Microsoft Power BI Desktop Data Analyst Masterclass

Power BI takes you from data preparation to analysis, visualization, and sharing, using Power Query and Power Pivot to shape, relate, and filter data before presenting insights.

Explore Power BI desktop and service for publishing and sharing reports, and compare free, pro, and premium licensing with data limits.

Install Power BI desktop and sign in to your account. Download from the Microsoft Store or powerbi.com, and sign up using an organizational email or an Office 365 trial.

Launch Power BI Desktop, sign in, and navigate the interface with the ribbon, three views (report, data, model), and panes for visuals, filters, and fields.

Explore Power BI desktop's Get Data menu, revealing six basic data source types from file sources (Excel, CSV, XML, PDF) to databases such as SQL Server and Access.

Load data from an Excel workbook called Median Age Years from Gapminder into the Power BI Desktop data model, then transform and save as a pbix.

Connect to a CSV file in Power BI desktop by selecting the text/csv data source, choosing the delimiter, previewing sample data, and loading it for data view and analysis.

Learn how to get data from databases in Power BI Desktop, including SQL Server and Amazon Redshift, using import or direct query, and consult your DBA for SQL filtering.

Open a PBIX file or Excel data model to bring data into Power BI Desktop, using Power Pivot and Power Query; access files via file, open, or recent files.

Change the data source for a pbix file by using the change source option to point to the location of the data, enabling reuse of your report with updated spreadsheets.

Explore Power BI's storage modes—import, direct query, and dual—and learn when to apply each for fast visuals, scalable data models, and a composite model.

Load data and build visualizations in Power BI using sample HR data to gain hands-on experience, and learn about the interface and optional sharing with online workspaces.

Connect to Excel data in Power BI and load the Melinta employee HR dataset, starting with table one and exploring fields like employee id, name, department, hire date, salary.

Explore the Power BI interface, switching between report, data, and model views; learn to adjust canvas settings, leverage the responsive 16:9 layout, and use the filter pane and visualizations.

Explore building stacked column and pie charts in Power BI to visualize department sizes and gender distribution, with hands-on data transformation using Power Query to handle missing values.

Master line charts and drill down in power bi to analyze headcount over time and salary versus performance, and explore trends by year, quarter, and month.

Learn to visualize salary versus performance in Power BI Desktop using matrix and card visuals, exploring average salary by department, manager, and gender with filters and conditional formatting.

Apply Power Query Editor in Power BI to clean a Wikipedia GDP data model, rename tables and columns, set headers, adjust data types, and build the top 25 GDP countries.

Use the power query editor to reduce the GDP data model to the top 20 World Bank GDP per capita countries, remove unused columns and errors, and extract first characters.

Learn to unpivot a pivot-like census dataset in Power Query Editor within Power BI, transforming states and admission years into census-year value records to support visualizations.

Explore advanced Power Query Editor options for transforming columns, including transpose, reverse rows, data type detection, and column operations like split, trim, clean, prefix or suffix, and extracting text.

Connect to web data sources in Power BI, import United States population data across 50 states from Wikipedia and World Population Review, and shape it with Power Query for mapping.

Join state population and seats data from web sources using Power Query, clean and transform tables, create a linked model, and visualize the state seats with a focused color scheme.

Use Power BI Desktop and Power Query to combine multiple Excel sheets into a single data model by adding continent columns, removing population data, and loading five queries.

Append queries across five continent tables into one all countries query in Power query Editor, creating a table with continent identifiers for easy sort and filter by continent and country.

Replace the duplicate key with an index starting at one, renamed ID. Clean and load population by country and population growth rate data in Power Query, then validate and save.

Create a sales dashboard in Power BI from a coffee chain dataset, focusing on storytelling and using card visuals to show sales, costs, expenses, and profit by product and market.

Create a Power BI sales dashboard with clustered bar charts of sales and profit by product categories and geographies, then compare actual versus budget profit via a symmetry-shaded scatter plot.

Switch to report view to add visuals from your data model, building a geospatial map of continents by population percent and a stacked bar chart by country.

Format and standardize Power BI visuals by adjusting map types and zoom options, and apply consistent title styling with blue fonts.

Explore how slicers act as page-wide filters in Power BI, enabling filtering of visuals. Create and format a continent slicer, adjust orientation, and configure single or multi-select with select all.

Apply slicers and page filters in Power BI Desktop to show visuals for America or all continents across pages, with filters pane visibility controlled by editing permissions.

Learn to add natural language Q&A to Power BI reports, typing questions to generate visuals like country lists, maps, and population charts, with synonyms and ambiguity handling.

Publish from Power BI Desktop to the service, selecting a new experience workspace, review mobile view options, and enable map visuals in tenant settings to share reports via Teams.

Master data modeling in Power BI Desktop with DAX and Power Pivot. Create measures like average salary by department and high pay proportion to reveal department insights.

Learn to load data from multiple sources, define relationships, and shape data in Power BI Desktop, using DAX to derive totals and period-to-period comparisons.

Download the Contoso sales sample pbix from the Microsoft Download Center, then open it in Power BI Desktop to see the tables and fields for building the data model.

Create your first measure in Power BI using the sum function in DAX by selecting the sales table and the sales amount column, then name and save the measure.

Explore DAX syntax by building a measure named total sales, using the equals sign and a DAX function to sum the sales amount column, with IntelliSense guiding selections.

Learn to build matrix visuals in Power BI by using measures like total sales, display by brand or product, and apply persistent currency formatting with zero decimals.

Learn how to edit and delete measures in Power BI Desktop, adjust visuals, rename measures, and add inline comments in DAX to document your data model.

Create a measure named No Class using countblank on the product class name, then use a matrix to reveal any missing class name values.

Apply DAX concepts to build measures with filter context in Power BI, using department filters, slicers, and visuals to drive aggregation like average salary and employee count.

Explore calculated columns in Power BI, comparing them with measures to compute gross profit as sales amount minus total cost, and learn when stored values aid filtering and performance.

Compare explicit and implicit measures in Power BI by creating a reusable explicit total sales measure and using temporary implicit measures in visuals.

Learn to create a net sales measure in Power BI by summing sales amount and subtracting total cost, discount amount, and return amount, with proper context and formatting.

Build DAX skills to create measures like staff percentage using the safe divide function, format as percentage, and analyze salary-based counts and proportions across departments and managers.

Create a greater than 100k percentage measure by using divide on greater than 100 count and employee count, and format as percent. Apply conditional formatting and explore department insights quickly.

Create DAX measures to compute the gender pay gap by calculating male and female average salaries, then derive gap dollar and gap percentage across departments with context-aware filtering.