prioritize multiple data requests from different teams
Answer:
I assess urgency, business impact, and effort required. I communicate clearly with stakeholders and use frameworks like ICE (Impact, Confidence, Effort) to align priorities. I also maintain a request tracker to manage expectations.
Double Tap ♥️ For More
❤8
Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of trannoscriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation ennoscriptd “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.
And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.
The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of trannoscriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation ennoscriptd “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.
And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.
The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
❤4
Free Resources to learn Power BI
👇👇
Microsoft Certified: Power BI Data Analyst Associate
https://learn.microsoft.com/en-us/certifications/power-bi-data-analyst-associate/?azure-portal=true
Visualize data in Power BI
https://learn.microsoft.com/en-us/training/paths/visualize-data-power-bi/?ns-enrollment-type=Collection&ns-enrollment-id=djwu3eywpk4name
Data analysis in Power BI
https://learn.microsoft.com/en-us/training/paths/perform-analytics-power-bi/?ns-enrollment-type=Collection&ns-enrollment-id=djwu3eywpk4nm
Learn Power BI Basics for Free
https://bit.ly/3sTa6Gj
Power BI Free Book
https://download.microsoft.com/download/0/8/1/0816F8D1-D1A5-4F60-9AF5-BC91E18D6D64/Microsoft_Press_ebook_Introducing_Power_BI_PDF_mobile.pdf
Power BI Dashboard Step by Step FREE UDEMY COURSE
https://bit.ly/3dho1Sleep
ENJOY LEARNING 👍👍
👇👇
Microsoft Certified: Power BI Data Analyst Associate
https://learn.microsoft.com/en-us/certifications/power-bi-data-analyst-associate/?azure-portal=true
Visualize data in Power BI
https://learn.microsoft.com/en-us/training/paths/visualize-data-power-bi/?ns-enrollment-type=Collection&ns-enrollment-id=djwu3eywpk4name
Data analysis in Power BI
https://learn.microsoft.com/en-us/training/paths/perform-analytics-power-bi/?ns-enrollment-type=Collection&ns-enrollment-id=djwu3eywpk4nm
Learn Power BI Basics for Free
https://bit.ly/3sTa6Gj
Power BI Free Book
https://download.microsoft.com/download/0/8/1/0816F8D1-D1A5-4F60-9AF5-BC91E18D6D64/Microsoft_Press_ebook_Introducing_Power_BI_PDF_mobile.pdf
Power BI Dashboard Step by Step FREE UDEMY COURSE
https://bit.ly/3dho1Sleep
ENJOY LEARNING 👍👍
❤10👍4
❌ Power BI alone won’t make you Data Analyst
❌ Power BI cannot get you a 18 LPA job offer
❌ Power BI cannot be mastered in 2 days
❌ Power BI is not just colorful dashboard
❌ Power BI is not simple “drag and drop”
❌ Power BI isn’t for Data Analysts only
But here’s what Power BI can do:
✔️ Power BI can save your reporting time
✔️ Power BI keeps your confidential data safe
✔️ Power BI helps you say bye to Pivot Tables
✔️ Power BI makes your report easy to consume
✔️ Power BI can update your dashboard with a single click
✔️ Power BI handles heavy data without testing your patience
✔️ Power BI is the next level for people whose work depends on Excel
I can go on and on, but you get the point.
Wrong expectations -> Wrong results
Right expectations -> Amazing results
❌ Power BI cannot get you a 18 LPA job offer
❌ Power BI cannot be mastered in 2 days
❌ Power BI is not just colorful dashboard
❌ Power BI is not simple “drag and drop”
❌ Power BI isn’t for Data Analysts only
But here’s what Power BI can do:
✔️ Power BI can save your reporting time
✔️ Power BI keeps your confidential data safe
✔️ Power BI helps you say bye to Pivot Tables
✔️ Power BI makes your report easy to consume
✔️ Power BI can update your dashboard with a single click
✔️ Power BI handles heavy data without testing your patience
✔️ Power BI is the next level for people whose work depends on Excel
I can go on and on, but you get the point.
