Microsoft Power BI: Data Analytics with Power BI Bootcamp

Power BI bootcamp teaches data analysis through summarizing data, visualizing results, and building reports and dashboards to support decision making.

Categorize each data field as categorical or quantitative, distinguishing measurements from identifiers. Count occurrences with only categorical fields to derive quantitative totals by category, enabling visualization by type or brand.

Explore Power BI desktop data view with an orders dataset, examining ship modes, segments, locations, product categories and sales metrics, and report sales by segment and region for visual insights.

Learn to create visuals on Power BI Desktop by selecting the right chart type, placing and resizing a bar chart, and configuring region sales totals or averages.

Select the chart, then use the visualizations pane's visual tab to format data-driven elements like y axis, x axis, and grid lines, while the general tab controls size and position.

Explore common visuals formatting options in Power BI, adjusting x axis labels, y axis labels, fonts, display units, titles, colors, data labels, and bar colors for polished charts.

Learn how to switch visuals in Power BI by selecting the chart, switching between column, bar, and pie charts using the fields and formatting tabs, and adjusting colors and slices.

Explore Power BI visuals field buckets and how chart types assign fields to x axis, y axis, legends, and details. Drop fields to transform placeholders into meaningful visuals.

Explore how axes and legends drive data visualization in Power BI, comparing x and y axes across bar, column, and line charts, and using legends to segment by program type.

Learn to use x axis, y axis, and legends in Power BI to create stacked bar charts of sales by region and customer segments, with focus mode and legend buckets.

Use small multiples to compare student grades across programs by duplicating column charts for each program, instead of the current stacked column chart, showing the number of students per grade.

Explore tooltips in power bi to reveal sales, region, and segment details by hovering over charts, and add fields such as profits to tooltips while adjusting summarization.

Create small multiples of a line chart to show monthly revenue by year, using the date hierarchy to split into year and month, producing four separate yearly lines.

Enable drill down in Power BI visuals by adding states under regions to view state-level data. Use data/drill tab to drill up and disable the data points table for focus.

Build three pages of interactive Power BI reports visualizing sales by month and day of week. Use slicers, drill through, Firebase AI visuals, key influencers, and a question tool.

Prepare data for reporting by mapping orders and customers to insights. Use Power Query to add a day name, shorten to three characters, and derive 0 to 6 day index.

Explore using shapes in Power BI to customize reports by inserting shapes via the insert tab, rotate a line to 90 degrees, and format it to create a left margin.

Use text boxes in Power BI to title reports and display total sales and transactions with dynamic values, formatted as currency, then create reusable templates.

Construct line charts to show sales trends over months, customize axes, colors, and titles, and use shadows to demarcate visuals; then create ribbon charts to display segment changes over time.

Learn to visualize ranking changes and sales variance over time with ribbon charts in Power BI, using month-level data and customer segments as the legend.

Sort the average sales by day name to reveal a natural Monday–Sunday order using column charts, sort by axis, and apply shadows with format painter.

Apply gradient conditional formatting to the average sales by day name, using darker blues for higher values and lighter blues for lower values to clearly highlight ranges.

Show transactions by day of week using a distinct count of order IDs, then update conditional formatting, title, and legend to improve interactivity on the reports page.

Explore how Power BI reports allow interactivity between visuals on the reporting canvas. Edit interactions with the format button to choose none, highlight, or filter and see March highlight behavior.

Learn to design report pages with slicers that enable slicing and dicing by year, region, and category, using dropdowns and single-select settings, with proper alignment and clean visuals.

Learn how to synchronize slicers across multiple Power BI report pages, using copy-paste or the sync slicers pane to ensure consistent regional selections on all pages.

Learn, in power bi, to use the filters pane to display top ten states by sales with a bar chart, and apply filters at chart, page, and all pages levels.

Display top N categories on a visual in Power BI using by value filters. Show top ten states by sales with the filters pane and page or visual filters.

Learn to create the bottom ten in Power BI by duplicating the top ten, adjusting the stage filter to bottom, and sorting ascending to show bottom ten states by sales.

Explore how to create a matrix table heatmap in Power BI to compare top ten states by sales, showing percentage of transactions by ship mode with conditional formatting.

Apply conditional formatting to Power BI matrix cell elements to create a heatmap highlighting high and low values. Use font color, data bars, or icons via the effects color sets.

Format a matrix visual in Power BI by adding a title (percentage of transaction shipments of top ten states), adjusting column widths, centering headers and numbers, and applying shadows.

Learn to visualize sales distribution with map visuals in Power BI Desktop, enable them, prepare city data to disambiguate by country, and use bubble size to reveal regional performance.

Create a dedicated report page tooltip in Power BI, resize to tooltip size, and attach a donut chart to the visual to show segment shares by state on hover.

