Data Analyst Interview Questions 👇
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
👇👇
https://news.1rj.ru/str/sqlspecialist
Hope this helps you 😊
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
👇👇
https://news.1rj.ru/str/sqlspecialist
Hope this helps you 😊
❤5
Data Analyst INTERVIEW QUESTIONS AND ANSWERS
👇👇
1.Can you name the wildcards in Excel?
Ans: There are 3 wildcards in Excel that can ve used in formulas.
Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.
Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.
Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.
Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.
2.What is cascading filter in tableau?
Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.
3.What is the difference between .twb and .twbx extension?
Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.
4.What are the various Power BI versions?
Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subnoscription while workspaces are at Premium capacity.
ENJOY LEARNING 👍👍
👇👇
1.Can you name the wildcards in Excel?
Ans: There are 3 wildcards in Excel that can ve used in formulas.
Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.
Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.
Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.
Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.
2.What is cascading filter in tableau?
Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.
3.What is the difference between .twb and .twbx extension?
Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.
4.What are the various Power BI versions?
Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subnoscription while workspaces are at Premium capacity.
ENJOY LEARNING 👍👍
❤2👍1
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1. What data sources can Power BI connect to?
Ans: The list of data sources for Power BI is extensive, but it can be grouped into the following:
Files: Data can be imported from Excel (.xlsx, xlxm), Power BI Desktop files (.pbix) and Comma Separated Value (.csv).
Content Packs: It is a collection of related documents or files that are stored as a group. In Power BI, there are two types of content packs, firstly those from services providers like Google Analytics, Marketo, or Salesforce, and secondly those created and shared by other users in your organization.
Connectors to databases and other datasets such as Azure SQL, Database and SQL, Server Analysis Services tabular data, etc.
2. What are the different integrity rules present in the DBMS?
The different integrity rules present in DBMS are as follows:
Entity Integrity: This rule states that the value of the primary key can never be NULL. So, all the tuples in the column identified as the primary key should have a value.
Referential Integrity: This rule states that either the value of the foreign key is NULL or it should be the primary key of any other relation.
3. What are some common clauses used with SELECT query in SQL?
Some common SQL clauses used in conjuction with a SELECT query are as follows:
WHERE clause in SQL is used to filter records that are necessary, based on specific conditions.
ORDER BY clause in SQL is used to sort the records based on some field(s) in ascending (ASC) or descending order (DESC).
GROUP BY clause in SQL is used to group records with identical data and can be used in conjunction with some aggregation functions to produce summarized results from the database.
HAVING clause in SQL is used to filter records in combination with the GROUP BY clause. It is different from WHERE, since the WHERE clause cannot filter aggregated records.
4. What is the difference between count, counta, and countblank in Excel?
The count function is very often used in Excel. Here, let’s look at the difference between count, and it’s variants - counta and countblank.
1. COUNT
It counts the number of cells that contain numeric values only. Cells that have string values, special characters, and blank cells will not be counted.
2. COUNTA
It counts the number of cells that contain any form of content. Cells that have string values, special characters, and numeric values will be counted. However, a blank cell will not be counted.
3. COUNTBLANK
As the name suggests, it counts the number of blank cells only. Cells that have content will not be taken into consideration.
Ans: The list of data sources for Power BI is extensive, but it can be grouped into the following:
Files: Data can be imported from Excel (.xlsx, xlxm), Power BI Desktop files (.pbix) and Comma Separated Value (.csv).
Content Packs: It is a collection of related documents or files that are stored as a group. In Power BI, there are two types of content packs, firstly those from services providers like Google Analytics, Marketo, or Salesforce, and secondly those created and shared by other users in your organization.
Connectors to databases and other datasets such as Azure SQL, Database and SQL, Server Analysis Services tabular data, etc.
2. What are the different integrity rules present in the DBMS?
The different integrity rules present in DBMS are as follows:
Entity Integrity: This rule states that the value of the primary key can never be NULL. So, all the tuples in the column identified as the primary key should have a value.
Referential Integrity: This rule states that either the value of the foreign key is NULL or it should be the primary key of any other relation.
3. What are some common clauses used with SELECT query in SQL?
Some common SQL clauses used in conjuction with a SELECT query are as follows:
WHERE clause in SQL is used to filter records that are necessary, based on specific conditions.
ORDER BY clause in SQL is used to sort the records based on some field(s) in ascending (ASC) or descending order (DESC).
GROUP BY clause in SQL is used to group records with identical data and can be used in conjunction with some aggregation functions to produce summarized results from the database.
HAVING clause in SQL is used to filter records in combination with the GROUP BY clause. It is different from WHERE, since the WHERE clause cannot filter aggregated records.
4. What is the difference between count, counta, and countblank in Excel?
The count function is very often used in Excel. Here, let’s look at the difference between count, and it’s variants - counta and countblank.
1. COUNT
It counts the number of cells that contain numeric values only. Cells that have string values, special characters, and blank cells will not be counted.