Wrong expectations -> Wrong results
Right expectations -> Amazing results
❤11
✅ Power BI Roadmap for Beginners (2025) 📊🧠
1. Understand What Power BI Is
⦁ Business intelligence tool for data visualization and sharing insights
⦁ Types: Power BI Desktop (free), Service (cloud), Mobile app
2. Learn the Interface Basics
⦁ Views: Report, Data, Model
⦁ Navigation: Ribbons, fields pane, visualizations
3. Connect & Import Data
⦁ Sources: Excel, CSV, SQL databases, web
⦁ Use Power Query for initial cleaning and transformation
4. Learn Data Modeling
⦁ Relationships between tables
⦁ Star schema basics, hierarchies
5. Master Visualizations
⦁ Charts: Bar, line, pie, maps
⦁ Slicers, filters, drill-through
6. Practice with DAX Formulas
⦁ Basics: SUM, AVERAGE, CALCULATE
⦁ Measures and calculated columns
7. Build Interactive Reports
⦁ Dashboards, bookmarks, tooltips
⦁ Conditional formatting, themes
8. Work on Projects
⦁ Sales dashboard
⦁ KPI tracker
⦁ Customer analytics report
9. Learn Publishing & Sharing
⦁ Upload to Power BI Service
⦁ Workspaces, apps, scheduled refresh
10. Bonus Skills
⦁ Advanced DAX (time intelligence)
⦁ Power BI Copilot AI features
⦁ Integration with Excel/PowerPoint
💬 Double Tap ♥️ For More
1. Understand What Power BI Is
⦁ Business intelligence tool for data visualization and sharing insights
⦁ Types: Power BI Desktop (free), Service (cloud), Mobile app
2. Learn the Interface Basics
⦁ Views: Report, Data, Model
⦁ Navigation: Ribbons, fields pane, visualizations
3. Connect & Import Data
⦁ Sources: Excel, CSV, SQL databases, web
⦁ Use Power Query for initial cleaning and transformation
4. Learn Data Modeling
⦁ Relationships between tables
⦁ Star schema basics, hierarchies
5. Master Visualizations
⦁ Charts: Bar, line, pie, maps
⦁ Slicers, filters, drill-through
6. Practice with DAX Formulas
⦁ Basics: SUM, AVERAGE, CALCULATE
⦁ Measures and calculated columns
7. Build Interactive Reports
⦁ Dashboards, bookmarks, tooltips
⦁ Conditional formatting, themes
8. Work on Projects
⦁ Sales dashboard
⦁ KPI tracker
⦁ Customer analytics report
9. Learn Publishing & Sharing
⦁ Upload to Power BI Service
⦁ Workspaces, apps, scheduled refresh
10. Bonus Skills
⦁ Advanced DAX (time intelligence)
⦁ Power BI Copilot AI features
⦁ Integration with Excel/PowerPoint
💬 Double Tap ♥️ For More
❤19
✅ Power BI: Data Modeling 📊🧠
Data modeling is key to organizing tables and relationships for accurate insights in Power BI. Here’s a quick guide to the essentials and best practices for 2025:
🔹 1. What Is Data Modeling?
Organizing tables and defining relationships so Power BI understands your data structure.
🔹 2. Relationship Types
⦁ One-to-Many (most common)
⦁ Many-to-One
⦁ One-to-One
⦁ Many-to-Many (needs special care)
🔹 3. Keys in Relationships
⦁ Primary Key – unique in one table
⦁ Foreign Key – matching ID in another
🔹 4. Cardinality & Cross Filter Direction
Defines type and filtering behavior of relationships. Single direction is safer; bi-directional can be costly and tricky.
🔹 5. Star Schema (Best Practice)
Central fact table linked to dimension tables like Date, Product, Region—faster, simpler, and easier for calculations.
🔹 6. Snowflake Schema (Alternative)
Dimension tables have sub-dimensions. More normalized but complex and slower.
🔹 7. Model View Features
Drag to link tables, rename relationships, set properties, and mark proper date tables.
🔹 8. Common Modeling Mistakes
⦁ Circular relationships cause loops
⦁ Missing relationships break visuals
⦁ Wrong cardinality leads to bad aggregations
⦁ No date table causes time intelligence to fail
🔹 9. Tips for Better Models
⦁ Always use a proper Date Table
⦁ Clear, business-friendly names for tables and fields
⦁ Hide technical columns not needed in reports
⦁ Use DAX measures over calculated columns for performance
🔹 10. Test Your Model
Create visuals, apply filters to check relationships, use Matrix/Table view to inspect values.
💬 Double Tap ♥️ For More
Data modeling is key to organizing tables and relationships for accurate insights in Power BI. Here’s a quick guide to the essentials and best practices for 2025:
🔹 1. What Is Data Modeling?
Organizing tables and defining relationships so Power BI understands your data structure.
🔹 2. Relationship Types
⦁ One-to-Many (most common)
⦁ Many-to-One
⦁ One-to-One
⦁ Many-to-Many (needs special care)
🔹 3. Keys in Relationships
⦁ Primary Key – unique in one table
⦁ Foreign Key – matching ID in another
🔹 4. Cardinality & Cross Filter Direction
Defines type and filtering behavior of relationships. Single direction is safer; bi-directional can be costly and tricky.
