Microsoft Certified: Data Analyst Associate with Power BI

Learn how to apply Power BI's five core functions to solve a business challenge, from preparing data to deploying deliverables using the Instock data source.

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.

Replace the sum with a DAX measure for the average in-stock percentage in Power BI, and build a category hierarchy to drill down into items within poorer performing categories.

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.

Learn to manage data sources in Power BI by using the Power Query editor's data source settings to locate updated files and rebind your data model to the correct source.

Connect to local files or shared data sources in Power BI using get data. Select Power BI datasets on the service to connect and create a shared dataset.

Select a storage mode in Power BI by choosing between import and direct query, enabling live connections to SQL Server or using Excel and CSV sources for the case study.

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.

Apply data shape transformations in Power Query Editor to clean and reshape an unstructured data source, using transpose, fill, unpivot, and header promotion to create a year and sales table.

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.

Explore the query dependency view in power bi and how queries relate. Use it when merging or appending data sources to see impact.

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.

Standardize data types across sources, create a calculated column for store velocity, unpivot survey data into question and answer, and organize queries with a sales data group in Power BI.

Create quick measures in Power BI without touching a line of DAX coding to compare prices and ratings against competitors, revealing pricing strategy insights via subtraction and percentage difference visuals.

Resolve many-to-many relationships by creating a lookup table of unique item numbers in Power Query, then join geo data and in-stock data through a many-to-one relationship to pull data.

Create a common date table to serve as a lookup for multiple tables with date fields, enabling time series analysis and resolving many-to-many relationships between in-stock and marketing data.

Master granularity by defining a specific level for dates, such as year and quarter, using a new column and week id, to reveal accurate quarterly sales trends.

Create calculated tables in Power BI using DAX to reference a geo data source, filter by state ID, and summarize total sales by state into a state sales table.

Learn to set up the Q&A feature in Power BI, empower non-technical users, train it with synonyms, and optimize visuals by surfacing key questions like stock out percentage and revenue.

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.

Explore time intelligence in DAX with month-to-date and year-to-date metrics for sales in Power BI, using week ID and date hierarchy.

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.

Explore cardinality in data modeling by linking tables through one-to-many and one-to-one relationships, and learn why many-to-many creates a mess, plus when to merge tables.

Explore the two types of tables in a data model: data tables with quantifiable sales data and lookup tables that add item details, enabling relationships and category-based insights.

Learn how cross filter direction governs filtering between related tables in a data model. Compare single and both directions with examples from geodata, category, sales, and in stock data.

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.

Create reports in Power BI by adding visualizations, selecting the right visuals, configuring them, and applying filtering to tell data stories.

Learn to quickly add visualizations to Power BI reports using the filters, visualizations, and fields panes to create charts and geo maps.

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.

Learn to format and configure Power BI visualizations, adjusting colors, labels, titles, alignment, background, borders, and tooltips for clear, effective data storytelling.

Import a custom visual in Power BI via the app store, add a histogram chart, and visualize sales to uncover clustering patterns and actionable insights.

Learn to refine Power BI visuals by applying basic, advanced, and top-end filters to focus on relevant data, such as excluding Alaska and Hawaii and analyzing sales by category.

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.

Use the selection pane to customize dashboard layout by managing layer order, tab order, and grouping visuals; learn to hide shapes, resize groups, and add visuals to enhance readability.

Explore drill down in hierarchies within Power BI visualizations, revealing items from category to item number and filtering data by selection. Learn to create hierarchies and navigate with drill down.

Use include and exclude to quickly clean up Power BI visualizations, filtering a sales-by-category tree map by omitting or reintroducing categories for clearer comparisons.

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.

Master color and conditional formatting in Power BI to highlight key data, using legends, data colors, and rules to create a red alert for values below thresholds.

Animate a scatter chart with a play axis to show how sales and in stock percentage, averaged, change over time by month.

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.

Explore the third AI method in Power BI to uncover distribution differences by using the analyze option to identify drivers, revealing item and product numbers with dual-axis bar charts.

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.

Turn a static Likert-scale survey into an interactive data app with slicers for age groups and gender, using respondent counts to guide marketing and product decisions.

Publish the sample file to Power BI service, connect the data source through your personal gateway, and configure a daily scheduled refresh with optional failure alerts.

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.