13+ Power BI Portfolio Projects with DAX & SQL (2026)

Explore a Power BI project using web-scraped cricket data for India and South Africa. Analyze batting, bowling, and fielding with data cleaning, M language, and DAX functions.

Learn web scraping with Power BI by extracting ESPN Cricinfo Statsguru data, then load, clean, and consolidate page data using Power Query, the advanced editor, and append queries.

Identify correct data types and clean data in Power BI by replacing hyphens with zeros, converting columns to text, number, or date, and validating data quality before reporting.

Scrape india vs south africa odi batting, bowling, and fielding data from espn cricinfo statsguru. Build a power bi report from web data using power query, pages, and data cleaning.

Scrape India vs South Africa bowling data in Power BI using web data and Power Query Editor, merging three pages into a 14-column bowling dataset of 119 records.

Extract fielding data for India and South Africa from a web source using a multi-page Power BI workflow. Transform, append pages, and deliver a clean dataset for reporting.

Explore column definitions and data cleaning in Power BI by transforming batting data: set data types, replace hyphens with zeros, and review applied steps for clean data.

Learn column definitions and data cleaning for fielding data in Power BI, converting fields to text, whole numbers, and decimals, then apply changes and load 119 records.

Create a three-page Power BI report showing batting, bowling, and fielding for any player using ESPN Cricinfo data, with background images, shapes, and hex colors.

Add multiple card visuals to the batting analysis report page. Use a player slicer and rename fields to show strike rate, runs, and highest score.

Create a bowling data analysis page in Power BI with multiple card visuals for wickets, strike rate, overs, maidens, and matches from the bowling table, linked to a one-player slicer.

Create a fielding data analysis page in Power BI by adding and formatting card visuals from the fielding table, configuring metrics like stumpings, dismissals, and D by I ratio.

Learn core dax functions, including the lookup value function, rankx, all, average, absolute, and power, to categorize strike rates in a Power BI batting dataset using a lookup table.

Add deviation and absolute deviation columns using DAX in the batting table, compute squared deviation with the power function, and publish the report to Power BI service.

Explore a panic attacks data analysis project for an NGO, building insights with Snowflake data, Power BI, Power Query, Snowflake SQL, and DAX functions like if, switch, and filter.

Learn to load a csv into Snowflake, create a Power BI project database and a blank table, then connect Snowflake to Power BI to build a report.

Clean and understand data in Power Query Editor by checking data types, handling blanks, replacing values, and applying transformations before modeling. Prepare reports in Power BI using DAX and calculations.

Add a conditional panic score column (high, medium, low) and subtract one from heart rate in place, then apply changes in the Power Query editor.

Design a three-page Power BI report with a heading page, an image page, and a patients-by-symptoms bar chart, plus filters page for panic score, gender, trigger reason, and medical history.

Create the second report page in Power BI, customize the canvas, and build bar charts showing the number of patients by symptoms like dizziness, trembling, sweating, and shortness of breath.

Create a third report page in Power BI, add and configure slicers for panic score, gender, trigger reason, and medical history, and build multiple charts with custom formatting and filters.

Create a new calculated column to classify ages into age groups using DAX in Power BI, employing nested if and switch functions to output child, adolescent, adult, or senior.

Create a DAX measure to compute percentage of dizziness in a panic attack using countrows, filter, and divide, with an alternate result for zero division and 100x for a card.

Learn to add age group analysis page in Power BI, building a clustered bar chart for sleep hours, panic score, and panic attack frequency by age group and trigger reason.

Introduce a Power BI project using Google BigQuery data to build a view on house market overview, sales performance, and house type analysis. Include data transformation, Power Query, and DAX.

Create a free Google Cloud account, set up a payment profile and verification, and prepare to load data and connect Google Cloud to Power BI for reporting.

Learn to load a local CSV into Google BigQuery, create a dataset and table, then connect BigQuery to Power BI Desktop and import data for reporting.

Use SQL in Google BigQuery for data understanding and transformations, perform data cleaning and profiling, then connect to Power BI using Power Query Editor to build reports.

Learn to clean and transform data in Power Query Editor within Power BI Desktop, using Google BigQuery for a housing dataset and applying column definitions.

Learn to compute year-on-year sales growth in Power BI by creating DAX measures using max dates and previous year, and visualize it with a line chart across sales types.

Create an offer price column from purchase price and percentage change to plot offer price versus purchase price in a scatter plot. Format axes and grid lines.

Create a median sales price change measure with medianx dax function and filters to compare current and previous year, then visualize it by region in a bar chart.