2. COUNTA
It counts the number of cells that contain any form of content. Cells that have string values, special characters, and numeric values will be counted. However, a blank cell will not be counted.
3. COUNTBLANK
As the name suggests, it counts the number of blank cells only. Cells that have content will not be taken into consideration.
❤4🤔1
📈 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!
❤8👍1👏1
✅ Power BI Interview Topics Checklist 📊💼
Frequently asked for Data Analyst & BI roles at startups, MNCs & consulting firms.
🔹 Power BI Basics
⦁ What is Power BI? Components & architecture
⦁ Power BI Desktop vs Service differences
⦁ Supported data sources
⦁ Import vs DirectQuery
⦁ Power Query Editor basics
🔹 Data Transformation (Power Query)
⦁ Cleaning nulls & duplicates
⦁ Data type conversions
⦁ Merge & Append Queries
⦁ Conditional & custom columns (M language)
🔹 Data Modeling
⦁ Star vs Snowflake Schema
⦁ Relationships: 1:1, 1:Many, Many:Many
⦁ Normalization vs Denormalization
⦁ Calculated Columns vs Measures
⦁ Role-playing dimensions (Date table)
🔹 DAX (Data Analysis Expressions)
⦁ DAX basics: CALCULATE, FILTER
⦁ Time Intelligence: YTD, MTD, SAMEPERIODLASTYEAR
⦁ Row Context vs Filter Context
⦁ SUMX, AVERAGEX, COUNTROWS
⦁ RANKX, SWITCH, IF, HASONEVALUE
⦁ Variables in DAX
🔹 Visualization & Reports
⦁ Bar, line, map, KPI, combo charts
⦁ Slicers, filters, drill-through
⦁ Bookmarks, tooltips & buttons
⦁ Custom visuals from AppSource
⦁ Report theming & formatting
🔹 Publishing & Sharing
⦁ Power BI Service workspace management
⦁ Dashboards vs Reports
⦁ Row-Level Security (RLS)
⦁ Scheduled data refresh
⦁ Power BI Gateway
🔹 Real-world Scenarios
⦁ Sales performance dashboard
⦁ Inventory & supply chain dashboard
⦁ HR attrition report
⦁ Financial forecasting with DAX
⦁ Executive summary with KPIs & trends
💡 Pro Tip: Build 3–5 diverse dashboards with real datasets for practice.
💬 Tap ❤️ for more!
Frequently asked for Data Analyst & BI roles at startups, MNCs & consulting firms.
🔹 Power BI Basics
⦁ What is Power BI? Components & architecture
⦁ Power BI Desktop vs Service differences
⦁ Supported data sources
⦁ Import vs DirectQuery
⦁ Power Query Editor basics
🔹 Data Transformation (Power Query)
⦁ Cleaning nulls & duplicates
⦁ Data type conversions
⦁ Merge & Append Queries
⦁ Conditional & custom columns (M language)
🔹 Data Modeling
⦁ Star vs Snowflake Schema
⦁ Relationships: 1:1, 1:Many, Many:Many
⦁ Normalization vs Denormalization
⦁ Calculated Columns vs Measures
⦁ Role-playing dimensions (Date table)
🔹 DAX (Data Analysis Expressions)
⦁ DAX basics: CALCULATE, FILTER
⦁ Time Intelligence: YTD, MTD, SAMEPERIODLASTYEAR
⦁ Row Context vs Filter Context
⦁ SUMX, AVERAGEX, COUNTROWS
⦁ RANKX, SWITCH, IF, HASONEVALUE
⦁ Variables in DAX
🔹 Visualization & Reports
⦁ Bar, line, map, KPI, combo charts
⦁ Slicers, filters, drill-through
⦁ Bookmarks, tooltips & buttons
⦁ Custom visuals from AppSource
⦁ Report theming & formatting
🔹 Publishing & Sharing
⦁ Power BI Service workspace management
⦁ Dashboards vs Reports
⦁ Row-Level Security (RLS)
⦁ Scheduled data refresh
⦁ Power BI Gateway
🔹 Real-world Scenarios
⦁ Sales performance dashboard
⦁ Inventory & supply chain dashboard
⦁ HR attrition report
⦁ Financial forecasting with DAX
⦁ Executive summary with KPIs & trends
💡 Pro Tip: Build 3–5 diverse dashboards with real datasets for practice.
💬 Tap ❤️ for more!
❤8
Hi guys,
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738
SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664
Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615
Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738
SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664
Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615
Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
❤9🔥1👏1
𝗣𝗼𝘄𝗲𝗿 𝗯𝗶 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗪𝗶𝘁𝗵 𝗔𝗻𝘀𝘄𝗲𝗿𝘀📊
1. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿 𝗳𝗿𝗼𝗺 𝗘𝘅𝗰𝗲𝗹?