🔹 5. Star Schema (Best Practice)
Central fact table linked to dimension tables like Date, Product, Region—faster, simpler, and easier for calculations.
🔹 6. Snowflake Schema (Alternative)
Dimension tables have sub-dimensions. More normalized but complex and slower.
🔹 7. Model View Features
Drag to link tables, rename relationships, set properties, and mark proper date tables.
🔹 8. Common Modeling Mistakes
⦁ Circular relationships cause loops
⦁ Missing relationships break visuals
⦁ Wrong cardinality leads to bad aggregations
⦁ No date table causes time intelligence to fail
🔹 9. Tips for Better Models
⦁ Always use a proper Date Table
⦁ Clear, business-friendly names for tables and fields
⦁ Hide technical columns not needed in reports
⦁ Use DAX measures over calculated columns for performance
🔹 10. Test Your Model
Create visuals, apply filters to check relationships, use Matrix/Table view to inspect values.
💬 Double Tap ♥️ For More
❤5
✅ Power BI DAX Basics 📊🧠
DAX (Data Analysis Expressions) is Power BI's formula language for custom calculations, measures, and columns—essential for dynamic insights in 2025 reports.
1️⃣ SUM()
Adds column values.
Example: Total Sales = SUM(Sales[Amount])
2️⃣ AVERAGE()
Gets column average.
Example: Avg Salary = AVERAGE(Employee[Salary])
3️⃣ COUNT(), COUNTA(), COUNTROWS()
⦁ COUNT() for numerics
⦁ COUNTA() for non-blanks
⦁ COUNTROWS() for table rows
Examples: Total Orders = COUNT(Orders[OrderID]) | Total Customers = COUNTROWS(Customers)
4️⃣ CALCULATE()
Modifies calculation context (DAX's powerhouse).
Example: Sales East = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")
5️⃣ FILTER()
Builds filtered tables for CALCULATE.
Example: High Sales = CALCULATE(SUM(Sales[Amount]), FILTER(Sales, Sales[Amount] > 1000))
6️⃣ IF()
Adds conditional logic.
Example: Sales Category = IF(Sales[Amount] > 500, "High", "Low")
7️⃣ SWITCH()
Handles multiple conditions cleanly.
Example: Rating = SWITCH(TRUE(), [Score] >= 90, "A", [Score] >= 75, "B", [Score] >= 60, "C", "Fail")
8️⃣ ALL()
Removes filters for totals.
Example: % of Total = DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALL(Sales)))
9️⃣ DISTINCT()
Gets unique column values.
Example: Unique Products = DISTINCT(Sales[Product])
🔟 Measures vs Calculated Columns
⦁ Measures: Dynamic, context-based (best for visuals)
⦁ Calculated Columns: Row-by-row, stored (use sparingly for performance)
💬 Tap ❤️ for more!
DAX (Data Analysis Expressions) is Power BI's formula language for custom calculations, measures, and columns—essential for dynamic insights in 2025 reports.
1️⃣ SUM()
Adds column values.
Example: Total Sales = SUM(Sales[Amount])
2️⃣ AVERAGE()
Gets column average.
Example: Avg Salary = AVERAGE(Employee[Salary])
3️⃣ COUNT(), COUNTA(), COUNTROWS()
⦁ COUNT() for numerics
⦁ COUNTA() for non-blanks
⦁ COUNTROWS() for table rows
Examples: Total Orders = COUNT(Orders[OrderID]) | Total Customers = COUNTROWS(Customers)
4️⃣ CALCULATE()
Modifies calculation context (DAX's powerhouse).
Example: Sales East = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")
5️⃣ FILTER()
Builds filtered tables for CALCULATE.
Example: High Sales = CALCULATE(SUM(Sales[Amount]), FILTER(Sales, Sales[Amount] > 1000))
6️⃣ IF()
Adds conditional logic.
Example: Sales Category = IF(Sales[Amount] > 500, "High", "Low")
7️⃣ SWITCH()
Handles multiple conditions cleanly.
Example: Rating = SWITCH(TRUE(), [Score] >= 90, "A", [Score] >= 75, "B", [Score] >= 60, "C", "Fail")
8️⃣ ALL()
Removes filters for totals.
Example: % of Total = DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALL(Sales)))
9️⃣ DISTINCT()
Gets unique column values.
Example: Unique Products = DISTINCT(Sales[Product])
🔟 Measures vs Calculated Columns
⦁ Measures: Dynamic, context-based (best for visuals)
⦁ Calculated Columns: Row-by-row, stored (use sparingly for performance)
💬 Tap ❤️ for more!
❤7👏1
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@UnboundGPT_bot doesn't lecture. It just works.
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