Build a last 12 months sales measure in Power BI with CALCULATE, DATESINPERIOD, and SUM, display it on a card visual, and organize measures in a dedicated measures table.

Create a measures table by moving measures from the housing table, then add a sales performance page with a background image and a bold, italic, underlined header at size 44.

Build a sales by region measure with calculate and sum on the housing table, using allexcept to filter only by region, and display it as a bar chart.

Develop a total YTD sales measure using DAX and present it in a table visual with date and purchase price from the housing table, then format for readability.

Create a donut chart showing the average price per square meter by region in Power BI, by creating a new measure and configuring visuals, labels, colors, and filter interactions.

Create a final Power BI visual showing the offer to SQM ratio per sales type, using a divide-based measure and a stacked bar chart.

Publish the report to Power BI service after signing in, select your workspace, and open the published report to review projects like cricket data analysis with SharePoint data sources.

Create a new workspace named Housing Project, enable the free trial and admin monitoring if prompted, then publish the report to that workspace and review reports and semantic models there.

Add a house type analysis page in Power BI to compare average offer price and average purchase price by house type using a clustered bar chart. Format data labels.

Create and format Power BI visuals to analyze house type with average offer/price, inflation, yield, and square meter price, then publish the report to Power BI service.

Create a power bi report from a sharepoint folder data source using a 365 trial, bookmarks, drill-through, and dax to reveal price and feature insights.

Create a free Office 365 trial account. Load data into SharePoint, connect SharePoint to Power BI, and build a report from the dataset.

Sign in to Office 365, access SharePoint, start a 30-day Business Standard trial, and complete checkout with billing details and OTP.

Assign a Microsoft 365 license to an unlicensed user, sign in to the admin center, select the 365 Business Standard trial, and prepare to connect data sources to Power BI.

Create and configure a SharePoint site, then load a dataset and connect SharePoint to Power BI using the SharePoint folder data source, enabling future reporting with DAX.

Clean and prepare data in Power BI by inspecting data types, profiling columns, and removing duplicates in Power Query Editor to build reliable reports.

Apply data cleaning steps in Power BI by using ChatGPT to replace not available PKR launch price with converted USD values and learn data privacy practices when using platforms.

Clean the Ram column in Power Query by removing non-numeric values, converting to numbers, and standardizing the unit to GB for accurate averaging in your report.

Trim the RAM column in Power BI before changing its data type, then insert and format a shape on the report overview page, including setting text, font, and alignment.

Add and format slicers on the overview page in Power BI, creating a company name and a launch year slicer, set drop-down style, and adjust color and headers for clarity.

Create a Power BI bar chart showing the count of models by company, and learn to adjust axes, data labels, colors, and the chart title.

Create a price analysis page with two bar visuals showing average price by model and by company, and add slicers for company name and launch year after currency data cleaning.

Convert launch price columns from text to numbers in Power Query Editor to enable averages, and create two bar charts showing average price by model and by company.

Learn to create a currency conversion factor table with ChatGPT and Power BI, and add custom columns to convert PK, INR, CNY, and AED to USD.

Create and format Power BI visuals to show average launch price by model and by company with bar charts, then use bookmarks to switch price analysis between Dubai and India.

Create and manage multiple bookmarks for price analysis by currency and country in Power BI, using bookmark navigator, selection pane, and data labels to compare average launch prices.

Create a price versus feature comparison page in Power BI using slicers for RAM and launch price, a table visual, and bookmarks across price ranges.

Create and group bookmarks for the price analysis page, then configure currency slicers (usd, inr, cny, pkr) and bookmarks for the feature versus price page to enable price comparisons.

Explore using concatenate, len, left, and if in DAX to create new columns, including a concatenated company and model field and a numerical weight column in the mobile data set.

Create a new table with the summarize function to group by company and model and compute average weight, average price USD, and average RAM for Power BI reporting.

Discover a Power BI project using SQL Server as the data source with 20 million records, covering profiling, dynamic SQL, SQL functions, transformation, cleaning, and publishing to Power BI service.

Learn how to download and install Microsoft SQL Server, choose between Developer and Express editions, install SQL Server Management Studio, and write simple queries in structured query language.

Create four SQL tables for a Power BI project using dynamic SQL, and run the provided code to initialize the employee salary tables for data profiling.

Learn dynamic SQL in SQL Server Management Studio, using while loops and UNION ALL to combine data from multiple employee salary tables for runtime query execution.

Profile data for the Power BI project with SQL code, calculating mean, median, mode, and null counts from SQL Server and Excel sources for clean, ready-to-report data.

Build a hash table from information_schema columns to profile data in a Power BI report, storing max, min, mean, median, mode, standard deviation, nulls, and distinct counts.