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 is Microsoft’s interactive data visualization and business intelligence tool that handles large datasets, offers advanced visualizations, supports real-time dashboards, and enforces strong security, whereas 𝗘𝘅𝗰𝗲𝗹 is best for flexible calculations, familiar analysis, and smaller data volumes���.
2. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀.
𝗞𝗲𝘆 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 include Power BI Desktop (report creation), Power BI Service (online sharing/collaboration), Power Query (data transformation), Power Pivot (data modeling), and Power BI Mobile apps���.
3. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗼𝘄𝗲𝗿 𝗤𝘂𝗲𝗿𝘆 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗶𝘀 𝗶𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁?
𝗣𝗼𝘄𝗲𝗿 𝗤𝘂𝗲𝗿𝘆 is a tool for connecting, cleaning, and transforming data from multiple sources; it is crucial because it automates and standardizes data preparation, saving time and ensuring repeatable, reliable analysis���.
4. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗶𝗺𝗽𝗼𝗿𝘁 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜?
𝗜𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗗𝗲𝘀𝗸𝘁𝗼𝗽, use the ‘Get Data’ button to select your data source (such as Excel, SQL, or web), then use Power Query Editor to transform the data before loading it into the report��.
5. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜?
𝗔 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹 is a collection of tables, relationships, and calculations that organize data for analysis; Power BI’s data model lets users relate multiple tables and define calculations for meaningful insights��.
6. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜.
𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 add computed fields to data tables and are calculated row by row, while 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 are dynamic calculations evaluated based on filter context in reports.
7. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲𝗺?
𝗩𝗶𝘀𝘂𝗮𝗹𝘀 are charts, graphs, and other visual representations of data; create them in Power BI Desktop by dragging fields onto the report canvas and selecting a visualization type.
8. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜?
𝗥𝗶𝗴𝗵𝘁-𝗰𝗹𝗶𝗰𝗸 a table in Data view, choose ‘New column’, and define the formula using DAX.
9. 𝗗𝗲𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗵𝗲 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝘂𝘀𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜'𝘀 𝗗𝗔𝗫 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲.
𝗗𝗔𝗫 enables advanced calculations, time intelligence functions, and dynamic aggregations, unlocking deep analytics directly within reports.
10. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝘀𝗹𝗶𝗰𝗲𝗿 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸?
𝗔 𝘀𝗹𝗶𝗰𝗲𝗿 is a visual filter; it lets viewers filter report data interactively by selecting values or ranges.
11. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗼𝗳 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜.
𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 connect tables based on key columns, allowing users to analyze data across tables seamlessly.
12. 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗮 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗿𝗲𝗽𝗼𝗿𝘁?
𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 with star schema modeling, avoid excessive visuals, use summary tables, and limit the amount of loaded data.
13. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿 𝗳𝗿𝗼𝗺 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗗𝗲𝘀𝗸𝘁𝗼𝗽?
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 is the online cloud platform for sharing and collaborating on reports, while Desktop is for authoring and local analysis.
14. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗵𝗮𝗿𝗲 𝗮 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗿𝗲𝗽𝗼𝗿𝘁 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿𝘀?
𝗣𝘂𝗯𝗹𝗶𝘀𝗵 the report to Power BI Service and use sharing options such as email, links, or Teams integration.
15. 𝗖𝗮𝗻 𝘆𝗼𝘂 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗱𝗮𝘁𝗮 𝗿𝗲𝗳𝗿𝗲𝘀𝗵 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗛𝗼𝘄?
𝗬𝗲𝘀, by configuring scheduled refresh settings in Power BI Service after publishing, you can automate data updates for reports.
1. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿 𝗳𝗿𝗼𝗺 𝗘𝘅𝗰𝗲𝗹?
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 is Microsoft’s interactive data visualization and business intelligence tool that handles large datasets, offers advanced visualizations, supports real-time dashboards, and enforces strong security, whereas 𝗘𝘅𝗰𝗲𝗹 is best for flexible calculations, familiar analysis, and smaller data volumes���.
2. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀.
𝗞𝗲𝘆 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 include Power BI Desktop (report creation), Power BI Service (online sharing/collaboration), Power Query (data transformation), Power Pivot (data modeling), and Power BI Mobile apps���.
3. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗼𝘄𝗲𝗿 𝗤𝘂𝗲𝗿𝘆 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗶𝘀 𝗶𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁?
𝗣𝗼𝘄𝗲𝗿 𝗤𝘂𝗲𝗿𝘆 is a tool for connecting, cleaning, and transforming data from multiple sources; it is crucial because it automates and standardizes data preparation, saving time and ensuring repeatable, reliable analysis���.
4. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗶𝗺𝗽𝗼𝗿𝘁 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜?
𝗜𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗗𝗲𝘀𝗸𝘁𝗼𝗽, use the ‘Get Data’ button to select your data source (such as Excel, SQL, or web), then use Power Query Editor to transform the data before loading it into the report��.
5. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜?