Create a drill through from the periodic reports page to the locations reports, duplicating the locations reports to preserve the original and use the month as the drill through context.

Learn to customize drill-through headers in Power BI by dynamically incorporating the selected month into the text, format the header, and hide tooltip and drill-through pages for clean publishing.

Build two report pages with year slicers, region and category filters, drill through from a month to locations, and view state market shares; create advanced sales exploration page with visualizations.

Create a decomposition tree visual in power bi to drill total sales by dimensions like year, quarter, month, region, and subcategory, with fixed levels and editable names.

Explore how the drill decomposition tree uses high value and low value to automatically drill to the level with the highest or lowest sales, demonstrated with ship modes and segments.

Discover how the key influencer visual uses machine learning to reveal contributors to sales increases or decreases, and set it up with category, city, region, segment, ship mode, and subcategory.

Explore Power BI bookmarks and the selection pane to capture page states, toggle visuals, and switch views using targeted bookmarks.

Design interactive Power BI reports by using bookmarks and buttons to switch between a default view and a Q&A view, and test the navigation with actions.

Create and customize a Q and A visual in Power BI Desktop, turning natural language questions into charts and insights from sales, regions, and key terms.

Design a mobile view of your Power BI reports using the mobile layout to drag, resize, and format visuals, then publish to the cloud for the Power BI mobile app.

Export Power BI reports to PDF and save or view them, publish to Power BI service from my workspace, share or embed, and enable publish to web via admin portal.

Complete your reports as you wrap up level one and prepare for level two, focusing on data preparation and data cleaning with Power BI.

Learn how to extract, transform, and load data with Power Query in Power BI, profile data, address data quality issues, apply essential transformations, and clean sample dirty data.

Connect to Excel data using Power Query, preview and transform three worksheets—consumer, corporate, and home office—handle nulls and errors, then close and apply to load into Power BI Desktop.

Power Query defaults to profiling the top 1000 rows, affecting column quality and distribution. Switch to column profiling based on entire data set to scan all rows.

Preview column quality in Power Query to identify empty and error values, and learn how the M language's case sensitivity affects transformations and data fixes.

Power BI bootcamp teaches using column distribution in Power Query to visualize a column's value distribution, count distinct vs unique values, and spot outliers with charts.

Explore column profile in Power Query to view quick statistical summaries for a selected column, including errors, empties, distinct and unique values, data-type specific stats, and distribution charts.

Highlight all columns, then use remove duplicates in Power Query to identify and remove duplicates across the entire data table, ensuring clean, unique records.

Discover how to combine data in Power Query by appending rows or merging columns, using the 80/20 rule to focus on the two core methods and starting with append queries.

Power BI bootcamp teaches how to append queries across datasets with the same headers, showing that differing column orders don’t prevent aligning segment and quantity columns in the final dataset.

Merge queries in Power BI to bring state and region from the cities table by matching the city columns, then expand to include state and region.

Learn how to merge queries in Power Query using left outer, inner, right outer, and full outer joins to enrich transactions with cities, region, and state data.

Merge the bio data with the academics table to add maths, english, and biology scores, using student name as the key to avoid duplicate rows.

Learn how to transpose data in Power Query within the Power BI data analytics bootcamp, turning rows into columns via the transform tab and using the first row as headers.

Learn how to correctly transpose data in Power Query, prevent the first row from becoming headers, and restore six columns by removing promoted headers and change-type steps.

Learn how unpivoting columns in Power Query converts header names into a new months column and their values into a corresponding value column, normalizing data for analysis.

Learn how null values affect unpivot operations in Power Query, and see how unpivoting other columns handles months with missing data, such as January, while nulls disappear.

Learn to unpivot columns in Power Query for Power BI by turning header rows into attributes (use first row as headers) and unpivoting other columns for analysis.

Pivot columns in Power Query convert a metric column into separate columns such as sales, quantity, discount, and profit by aggregating values, usually with sum.

Master group by in Power Query to summarize data by one or two columns, using basic and advanced aggregations like sum, average, and custom settings.

Learn how to add new columns in Power Query using duplicate and index columns, plus general, data type specific, conditional, and custom columns in Power BI Desktop.

Derive new columns in power query with the add column tab, focusing on text, numbers, and date-time, including extracting the last characters after the final hyphen from a text column.

Derive new columns from numbers and dates in Power Query within Power BI, including price per unit, and age calculations, with year, month, and day name extractions.

Create a conditional column in Power Query to classify students by grade points using rules: <2.0: advised to withdraw; <2.5: fail; else: pass, named student grade.

Create a conditional column in Power BI to determine promotion status from a student grade column, moving to the next level when passed, or staying or withdrawing as needed.

Learn to create custom columns in Power Query using M expressions, including price per unit calculations, referring to data columns, and building a conditional quantity band with if statements.

