Microsoft Certified: Data Analyst Associate with Power BI

Learn how to apply demand planning by evaluating the in-stock percentage against a 95% threshold, identify categories meeting that target from the stock sheet, and create a visualization of performance.

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.

Apply conditional formatting in Power BI to highlight low in-stock percentages below 95%, creating dynamic alerts that update with new data and flag issues for supply chain review.

Save the file and publish to powerbi.com, signing in to upload to your workspace. Explore the Power BI service to share reports and drill down for insights.

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.

Connect to diverse data sources in Power BI with the get data button, choosing from files, databases, online services, and web data such as Google Analytics.

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.

Create a parameter-driven category filter in Power BI to prompt end users to select a category and filter visuals, then export a template for sharing.

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

Examine data structures in the Power Query editor by inspecting column headers and data types, then expand the geo data to bring in category and product name for survey respondents.

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.

Resolve data quality issues in Power BI by cleaning and transforming data with Power Query Editor: fix product codes, implement data governance, and split columns into item name and subcategory.

Replace missing or corrupt values with user friendly replacements in Power BI, using Power Query. Replace nulls with zero for order quantity and with 'no price listed' for order price.

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.

Merge data sources in power query editor using a left outer join on item name and product name, expand the desired fields, and remove duplicates to create a unified dataset.

Learn how to append queries in Power BI to stack quarterly data sources with identical columns, creating a seamless union and saving time.

Learn to create user friendly naming conventions for queries and columns in Power BI by renaming items in the Power Query Editor and Model tab, with descriptive labels and metadata.

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.

Identify and fix data load errors in Power BI by using the Power Query Editor to view error details, adjust data types, and replace invalid values with logic-driven solutions.

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.

Master basic transformations in Power BI using the Power Query Editor, including manage columns, reduce rows, sort, and quick operations like select columns and change data types.

Transform a date field with Power Query editor in Power BI, duplicate the query to create a calendar table, and add date fields such as year, month, week, and day.

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 conditional column in Power BI to segment transactions into low, moderate, and high value customers by converting the transactions field from text to a number and using if-else logic.

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.

Combine sales data, marketing data, psychographic data, cost data, external data sources, and competitive intelligence using a data map to uncover insights and deliver actionable, data-informed recommendations.

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.

Learn how to build a Power BI data model by connecting multiple data sources, creating relationships between tables, and enabling cross-source analysis with hierarchies and drill-through visuals.

Map out a data model like a blueprint, defining tables, flattening parent-child hierarchies, resolving many-to-many relationships, creating a common data table, and setting true granularity.

Define tables as the building blocks of a data model by distinguishing fact tables from lookup or dimension tables and configuring connections in Power BI to resolve many-to-many relationships.

Learn to configure tables and columns in the Power BI model view, including renaming, synonyms, descriptions, hiding data, display folders, and storage mode, formatting, and aggregation.

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.

Flatten a parent-child hierarchy in Power BI by building an employee-manager table, using the path function, and splitting the hierarchy into level one, level two, and level three columns.

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.

Define and test row-level security roles in Power BI to filter data by user, then assign emails and publish the secured report to Power BI service.

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.

Master DAX, a library of functions for relational data models, to create calculated columns and measures that respond to row and filter contexts in analyses and visuals.

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.

Delve into additional data modeling lectures to strengthen your understanding of data models, covering cardinality, types of tables, filter direction, and filter versus row context.

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.

Use the Q&A feature to uncover insights by adding it to the workspace and exploring suggested questions with visuals like bar, line, and scatter charts across categories and over time.

Create custom tooltips in Power BI by embedding a second visualization into the tooltip, showing category sales and average inventory on hand for deeper context.

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.

Learn to configure sync slicers to apply a single filter across all report pages. It ensures consistent views and a connected narrative when analyzing specific categories.

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 interactions with a 100% stacked bar chart to build a Likert scale, compare gender by respondents count with pie chart, and edit interactions to filter insights for actionable recommendations.

Explore visual types in Power BI, including maps, bar charts, and line charts, and master drill-down in hierarchies while learning to include or exclude data points to enhance interactivity.

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.

Visualize time-series sales with line charts and a dual-axis view of sales and average inventory. Explore area charts to reveal inventory peaks and support demand planning.

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.

Learn to use focus mode in power bi to enlarge a map or table view, drag category into the legend to modify data, and export data for excel analysis.

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.

Turn raw data into a standardized sales dashboard with KPI cards for total sales and average inventory on hand, using Power BI visuals for category, time, and geography.

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.

Discover how slicers in Power BI enhance UX by filtering multiple visuals, using a category data source to build a data app with a bubble map and a bar chart.

Apply top N analysis in Power BI to identify top selling states by category using a stacked bar chart and filters.

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

Identify outliers and perform time series analysis in Power BI, using grouping and binning, the decomposition tree visual, and AI insights to deepen data visualization and analysis.

Explore time series analysis with a dual axis line chart to compare sales and in-stock percentage across weeks, and build dashboards with kpi cards and category slicers for demand planning.

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.

Explore how to use Power BI service to manage datasets and create and manage workspaces, enabling you to share insights with your organization.

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.

Create and manage workspaces in Power BI, share with your organization, configure workspace roles, and publish, import, or update assets.

Create and configure a new Power BI workspace in the Power BI service, assign workspace admins, set dedicated capacity, enable a template app, and allow contributors to update the app.

Wrap up your data analytics journey with added resources and podcast highlights featuring hiring managers and career veterans who share advice to navigate your analytics career and ace the certification.

Explore emerging analytics trends, from automation and data storytelling to governance and data democratization, and learn to blend business insight with hard and soft skills.