𝗔 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹 is a collection of tables, relationships, and calculations that organize data for analysis; Power BI’s data model lets users relate multiple tables and define calculations for meaningful insights��.
6. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜.
𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 add computed fields to data tables and are calculated row by row, while 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 are dynamic calculations evaluated based on filter context in reports.
7. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲𝗺?
𝗩𝗶𝘀𝘂𝗮𝗹𝘀 are charts, graphs, and other visual representations of data; create them in Power BI Desktop by dragging fields onto the report canvas and selecting a visualization type.
8. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜?
𝗥𝗶𝗴𝗵𝘁-𝗰𝗹𝗶𝗰𝗸 a table in Data view, choose ‘New column’, and define the formula using DAX.
9. 𝗗𝗲𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗵𝗲 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝘂𝘀𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜'𝘀 𝗗𝗔𝗫 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲.
𝗗𝗔𝗫 enables advanced calculations, time intelligence functions, and dynamic aggregations, unlocking deep analytics directly within reports.
10. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝘀𝗹𝗶𝗰𝗲𝗿 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸?
𝗔 𝘀𝗹𝗶𝗰𝗲𝗿 is a visual filter; it lets viewers filter report data interactively by selecting values or ranges.
11. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗼𝗳 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜.
𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 connect tables based on key columns, allowing users to analyze data across tables seamlessly.
12. 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗮 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗿𝗲𝗽𝗼𝗿𝘁?
𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 with star schema modeling, avoid excessive visuals, use summary tables, and limit the amount of loaded data.
13. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿 𝗳𝗿𝗼𝗺 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗗𝗲𝘀𝗸𝘁𝗼𝗽?
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 is the online cloud platform for sharing and collaborating on reports, while Desktop is for authoring and local analysis.
14. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗵𝗮𝗿𝗲 𝗮 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗿𝗲𝗽𝗼𝗿𝘁 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿𝘀?
𝗣𝘂𝗯𝗹𝗶𝘀𝗵 the report to Power BI Service and use sharing options such as email, links, or Teams integration.
15. 𝗖𝗮𝗻 𝘆𝗼𝘂 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗱𝗮𝘁𝗮 𝗿𝗲𝗳𝗿𝗲𝘀𝗵 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗛𝗼𝘄?
𝗬𝗲𝘀, by configuring scheduled refresh settings in Power BI Service after publishing, you can automate data updates for reports.
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🎭 𝗥𝗲𝗲𝗹 𝘃𝘀 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗘𝗱𝗶𝘁𝗶𝗼𝗻
We often romanticize roles in tech. The truth? It's not always as shiny as it seems on the surface.
👨💻 𝗧𝗵𝗲 𝗥𝗲𝗲𝗹 𝗩𝗲𝗿𝘀𝗶𝗼𝗻:
"Just learn SQL, Python, and build a dashboard in Power BI or Tableau… and you're all set!"
It feels achievable. Even fun. And while these are important, they’re just the beginning.
💥 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸:
Most real-world data analyst roles demand far more:
🔹 Snowflake for data warehousing
🔹 Databricks for collaborative data engineering
🔹 AWS for scalable cloud computing
🔹 Git for version control
🔹 Airflow for orchestrating complex data pipelines
🔹 Bash noscripting for automation and operations
📊 The transition from classroom projects to production environments is where most struggle — not because they aren’t smart, but because the expectations shift drastically.
💡 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲 𝗳𝗼𝗿 𝗮𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀?
Learn the basics, yes. But don't stop there.
🔍 Go beyond tutorials. Get comfortable with tools used in enterprise environments.
🛠️ Build side projects that mimic real data complexity.
🤝 Connect with professionals to understand the real challenges they face.
✅ This post isn't meant to discourage — it's a wake-up call.
The gap between “𝗥𝗲𝗲𝗹” 𝗮𝗻𝗱 “𝗥𝗲𝗮𝗹𝗶𝘁𝘆” is exactly where growth happens.
We often romanticize roles in tech. The truth? It's not always as shiny as it seems on the surface.
👨💻 𝗧𝗵𝗲 𝗥𝗲𝗲𝗹 𝗩𝗲𝗿𝘀𝗶𝗼𝗻:
"Just learn SQL, Python, and build a dashboard in Power BI or Tableau… and you're all set!"
It feels achievable. Even fun. And while these are important, they’re just the beginning.
💥 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸:
Most real-world data analyst roles demand far more:
🔹 Snowflake for data warehousing
🔹 Databricks for collaborative data engineering
🔹 AWS for scalable cloud computing
🔹 Git for version control
🔹 Airflow for orchestrating complex data pipelines
🔹 Bash noscripting for automation and operations
📊 The transition from classroom projects to production environments is where most struggle — not because they aren’t smart, but because the expectations shift drastically.
💡 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲 𝗳𝗼𝗿 𝗮𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀?
Learn the basics, yes. But don't stop there.