Power BI: create a custom column to apply a 20% discount when sales exceed 1500 and quantity exceeds five, showing why multi-condition logic requires a custom column.

Use column from example in Power Query to create a new column by showing a pattern, such as first character of the first name dot surname for emails.

Explore common Power Query column transformations, including filling down nulls, replacing values, merging and splitting columns, and creating distinct postal code and city fields in Power BI.

Highlight all columns in power query using ctrl+a after clicking a column, then select remove duplicates to remove duplicate records.

Learn to diagnose Power Query errors, such as divide by zero, non-numeric characters and misformatted dates, by inspecting cells, converting types to text, and applying replace values or replace errors.

Trace and diagnose Power Query errors by using keep rows with errors, unpivoting, and a custom try expression to reveal error reasons and messages across columns.

Learn to clean dirty data with Power Query by first identifying issues, since 80% of cleaning is recognizing what’s wrong, then apply a mix of tools across six sample datasets.

Identify and fix misaligned data by transposing and unpivoting in Power Query, align ship mode and segment into columns, and clean order date in Power BI from Excel.

learn to clean and reshape badly structured sales data in Power Query, using transpose, merge columns, unpivot, and split by delimiter to align order IDs and dates.

Learn how to clean messy ERP-exported customer data with Power Query, using extract between delimiters, add column, and create separate name, address, age, and gender columns.

Learn to clean mixed data in Power BI by separating numbers from text in a hospital quantity column and extracting units, using Power Query's column from examples and M formulas.

Power BI bootcamp teaches cleaning dirty data in Power Query by splitting pipe-delimited cells into rows, duplicating and merging queries, and trimming spaces to map categories to amounts.

Demonstrates turning a single column with multi-level headers into separate category, subcategory, and account type columns using Power Query, blank-row removal, and delimiter-based extraction.

Connect to a folder via a manual Power Query workflow to extract Excel workbooks, create a custom column for extraction, filter metadata, and expand data before applying.

Learn to use an excel workbook as a power query source and combine multiple worksheets with the same structure. Refresh updates add new worksheets automatically.

Explore connecting SharePoint or OneDrive as a data source for Power BI by creating a team site, uploading folders, and loading data into Power BI desktop with Power Query.

Connect to an Excel file on SharePoint or OneDrive for business with Power BI Desktop, copy the file path, sign in, and transform data in Power Query.

Connect to a SharePoint folder with Power Query in Power BI, extract Excel files, filter by folder path, and expand metadata to load worksheet data.

Connect to OData feeds with Power BI using the Open Data Protocol to access Northwind data, then import customers and invoices via the URL and apply changes in Power Query.

Import data from Microsoft SQL Server AdventureWorks 2022 in Power BI, compare native and embedded queries in Power Query, and highlight query folding and how top rows affect it.

Learn to import data from SQL Server with the SQL Server connector, run a select statement, preview in Power Query editor, rename the query, and apply changes.

Learn how query folding in Power Query pushes transformations to the SQL Server, compare native query folding with embedded queries, and see how keeping top rows can break folding.

Explore direct query in Power BI by connecting to SQL Server and loading AdventureWorks 2022 tables, understanding query folding, native queries, and limitations when transforming in direct query mode.

Wraps up Power Query basics in the Power BI bootcamp, guiding data import, quality profiling, and common transformations such as transpose, merge, and append, plus error handling and data sources.

Prepare for Power BI data modeling with prerequisites like basic Power BI use, Power Query, and Windows desktop installation. Learn normalization, dimensional modeling, star schema, relationships, and DAX.

Learn how data normalization creates separate tables for sales, customers, and locations, and how denormalized tables keep all fields in a single table.

Compare denormalized and normalized data tables, and learn how normalization enables efficient storage for transactional business data, covering products, customers, people, places, and dates.

Treat revenue as a grasshopper and separate basic data into one table for products, customers, places, people, and dates, while adding detail tables for attributes like type, color, and size.

Understand that fact tables hold numeric measurements like revenue and cost with IDs such as product ID and customer ID, while dimension tables store descriptive attributes in normalized data.

Explore normalization by separating fact tables and dimension tables to eliminate redundancy, and compare denormalized versus normalized data using orders, customers, locations, and products.

Explore how data moves from denormalized to normalized by understanding the business domain and context. Map data entities and link ids across departments to ensure clean, connected data.

Map data fields into fact and dimension tables to achieve normalization, using a five-step process with an example denormalized superstore dataset in Excel.

Map data for normalization in Power BI by identifying categorical and measurement columns, using Excel to transpose data, and assigning unique keys for fact and dimension tables.

Map out the data by defining dimensions such as customer, location, and product using related columns, and identify fact fields by pairing measurements with dimension keys while avoiding redundancy.

Create an entity relationship diagram from a normalized data model, build a fact table and dimension tables, link them via relationships, and form a denormalized table.