Explore how to calculate maximum, minimum, mean, standard deviation, nulls, zero counts, and distinct counts for any column in SQL Server, using dynamic SQL to apply calculations across tables.

Learn how to calculate the median salary using sql, handle odd and even record counts, and generate dynamic sql to append median logic into a data profiling query.

Learn to compute the mode in data profiling using dynamic SQL, with examples on employee data, and use dense_rank and string_aggregate to handle ties.

Declare integer variables @I and @J, initialize them to 1, and use a dynamic SQL while loop to iterate columns in hash one, retrieving each column name and data type.

Populate distinct counts and mode for numerical columns using dynamic sql to update hash one by ordinal position from the employee data.

Profile the clinical data table with SQL to reveal two crore records and key metrics. Address errors and enable mean calculations with bigint in Power BI and Power Query Editor.

Explore clinical data column distribution and data profiling in SQL Server to reveal value frequencies across key columns and prepare clean data for Power BI reporting.

Create a replica of the clinical data table for safe testing. Update the replica's admission date so the year range stays 2024-2030 for Power BI reporting.

Write sql to import data into power bi, excluding a column for column-level security. Load data from sql server into power bi desktop and troubleshoot loads.

Format the report pages in power bi by setting canvas backgrounds, wallpapers, and a consistent color palette, build a two-page layout showing hospital-level charts and year-wise data from admission dates.

Create the first Power BI report page, add a text box and a line chart, and format it to display total infections by hospital ID, including page naming.

Create the second report page by duplicating visuals, filtering to year, and updating titles for year-based insights. Publish to Power BI service and review data cleaning and load troubleshooting.

Update chart titles to year in a Power BI report, then publish to Power BI service and replace the existing report in my workspace.

Create a three-page Power BI report from a loan default data flow, employing DAX measures and visuals, while performing data profiling, cleaning, validation, refresh scheduling, and sharing the report.

Download, install, and configure the standard mode gateway to use data flows as a data source in Power BI, and set up a workspace with a free Power BI account.

Load data from an Excel sheet into Microsoft SQL Server, create the loan database, and import the loan default table to prepare a data flow for Power BI.

Create a Power BI data flow in the Power BI service using Gen one to load and transform data from SQL Server, save the flow, and schedule refresh.

Connect to data flows in Power BI desktop and load data from a loan default data flow. Understand timing for flows and outline data cleaning, reporting, publishing, and refresh scheduling.

Explore column definitions in a loan default data set and learn to build a Power BI report using DAX and SQL to assess loan eligibility and default risk.

Explore data cleaning in Power Query Editor by profiling data types, and reviewing column quality and distribution across a dataset of about 255,347 records, in Power BI.

Rename the report page to loan default and overview, insert and customize a shape, and set up DAX measures and DAX columns for KPI-driven reporting.

Build a Power BI visual with a DAX measure loan amount by purpose using SUMX, FILTER, NOT, and ISBLANK, and validate results with a table visual of raw fields.

Create a second visual showing average income by employment type using a DAX measure with calculate, average, and allexcept; validate results with the data source.

Create a dax measure for default rate by employment type using all, allexcept, countrows, divide, and filter, then visualize it with a line chart to compare defaults across employment types.

Add an age group column with a nested if DAX expression in Power BI, then compute average loan by age group using AVERAGEX and VALUES in a line chart.

Create a default rate by year measure in Power BI using calculate, countrows, allexcept, filter, and divide to apply only the year filter and display results in a line chart.

Learn data validation in Power BI by validating default rate by year with a table visual, applying correct aggregations, and using all except; compare with an Excel pivot table validation.

Create credit score bins using a nested if DAX column and visualize the median loan amount by credit score category in a Power BI line chart.

Create a donut chart in power bi to display the average loan amount for high credit score by age group and marital status, using a dax measure with filter.

Validate Power BI donut chart data by building a table visual and comparing averages by credit score bins with Excel pivot tables, confirming values across marital status and age groups.

Learn to build a line chart of total loan amount by adults' credit score bins, using calculate, sum, and averagex, with data validation across Excel and SQL.

Learn to build a DAX measure to calculate total loan for middle age adults and visualize it on a clustered column chart, breaking down by mortgage status and dependents.

Validate data in Power BI by comparing loan amount totals in a clustered column chart and a table visual using filters for middle age adults, mortgage status, and dependents.

Create and validate a Power BI report visualizing the number of loans by education type using a DAX measure, with data validation steps comparing counts to the loan default table.

Create a third Power BI page named financial risk metrics, and build a year-on-year loan amount change measure in DAX using divide, current minus previous, and previous-year filters.