🔍 Go beyond tutorials. Get comfortable with tools used in enterprise environments.
🛠️ Build side projects that mimic real data complexity.
🤝 Connect with professionals to understand the real challenges they face.
✅ This post isn't meant to discourage — it's a wake-up call.
The gap between “𝗥𝗲𝗲𝗹” 𝗮𝗻𝗱 “𝗥𝗲𝗮𝗹𝗶𝘁𝘆” is exactly where growth happens.
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1.What is quick filter in tableau?
Whenever using a filter in Tableau, it comes with some options to change the functionality of filter very easily, such as using it as a single value drop down or single value list or multiple value list or multiple value drop down and various other options. After we set a filter to a sheet just right click on the sheet and there you can see all the quick filter options. Changes made to these options will also change the aesthetics of filter shown on the sheet.
2.How to calculate percentage in tableau?
To calculate the percentage of data on your worksheet. Go to Analysis pane and select Percentages of, there you will see a lot percentage options such as percentage of table, column, row, pane, row in pane, column in pane and cell. Select any of the above options then define the total value o which percentage is to be calculated. The option you choose will be uniform to all the rows and columns and there is no way to specify different options to rows and columns.
3. What is Power Pivot?
The Power Pivot is an in-memory data modeling component. It provides highly compressed data storage with fast calculation. It helps you build a data model, relationships, creating formulas, calculated columns, Pivot Tables, and Pivot Charts from multiple resources.
4. What is x-velocity in Power Pivot?
X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.
Whenever using a filter in Tableau, it comes with some options to change the functionality of filter very easily, such as using it as a single value drop down or single value list or multiple value list or multiple value drop down and various other options. After we set a filter to a sheet just right click on the sheet and there you can see all the quick filter options. Changes made to these options will also change the aesthetics of filter shown on the sheet.
2.How to calculate percentage in tableau?
To calculate the percentage of data on your worksheet. Go to Analysis pane and select Percentages of, there you will see a lot percentage options such as percentage of table, column, row, pane, row in pane, column in pane and cell. Select any of the above options then define the total value o which percentage is to be calculated. The option you choose will be uniform to all the rows and columns and there is no way to specify different options to rows and columns.
3. What is Power Pivot?
The Power Pivot is an in-memory data modeling component. It provides highly compressed data storage with fast calculation. It helps you build a data model, relationships, creating formulas, calculated columns, Pivot Tables, and Pivot Charts from multiple resources.
4. What is x-velocity in Power Pivot?
X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.
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Interviewer: "Show me the top 3 products by sales within each category in Power BI."
Me: "Here's how I'd achieve that using DAX!"
First, I'd create a Rank measure:
Then, I'd use that measure as a visual-level filter.
✅ I used the
🧠 Key Concepts:
•
•
•
📝 Real-World Tip:
This technique answers questions like:
• What are the top-performing items by product line?
• Which customers are the most valuable within each region?
• What are the most popular features by software version?
💬 Tap ❤️ for more Power BI tips!
Me: "Here's how I'd achieve that using DAX!"
First, I'd create a Rank measure:
Rank =
RANKX (
ALLEXCEPT ( SalesTable, SalesTable[Category] ),
[Total Sales],
,
DESC,
DENSE
)
Then, I'd use that measure as a visual-level filter.
✅ I used the
RANKX function with ALLEXCEPT to calculate the rank of each product within its category based on total sales. I then filtered the visual to only show ranks 1-3.🧠 Key Concepts:
•
RANKX(): Ranks rows based on an expression.•
ALLEXCEPT(): Removes all filters except the specified column (Category), ensuring ranking is done within each category.•
[Total Sales]: A pre-existing measure that calculates the sum of sales.📝 Real-World Tip:
This technique answers questions like:
• What are the top-performing items by product line?
• Which customers are the most valuable within each region?
• What are the most popular features by software version?
💬 Tap ❤️ for more Power BI tips!
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✅ Data Visualization A-Z! 📊🎨
🅰️ A - Axis: The reference lines in a chart that define the scale and direction of the data.
🅱️ B - Bar Chart: Visualizes categorical data with rectangular bars, comparing values across categories.
©️ C - Choropleth Map: Displays data using color-coded regions on a map, showing geographical patterns.
🅳 D - Dashboard: A single screen displaying key performance indicators (KPIs) and insights for quick decision-making.
🅴 E - Encoding: The mapping of data attributes to visual properties like color, size, or position.
🅵 F - Filters: Allow users to interactively narrow down the data displayed in a visualization.
🅶 G - Gestalt Principles: Psychological principles of visual perception that explain how humans organize visual elements into groups.
🅷 H - Heatmap: Uses color-coding to represent the magnitude of values in a matrix, revealing patterns and correlations.
🅸 I - Interactivity: Enables users to explore data by hovering, clicking, zooming, and filtering.
