Business Intelligence with Power BI Desktop

Open Power BI Desktop, adjust options, deselect preview features, and configure data load to disable auto detect new relationships; set regional to English (United States) before connecting data.

Import and connect data in Power BI, then transform data with Power Query to clean headers and remove unnecessary columns. Save the project and review applied steps.

Deal with text in Power BI Desktop by importing a CSV, transforming text with Power Query Editor, creating and merging columns, and extracting usernames and domains from emails.

Master transforming numerical data in Power Query Editor, computing averages, maximums, and rounded values. Add and manipulate columns to calculate discounts and discounted prices with rounding, then close and apply.

Import and transform date and calendar data in Power BI, create date-related columns, adjust the start of the week, and extract year, day names, earliest and latest dates.

Learn to import csv data in Power BI Desktop, add an index column starting from one, and create a conditional column classifying orders as single or multiple items.

Group and aggregate data in Power BI Desktop with the transform 'group by' feature, using basic and advanced options to compute total quantities by product key and by customer key.

Learn how to pivot and unpivot data in Power BI Desktop, turning columns into rows and rows into columns, while cleaning messy tables and promoting headers.

Pivot the table by turning rows into columns using pivot column and value columns, yielding a year view with 1994 sales and revenues, ready for data analysis.

Learn to merge queries in Power BI Desktop by joining tables on product key, selecting needed fields, and creating a product lookup, while avoiding duplicates via relationships.

Append three csv-based tables in Power BI's Power Query editor to form a single dataset, load sources, rename the project, sort by date, and organize queries into a folder.

Connect a folder in Power BI Desktop, combine sales data from 2015 to 2017, and load a unified table with Power Query editor.

Fix data source errors by renaming or reselecting the original source in Power Query editor, and ensure all connections are set to include in report refresh.

Adjust data types and categories in Power BI desktop by formatting dates and currencies, and transforming new tables like territories. Define data categories for maps to ensure accurate visuals.

Create a territory hierarchy by nesting continent, country, and region under one item, then drag the territory hierarchy into the map visualization to illustrate data.

Investigate the data model and business issue that prevents analysis, using a matrix to show how a missing link between sales and product tables blocks breakdown by product.

Apply normalization to organize tables for efficiency. Distinguish data tables from data lookups, noting that lookups hold descriptive attributes like customer and product details, while data tables store numeric values.

Build a proper data model by creating active relationships between tables in Power BI Desktop, linking sales to customer, calendar, territory, and product using keys, instead of merging queries.

Build a snowflake schema by linking the sales table to product, subcategory, and category through product subcategory key and product category name, establishing connections to all tables.

Manage relationships in Power BI Desktop by editing connections between stock date, order date, and the calendar; toggle between active and inactive relationships.

Connect multiple data tables by establishing one-to-many relationships and using shared lookup tables to resolve many-to-many issues between sales, return, and product key and territory key.

Explore filter flow in Power BI Desktop, guiding downstream filters from territory to sales and return, and learn how key selection impacts accuracy and duplication.

Understand bi-direction filters in Power BI Desktop, where two-way cross-filter directions risk ambiguous relationships and headaches; prefer single-direction filters for simplicity.

Hide fields from the report view to prevent end users from filtering on customer key, product key, and full name, and learn how to hide them in the data model.

Learn the DAX language in Power BI to create calculated columns and measures, understand row context versus aggregation, and choose when to use Power Query versus DAX.

Master measures in Power BI: they are invisible in tables, depend on filter context, and return aggregate values for visualizations, unlike calculated columns that rely on row context.

Add columns and measures in Power BI from the report or data panel, and create an explicit measure like quantity sold, while implicit measures stay inside a visual.

Master DAX date and time functions and create calculated columns in Power Query to extract birth year and compute current age with today.

Master DAX date and time functions in Power BI Desktop by correcting birth date versus birth year errors, updating tables, and using shortcuts to extract the year.

Learn to write conditional logic in Power BI using if, or, and to create calculated columns—classifying parents by total children and weekends by day of week.

Master text functions in Power BI Desktop using Power Query Editor: build full names with concatenate or and operator, add spaces, derive three-letter month abbreviations with left, convert to uppercase.

Master joining related data in Power BI by pulling the retail price from the product table into sales with DAX, then calculating revenue from price and order quantity.

Explore DAX math functions like sum, divide, and average in Power BI Desktop to create measures, visualize data, and analyze quantity returned, return rate, and revenue.

Explore DAX count functions: count, count rows, count A, and distinct count, showing how to handle duplicates in total return and total orders with unique value counting.

Examine why total orders may not match the sum of line items in Power BI, due to duplicate order numbers; count unique orders to get accurate totals.

Master using the DAX calculate function to apply filter context and return conditional measures. Explore examples like bulk orders, total orders, and weekend versus weekday sales.

Learn how the calculate function with all removes filters to return grand totals and create measures like all orders and overall average price for cross-category comparison.

Use the filter function to create a table subset by a filter expression, and compare with calculate; avoid using measures in filters when filtering by price above the overall average.

Learn time intelligence in Power BI Desktop to calculate year-to-date revenue and start-of-month totals. Create a previous-month revenue measure and a revenue target with calculate and date functions.

The lecture demonstrates creating a stacked bar visualization showing total orders, total revenue, and total profit by subcategory, and building measures for total revenue, total cost, and total profit. It also guides applying formatting options like legend, axis labels, color, and display units to tailor the report.

Master visual, page, and report level filters in Power BI to drive data across visuals; apply basic, top N, and advanced filtering with clear filter context.

Visualize data with matrix by placing total orders and return rate in values, drill down by category name, sub category name, product name, and apply conditional formatting with data bar.

Learn to use slicers as filters in Power BI Desktop, create date timelines with start and end dates and relative dates, and sync slicers across pages with report-level filtering.

Explore cards and KPI visuals in Power BI Desktop, displaying total revenue and profit with indicators, trend lines, and start of month and previous month comparisons.

Rearrange visuals in Power BI to visualize total orders and monthly revenue with KPI and card elements, then filter to show the top product by orders or profit.

Visualize orders, profit, and revenue on maps in Power BI using basic and field map visuals, with interactive slicers to compare continents and countries across a dynamic dashboard.

visualize data with a treemap in Power BI Desktop, drill down by category and subcategory, toggle data labels, and explore interactions with slicers and key metrics.

Create trendline and area chart visualizations to analyze weekly profit trends over time, using a line chart and stacked area chart with product categories and legends.

Learn to add tooltips for total profit, total revenue, and total order, apply a trend line with forecast in Power BI Desktop, and adjust confidence intervals, period, and seasonality settings.

Learn to build gauge visuals in Power BI to track monthly performance against targets, create dynamic measures like order and revenue targets, and apply date context filters for up-to-date pacing.

Edit report interactions among visualizations in Power BI Desktop, including slicers, KPIs, and maps. Learn to fix interactions, apply filter context, and highlight subcategories for precise data storytelling.

Use the drill through filter in Power BI Desktop to jump to a detail page filtered by the selected product name, enabling data discovery and trend insights.

Create and link bookmarks in Power BI desktop to capture page states, rename tabs for clarity, and use buttons with bookmark actions to navigate between product detail and executive summary.

Explore what-if analysis and parameterized scenario testing to forecast revenue and profit by adjusting price with a percentage parameter, and compare adjusted revenue and profit across products.

Define filtered views with row-level security to tailor Power BI reports by territory, and test manage roles and view as for Pacific and North America.

Add more visuals from the visuals pane, sign in to access visuals, and switch to the mobile layout to preview reports on mobile in the PBI service.