Microsoft Power BI: Fast-Track to Business Intelligence Wins

Explore how to view imported data in Power BI using the table view. Switch between tables, copy to spreadsheets, and apply column filters to analyze large datasets.

Enter data directly in Power BI with the Enter Data feature to create a table, rename columns, add rows, and load for viewing in the data pane.

Import Excel data into Power BI by selecting the Excel workbook, choosing sheets, and loading them into tables; fix headers via the query editor and refresh after updating the source.

Practice importing data into Power BI using prepared files: TV shows.xls with all sheets, a CSV, and a TSV, to get everything into Power BI.

Import and load multiple data sources in Power BI using Get Data, including Excel, CSV, and TSV, fix headers with Edit Query, then view tables for visualizations and formulas.

Explore data types in Power BI, learn to configure them correctly, apply best practices, and boost data analysis and dashboard capabilities.

Learn to manipulate dates in Power BI by extracting month and year, adding days, calculating days between dates, and creating custom dates with the date function.

Learn boolean values and blanks in Power BI. Test true with TRUE() and a single equals sign, check blanks with ISBLANK(), and combine results using NOT, AND, OR.

Explore how visuals convey information in dashboards, discuss advantages and disadvantages, and learn to create bar charts, line charts, pies, and cards in Power BI using an insurance sales dataset.

Explore the Power BI visual editor interface, learn to create and format visuals, manage data, apply filters, and navigate pages to build actionable BI insights.

Learn to create and customize a bar chart in power bi, differentiating total sales by gender, with data filtering, axis and title customization, and data labels.

Create a month-level line chart in Power BI to visualize sales amount over time, with markers, data labels, currency formatting, and cleaned axes for a polished dashboard.

Apply filters to chart data in Power BI to tailor visuals across pages and the whole report, excluding the other gender category from sales distributions.

Learn to create and customize a Power BI card visual using total sales, format currency, adjust display units, and configure title placement and alignment for clear business insights.

Build and format a Power BI table visual to analyze sales by gender, count unique advisors, and display withdrawals with currency formatting and centered alignment.

Craft a beautiful Power BI dashboard that analyzes City Life Insurance data, visualizing sales and withdrawals, month-by-month trends, regional performance, product contributions, and top selling advisors.

Explore the personal finance dashboard project in Power BI, using visuals to analyze Rob's transactions without DAX, and determine monthly spending and if income covers expenses.

Learn to download your own financial transaction data—or use provided samples as an optional alternative—and compare business and analyst perspectives while exporting from banks to csv for dashboards.

Create the personal finance dashboard in Power BI, visualizing deposits and withdrawals with cards and a month-by-month bar chart, using currency formatting and color coding.

Analyze insights from the data by validating dashboard figures, cleaning deposits and withdrawals, and exploring transaction descriptions and categories to reveal realistic spending patterns.

Explore Data Analysis Expressions (DAX), a library of functions to build formulas in Power BI. Understand why DAX enables complex BI, including net sales and product totals.

Write your first DAX formula by creating a calculated column named Net Sales from sales amount minus withdrawal amount in the simple data table.

Define measures in Power BI to return a single value, specify the table and column, and create a net sales measure to reflect filter context.

Master calculation context in Power BI by understanding how rows and columns form the calculation, how measures and calculated columns differ, and how visuals and filters modify the context.

Explore how DAX measures drive visuals in Power BI, compare implicit versus explicit measures, and create a total sales measure using the sales amount in insurance data.

Rename and delete measures and columns in power BI, using formula edits or right-click, with automatic updates to formulas and visuals across charts and tables.

Create measures in Power BI to aggregate numbers using sum, minimum, maximum, and standard deviation across tables; place measures in the most relevant data tables for clarity.

Learn to aggregate text values in Power BI using min, max, count, distinct count, and concatenate, and create dynamic measures such as max rating, number of products, and distinct advisors.

Learn to aggregate dates in DAX with the minimum and maximum functions, create measures for record date and date of birth, and explore date versus number representations in Power BI.

Master the switch function as a powerful DAX conditional that replaces nested if, handles multiple values, and supports the flexible form with true value for complex classifications like transaction type.

Explore DAX value comparison in Power BI, using single equals, greater/less than, not equal, and is blank. Apply these to calculated columns like large sales and rating categories.

Learn to use the calculate function with filters in Power BI, establishing context filters to compute total insurance sales from total sales, via the filter function and context modifiers.

Explore how the calculate function combines context and aggregations to produce a single value, using filters and different calculation contexts to build insurance to investment sales ratios.

Explore how implicit context shapes visuals in Power BI by examining dynamic measures, breakdowns, and filter logic in DAX, and learn methods to expand or shrink that context.

Power Query Editor cleans data before loading into Power BI, guiding you to transform tables, edit queries, and view applied steps via the data explorer and formula bar.