🅹 J - Just Noticeable Difference (JND): The smallest difference in stimulus that a person can detect.
🅺 K - KPIs (Key Performance Indicators): Measurable values that reflect the critical success factors of an organization.
🅻 L - Legend: A key that explains the symbols, colors, or patterns used in a visualization.
🅼 M - Metrics: Quantitative measurements used to track and assess performance.
🅽 N - Narrative: The story or message conveyed by a data visualization.
🅾️ O - Overview: Providing a summary or high-level view of the data before diving into details.
🅿️ P - Pie Chart: Displays proportions of a whole as slices of a circular pie. (Use with caution!)
🆀 Q - Qualitative Data: Denoscriptive data that cannot be easily measured or quantified (e.g., colors, categories).
🆁 R - Relationships: Highlighting connections and correlations between different data points.
🆂 S - Scatter Plot: Displays the relationship between two numerical variables using points.
🆃 T - Treemap: Displays hierarchical data as nested rectangles, with the area of each rectangle proportional to its value.
🆄 U - Univariate: Relating to a single variable (e.g., a histogram showing the distribution of one variable).
🆅 V - Visual Hierarchy: Arranging visual elements in order of importance to guide the viewer's attention.
🆆 W - Word Cloud: Visualizes the frequency of words in a text, with the size of each word proportional to its frequency.
🆇 X-Axis: The horizontal axis on a chart, typically representing independent variables or categories.
🆈 Y-Axis: The vertical axis on a chart, typically representing dependent variables or numerical values.
🆉 Zoom: Allowing users to magnify or reduce the scale of a visualization to explore details or patterns.
Tap ❤️ for more data visualization tips and tricks!
🅰️ A - Axis: The reference lines in a chart that define the scale and direction of the data.
🅱️ B - Bar Chart: Visualizes categorical data with rectangular bars, comparing values across categories.
©️ C - Choropleth Map: Displays data using color-coded regions on a map, showing geographical patterns.
🅳 D - Dashboard: A single screen displaying key performance indicators (KPIs) and insights for quick decision-making.
🅴 E - Encoding: The mapping of data attributes to visual properties like color, size, or position.
🅵 F - Filters: Allow users to interactively narrow down the data displayed in a visualization.
🅶 G - Gestalt Principles: Psychological principles of visual perception that explain how humans organize visual elements into groups.
🅷 H - Heatmap: Uses color-coding to represent the magnitude of values in a matrix, revealing patterns and correlations.
🅸 I - Interactivity: Enables users to explore data by hovering, clicking, zooming, and filtering.
🅹 J - Just Noticeable Difference (JND): The smallest difference in stimulus that a person can detect.
🅺 K - KPIs (Key Performance Indicators): Measurable values that reflect the critical success factors of an organization.
🅻 L - Legend: A key that explains the symbols, colors, or patterns used in a visualization.
🅼 M - Metrics: Quantitative measurements used to track and assess performance.
🅽 N - Narrative: The story or message conveyed by a data visualization.
🅾️ O - Overview: Providing a summary or high-level view of the data before diving into details.
🅿️ P - Pie Chart: Displays proportions of a whole as slices of a circular pie. (Use with caution!)
🆀 Q - Qualitative Data: Denoscriptive data that cannot be easily measured or quantified (e.g., colors, categories).
🆁 R - Relationships: Highlighting connections and correlations between different data points.
🆂 S - Scatter Plot: Displays the relationship between two numerical variables using points.
🆃 T - Treemap: Displays hierarchical data as nested rectangles, with the area of each rectangle proportional to its value.
🆄 U - Univariate: Relating to a single variable (e.g., a histogram showing the distribution of one variable).
🆅 V - Visual Hierarchy: Arranging visual elements in order of importance to guide the viewer's attention.
🆆 W - Word Cloud: Visualizes the frequency of words in a text, with the size of each word proportional to its frequency.
🆇 X-Axis: The horizontal axis on a chart, typically representing independent variables or categories.
🆈 Y-Axis: The vertical axis on a chart, typically representing dependent variables or numerical values.
🆉 Zoom: Allowing users to magnify or reduce the scale of a visualization to explore details or patterns.
Tap ❤️ for more data visualization tips and tricks!
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If I had to start learning data analyst all over again, I'd follow this:
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
Hope this helps you 😊
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
Hope this helps you 😊
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✅ Power BI Measures vs. Calculated Columns! 📊✨
Understanding the difference between Measures and Calculated Columns is key to effective Power BI modeling.
🔹 Calculated Columns:
- Evaluated at data refresh
- Stored in the data model (increase file size)
- Best for row-level calculations (e.g., combining first & last name)
🔹 Measures:
- Evaluated at query time (dynamic)
- Not stored in the data model (smaller file size)
- Best for aggregations, calculations based on filters (e.g., Total Sales, Average Profit)
💡 Key Takeaway: Use Measures for aggregations & calculations on the fly. Use Calculated Columns when you need a new column in your table.