Create a year-on-year default loans change DAX measure in Power BI using calculate, divide, and filter to compare current and previous years in the loan default table.

Create two line charts to visualize year-on-year default loans change and year-on-year loan amount change with DAX measures. Customize colors and remove grid lines for clean, aligned visuals across pages.

Create a DAX measure for the YTD loan amount. Render it as a ribbon chart by credit score bins and marital status.

Add a decomposition tree in Power BI to analyze loan amount by income bracket and employment type, using switch to create income brackets and applying formatting options.

Publish your Power BI desktop report to the Power BI service after signing in, then set up schedule refreshes for the data flow and report in the workspace.

Share Power BI reports with colleagues by using the share option, ensuring recipients have the proper license and same domain, and sending email invitations with access links.

Explore building six Power BI KPIs across a two-page report using DAX measures, transitioning from SQL Server to MySQL via Power Query editor and advanced editor, with data validation.

Download and install Microsoft SQL Server Developer Edition and SQL Server Management Studio, connect to the database engine, run a simple query, and prepare for structured query language.

Install MySQL server and MySQL workbench on Windows, configure a custom setup, and create databases and tables, using MySQL as a data source for Power BI projects.

Learn to build a Power BI report from test environment data in SQL Server or MySQL, and transition it to production while preserving KPIs like average demand, availability, and profit.

Import inventory data and products into a test environment in Microsoft SQL Server via flat files in SSMS, create the test_env database, and verify dbo tables for Power BI reporting.

Apply a left join in sql server to combine the test environment inventory data set with the products table, preparing data for reporting in Power BI.

Learn how to import data from SQL Server into Power BI Desktop, transform data, adjust data types and regional formats, and build a multi-page KPI report.

Create a production database in sql server, import inventory and products via flat files, verify the production dataset, and shift reports from test to production by updating data source settings.

Perform data cleaning in SQL to validate production data against test data and fix quality issues. Transition the Power BI report from test to production by updating data sources.

Import data into a MySQL database with MySQL Workbench, create prod, import prod environment inventory data set and products table, and migrate from SQL Server to MySQL for Power BI.

Create a new table in MySQL Workbench mirroring the Microsoft SQL Server table, maintaining column names, and transition Power BI reports from SQL Server to MySQL using equivalent SQL syntax.

Learn to transition a Power BI report from Microsoft SQL Server to MySQL using the Power Query advanced editor, updating the data source while preserving DAX calculations.

Validate data after transitioning a report from SQL Server to MySQL, ensure numbers align between desktop and Power BI service, and publish to the MySQL workspace.

Explore a Power BI project with a fruits sales data set to build reports in Power BI service and desktop, featuring snowflake and star schemas, data cleaning, and DAX measures.

Sign in to the Power BI service and create a workspace to build a fruits sales data set report, exploring service versus desktop connectivity and star and snowflake schema concepts.

Examine the Power BI service dataset, including customers, stores, category and brand dimensions, product and sales tables, and the snowflake schema, then import data and prepare for reporting.

Load data into Power BI service by uploading an Excel file or using OneDrive, select seven tables, and create an interactive report, then customize with wallpaper and canvas background.

Explore building and refining a snowflake schema data model in Power BI by linking the sales fact table to store dimension, customer dimension, and product dimension tables and configuring relationships.

Remove unused columns from the store and product dimensions, then build and format a Power BI line chart of yearly and monthly sales (sum and average) with styled visuals.

Add and format a customer name slicer on a Power BI report page, customize the line chart title, and tailor slicer appearance, borders, and search features.

Learn to use bookmarks in Power BI to toggle between total and average sales by year and month, using the selection and bookmarks panes and minimal visuals.

Format Power BI slicers by changing font color and font style for values and labels, including category name, product name, store name, and date slicer. Then save the report.

Learn how to use a sum of sales DAX measure to enable slicer interactivity across store and customer dimensions in Power BI, while preserving single-direction relationships.

Create an average sales measure in Power BI by dividing sum of sales by the distinct customer keys, then show top five stores and bottom five customers using top/bottom filters.

Embed power BI reports in websites, share via secure embed links, and publish a site, then sign in to view with others; future sessions cover more sharing methods.

Share Power BI reports through the chat in Teams, preview the link, and send it to colleagues; open Teams to confirm recipients can view the report.

Publish a Power BI report to the web, generate embed codes, and share publicly while noting that row-level security cannot be used with web publishing and understanding admin tenant settings.

Learn to establish a live connection to Power BI semantic models from Power BI Desktop, enabling you to build reports and publish to the Power BI service.