Microsoft Certified: Data Analyst Associate with Power BI

Connect to an Excel sales data source in Power BI, use Power Query Editor to remove the top row and set headers, remove extra columns, then close and apply.

Connect to a data source profile, then clean, load, and transform the data to prepare your analysis. As a vital first step, it helps you get your analysis going.

Connect Power BI to diverse data sources, select appropriate query types, and use parameters to optimize performance and enable data modeling, visualizations, and insights.

Profile the data after connecting to a data source to identify anomalies, examine underlying structures, and interrogate column properties and statistics.

Explore how to interrogate column properties and statistics in the Power Query editor, assessing data quality, distributions, and summary statistics for quantitative columns.

Learn to extract, transform, and load data using Power BI’s Power Query editor, reshaping sources, resolving quality issues, combining queries, and configuring data loads to avoid import errors.

Explore three Power BI methods to evaluate and transform data types: data view with model tab, model view with categorization, and the query editor’s transform option.

Explore the Power Query advanced editor to inspect and modify M code, understand let statements and applied steps, and add a trim columns step with lowercase results.

Configure data loading by enabling load for relevant queries and excluding refresh for static data to improve performance with large datasets.

Harness Power BI's Power Query editor to connect data sources, automate ETL by removing unused columns, and create custom columns like store velocity for sales analysis.

Connect to a csv Google Analytics data source and open the power query editor to create a calculated column that computes average revenue per session by dividing revenue by sessions.

Create a simple Power BI table from scratch with the enter data function, then pivot and unpivot in Power Query to reshape data for survey and Likert-scale analyses.

Create a folder of files with identical columns and use Power BI to combine them into one dataset, enabling automated etl by refreshing and loading the folder contents.

Connect to sales, competitive intelligence, survey, and marketing data sources. Transform in the Power Query Editor by removing top rows and unused columns, then close and apply to Power BI.

Learn to use the calculate function to isolate high-volume products and their average ratings by applying filters in Power BI, with a practical case study.

Remove unnecessary rows and columns from your data model to boost performance, using Power Query editor to clean a sales Excel source, set headers, and filter to the monitors category.

Build a data model by creating an item fact table and a common date table. Merge data sources and create quick measures for price and rating differences.

Explore how to select the right data visualization type—from KPI indicators and bar, line, geo, area, to pie and donut charts—and learn design tips for placing key metrics.

Configure the report page in Power BI by adjusting the page name, tooltip, Q&A toggle, page size, background, alignment, and even adding a wallpaper image to create a polished dashboard.

Learn to edit and configure interactions between visuals in Power BI, using format options to toggle interactions and switch between no filter, highlight, and filter modes for California sales.

Master bar charts as a visualization tool to compare categories and uncover insights. Learn to use aggregations like sum and average, cluster and stacked charts, track inventory and in-stock percentage.

Learn how to build and use pie charts, donut charts, and treemaps display sales by item number, while understanding why pie slices are difficult to compare and how treemaps help.

Create and customize map visualizations in Power BI using bubble maps, filled maps, and ArcGIS, and apply conditional formatting to reveal state-level sales trends and refine visuals.

Create and customize tables and matrices in Power BI, adjust sorting and column width, reorder fields, switch views, and apply conditional formatting, data bars, and icons to highlight top-selling items.

Enhance the dashboards and reports pulled from the last section to reveal deeper insights and perform advanced analysis in the data analysis section.

Enhance your Power BI reports by adding interactive visualizations and deeper analysis. Learn to apply conditional formatting, slicers, filters, reference lines, and a play axis to expose insights.

Create groups and bins in Power BI to tailor data views for different departments. Visualize grouped data, such as sales by sprint, using bar charts to compare performance.

Turn the bar chart into a drill-through with a category hierarchy to inspect item-level sales by category. Use store velocity on a field map with color scales for geographic insights.

Discover how to provide access to datasets in the Power BI service, verify data quality, and share datasets with others in your organization using manage permissions.