🤔 Example: Use a Calculated Column to get "Full Name" and a Measure for "Total Sales per Region."
Double-tap ❤️ if you found this useful!
Understanding the difference between Measures and Calculated Columns is key to effective Power BI modeling.
🔹 Calculated Columns:
- Evaluated at data refresh
- Stored in the data model (increase file size)
- Best for row-level calculations (e.g., combining first & last name)
🔹 Measures:
- Evaluated at query time (dynamic)
- Not stored in the data model (smaller file size)
- Best for aggregations, calculations based on filters (e.g., Total Sales, Average Profit)
💡 Key Takeaway: Use Measures for aggregations & calculations on the fly. Use Calculated Columns when you need a new column in your table.
🤔 Example: Use a Calculated Column to get "Full Name" and a Measure for "Total Sales per Region."
Double-tap ❤️ if you found this useful!
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✅ 5 Power BI Projects for Beginners 📊🟡
1️⃣ Sales Dashboard
→ Track revenue, profit, top products & sales by region
→ Practice: bar charts, slicers, KPIs, date filters
2️⃣ Customer Analysis Report
→ Analyze customer demographics, behavior, and retention
→ Practice: pie charts, filters, clustering
3️⃣ HR Analytics Dashboard
→ Monitor employee count, attrition rate, department stats
→ Practice: cards, stacked bars, trend lines
4️⃣ Financial Statement Report
→ Visualize income, expenses, cash flow trends
→ Practice: waterfall chart, time intelligence
5️⃣ Social Media Performance Dashboard
→ Track engagement, followers, reach by platform
→ Practice: multi-page reports, custom visuals, drill-through
💡 Tip: Use sample datasets from Kaggle, Microsoft, or mock Excel files.
👍 Tap ❤️ if you found this helpful!
1️⃣ Sales Dashboard
→ Track revenue, profit, top products & sales by region
→ Practice: bar charts, slicers, KPIs, date filters
2️⃣ Customer Analysis Report
→ Analyze customer demographics, behavior, and retention
→ Practice: pie charts, filters, clustering
3️⃣ HR Analytics Dashboard
→ Monitor employee count, attrition rate, department stats
→ Practice: cards, stacked bars, trend lines
4️⃣ Financial Statement Report
→ Visualize income, expenses, cash flow trends
→ Practice: waterfall chart, time intelligence
5️⃣ Social Media Performance Dashboard
→ Track engagement, followers, reach by platform
→ Practice: multi-page reports, custom visuals, drill-through
💡 Tip: Use sample datasets from Kaggle, Microsoft, or mock Excel files.
👍 Tap ❤️ if you found this helpful!
❤3👏2
Master Power BI with this Cheat Sheet🔥
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:
✅ Use DAX for efficient data analysis.
✅ Optimize data models for performance.
✅ Utilize drill-through and drill-down for deeper insights.
✅ Leverage bookmarks for enhanced navigation.
✅ Annotate your reports with comments for clarity.
Like this post if you need more content like this 👍❤️
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:
✅ Use DAX for efficient data analysis.
✅ Optimize data models for performance.
✅ Utilize drill-through and drill-down for deeper insights.
✅ Leverage bookmarks for enhanced navigation.
✅ Annotate your reports with comments for clarity.
Like this post if you need more content like this 👍❤️
❤5🔥1
📊 Complete Power BI Syllabus Roadmap (Beginner to Expert) ✨
🔰 Beginner Level:
1. Introduction to Power BI:
• What is Power BI? Components of Power BI
• Power BI Desktop Installation and Interface Overview
2. Data Acquisition:
• Connecting to Various Data Sources (Excel, CSV, Databases, Web)
• Import vs. DirectQuery Mode
3. Power Query Editor:
• Data Transformation and Cleaning (Removing Duplicates, Handling Null Values, Changing Data Types)
• Appending and Merging Queries
• Creating Calculated Columns
4. Data Modeling:
• Creating Relationships between Tables
• Understanding Cardinality and Cross-Filter Direction
5. Basic Visualizations:
• Creating Bar Charts, Line Charts, Pie Charts, Tables, Matrices
• Using Slicers, Filters, and Tooltips
6. Publishing and Sharing:
* Publishing Reports to Power BI Service
* Creating Dashboards
* Sharing Dashboards and Reports with Others
7. Basic Projects: Building a simple Sales Dashboard, Creating a basic Customer Analysis Report
⚙️ Intermediate Level:
1. Advanced DAX:
• Understanding DAX Syntax and Functions
• Creating Measures for Aggregation, Filtering, and Time Intelligence
• Using CALCULATE, FILTER, ALL, and RELATED Functions
2. Advanced Data Modeling:
• Star Schema vs. Snowflake Schema
• Creating Calculated Tables
• Using Hierarchies