Learn how to clean data in Power Query by removing rows, including top, bottom, alternate, blank, and duplicated rows, to prep tables with notes or footnotes.

Import data from an Excel workbook, remove the top four rows in Power Query, and promote the first row as headers to fix the column names.

Learn to remove duplicated rows in a table by selecting the row column or multiple columns, using the remove duplicates feature, and preserving the first record.

Remove and rename columns in Power BI using the home tab and Power Query editor; delete the employee id column and rename the row column to position, then apply changes.

Learn to replace obscure compensation code values in Power Query by converting codes to readable text, using exact match options to preserve data integrity in Power BI.

Filter rows in Power Query using text and number filters to refine data, and convert the tenure in month column to a whole number for accurate comparisons.

Import credit card data via Power Query, remove top and bottom rows, promote the first row as header, and filter out deposit rows using the deposit column or transaction description.

Convert data types in Power Query, moving between text, whole numbers, decimal numbers, and dates, while learning to preserve decimal places, handle conversion steps, and manage warnings in the editor.

Identify common Power Query errors and learn fixed steps to resolve refresh issues, including file not found and invalid data type conversions, by adjusting sources and query steps.

Understand when to use Power Query vs DAX for data import, transformation, and calculations. Use the rule: reduce data size with Power Query, then do calculations with DAX after import.

Import the personal finance data into Power BI, remove top rows, promote the first row as header, and validate data types for a clean dashboard-ready dataset.

Translate business requests into Power BI dashboard requirements. Build a CityLife sales dashboard that shows total sales and withdrawals with regional breakdowns and top-performing advisors.

Import the insurance data into a Power BI workbook and load the Insurance Data tab to access advisor, sales, withdrawal, region, and product details. Net sales equal sales minus withdrawals.

Build a dynamic Power BI dashboard by creating current and past year sales, withdrawal, and net sales measures, then design visuals, filters, and regional/advisor breakdowns.

Format currency as dollars with zero decimals across the city dashboard cards. Enhance visuals with backgrounds, borders, alignment, and titles for the advisor details table and bar chart comparisons.

Explore why data models are essential in Power BI for combining data from different sources. Learn how relationships between tables improve storage efficiency and enable scalable dashboards.

Learn how relationships in Power BI data models act as filters, not joins, with organization and sales tables, showing cross-filter direction and how selections affect totals.

Master directional relationships in Power BI by learning how filters flow from the product table to the sales table, guided by the relationship direction shown in the model view.

Explore Power BI table relationships, including 1-to-1, 1-to-many, and many-to-many, by examining unique versus duplicate values in connected columns using product and sales examples.

Learn how active and inactive relationships affect filter propagation in Power BI, and how toggling them changes product and sales table interactions.

Learn to configure relationships in Power BI, building one-to-one, one-to-many, and many-to-many links, managing relationships, and handling active vs inactive paths and ambiguous paths.

Organize data as one big table to merge advisor, product, and sales information in a single view, enabling easy filtering while risking storage waste and limited scalability.

Explore organizing data with a star schema. Build dimension tables (product, advisor) and a central fact sales, linked by product ID and advisor ID for efficient storage and filtering.

Understand how the related function retrieves a value from a related table within a row context. Apply it in calculated columns in a one-to-many relationship between organization and sales tables.

Master the cross filter function in Power BI to adjust table relationships, explore active and inactive relationships, and configure direction to ensure correct filters across related tables.

Apply Power BI data modeling skills by connecting tables and fixing a dashboard in a star schema using advisor, product, and fact sales dimensions, ensuring filters produce distinct values.

Explore advanced Dax context management with the all function to expand calculation context, modify filters, and master its interaction with calculate, measures, and calculated columns for total sales.

Explore how the allexcept function removes all filters except the product column to compute total sales per product, expanding the calculation context beyond the current row.

Master remove filter to clear custom filters on specific columns in Power BI, using remove filter syntax to target columns like product rating and compare its effects across visuals.

Master the get filter context with the selected value function in Power BI, returning the unique value when only one exists and an alternative result when multiple values occur.

learn two methods to concatenate texts in dax: the concatenate function and the ampersand operator, including spaces for readability. use selected value to dynamically display product names in a breakdown.

Learn to split text in Power BI using left, find, and mid functions to dynamically extract first and last names, accounting for spaces and variable name lengths.

Apply the substitute function in Power BI to replace text by specifying old and new text and optional occurrences, for example replacing spaces with underscores in product descriptions.

Explore text transformation in Power BI with upper, lower, and trim functions; learn how trim removes leading-trailing spaces and compresses multiple spaces to a single space, using DAX techniques.

Explore how DAX variables improve calculation context by computing total sales per product using a variable to capture the product ID, enabling accurate filters without active relationships.