3. Advanced Visualizations:
• Creating Map Visuals, Scatter Charts, Gauge Charts, Treemaps
• Using Bookmarks and Buttons for Navigation
• Custom Visuals from AppSource
4. Power BI Service:
• Managing Datasets, Gateways, and Dataflows
• Setting up Scheduled Refreshes
• Row-Level Security (RLS)
5. Dataflows:
• Creating and Managing Dataflows
• Using Dataflows for Data Preparation and Transformation
6. Intermediate Projects: Building an HR Analytics Dashboard, Creating a Financial Statement Report
🏆 Expert Level:
1. DAX Optimization:
• Identifying and Resolving Performance Bottlenecks in DAX Code
• Using Variables and Iterators Effectively
• Understanding Storage Engine vs. Formula Engine
2. Advanced Data Modeling Techniques:
• Implementing Role-Playing Dimensions
• Handling Many-to-Many Relationships
3. Power BI Embedded:
• Embedding Power BI Reports and Dashboards in Custom Applications
• Using the Power BI REST API
4. Power BI Administration:
• Managing Power BI Tenant Settings
• Monitoring Performance and Usage
• Implementing Data Governance and Security Policies
5. Power BI Premium:
• Understanding Premium Capacity and Features
• Using Aggregations and Incremental Refresh
6. AI and Machine Learning in Power BI:
• Using the AI Visuals (Key Influencers, Decomposition Tree)
• Integrating Custom Machine Learning Models with Power BI
7. Expert Projects: Designing and implementing a comprehensive enterprise-level Power BI solution.
💡 Bonus: Learn about Power Automate Integration, Power Apps Integration, and Data Storytelling Techniques. Get familiar with new features and updates released by Microsoft regularly.
👍 Tap ❤️ for more
🔰 Beginner Level:
1. Introduction to Power BI:
• What is Power BI? Components of Power BI
• Power BI Desktop Installation and Interface Overview
2. Data Acquisition:
• Connecting to Various Data Sources (Excel, CSV, Databases, Web)
• Import vs. DirectQuery Mode
3. Power Query Editor:
• Data Transformation and Cleaning (Removing Duplicates, Handling Null Values, Changing Data Types)
• Appending and Merging Queries
• Creating Calculated Columns
4. Data Modeling:
• Creating Relationships between Tables
• Understanding Cardinality and Cross-Filter Direction
5. Basic Visualizations:
• Creating Bar Charts, Line Charts, Pie Charts, Tables, Matrices
• Using Slicers, Filters, and Tooltips
6. Publishing and Sharing:
* Publishing Reports to Power BI Service
* Creating Dashboards
* Sharing Dashboards and Reports with Others
7. Basic Projects: Building a simple Sales Dashboard, Creating a basic Customer Analysis Report
⚙️ Intermediate Level:
1. Advanced DAX:
• Understanding DAX Syntax and Functions
• Creating Measures for Aggregation, Filtering, and Time Intelligence
• Using CALCULATE, FILTER, ALL, and RELATED Functions
2. Advanced Data Modeling:
• Star Schema vs. Snowflake Schema
• Creating Calculated Tables
• Using Hierarchies
3. Advanced Visualizations:
• Creating Map Visuals, Scatter Charts, Gauge Charts, Treemaps
• Using Bookmarks and Buttons for Navigation
• Custom Visuals from AppSource
4. Power BI Service:
• Managing Datasets, Gateways, and Dataflows
• Setting up Scheduled Refreshes
• Row-Level Security (RLS)
5. Dataflows:
• Creating and Managing Dataflows
• Using Dataflows for Data Preparation and Transformation
6. Intermediate Projects: Building an HR Analytics Dashboard, Creating a Financial Statement Report
🏆 Expert Level:
1. DAX Optimization:
• Identifying and Resolving Performance Bottlenecks in DAX Code
• Using Variables and Iterators Effectively
• Understanding Storage Engine vs. Formula Engine
2. Advanced Data Modeling Techniques:
• Implementing Role-Playing Dimensions
• Handling Many-to-Many Relationships
3. Power BI Embedded:
• Embedding Power BI Reports and Dashboards in Custom Applications
• Using the Power BI REST API
4. Power BI Administration:
• Managing Power BI Tenant Settings
• Monitoring Performance and Usage
• Implementing Data Governance and Security Policies
5. Power BI Premium:
• Understanding Premium Capacity and Features
• Using Aggregations and Incremental Refresh
6. AI and Machine Learning in Power BI:
• Using the AI Visuals (Key Influencers, Decomposition Tree)
• Integrating Custom Machine Learning Models with Power BI
7. Expert Projects: Designing and implementing a comprehensive enterprise-level Power BI solution.
💡 Bonus: Learn about Power Automate Integration, Power Apps Integration, and Data Storytelling Techniques. Get familiar with new features and updates released by Microsoft regularly.
👍 Tap ❤️ for more
❤7👏1