Microsoft Power BI for Business Intelligence & Career Growth

Power Query Editor enables data transformation and cleaning challenges in Power BI, including editing rows and columns, removing duplicates and blank rows, and applying steps with the M language.

Learn to transform dates in Power Query Editor for Power BI, extracting year, month, day, quarter, week, and computing age, delivery days, and bucket analyses from order and ship dates.

Explore text transformation in Power Query Editor by formatting text, trimming spaces, cleaning, and splitting or merging columns. Apply uppercase, lowercase, or proper case as needed.

Learn to merge tables in Power Query Editor by matching common fields, choosing left, inner, right, and outer joins, and expanding columns such as customer name and annual income.

Explore how to perform number transformations in Power Query Editor, including arithmetic, scientific, and trigonometric calculations, rounding, modulo, and is even/odd checks for data cleaning.

Create a conditional column in Power Query Editor to categorize delivery days as early, on time, or late, with multiple clauses and data type settings.

Append query in Power Query Editor to join data by rows from Excel and CSV files into a single table in Power BI.

Learn to reshape data in Power Query Editor by unpivoting month columns into rows and pivoting sales data to create flexible visuals in Power BI.

Use the group by feature in Power Query Editor to aggregate sales by ship mode in a Power BI workbook, creating a total sales column and supporting multiple grouping levels.

Insert a custom column in Power Query Editor to label each transaction as gain or loss using an if formula, creating a 'Profit status' column from the profit values.

Explore data modeling in Power BI by establishing relationships between tables, including transaction, customer, and product, using common fields like customer ID and product ID to build a solid model.

Learn to establish one-to-many relationships between six tables in a Power BI data model, linking transactions to customers, products, and territories using common keys.

Create a date dimension table in Power BI to provide a continuous date range and a unique date column for time intelligence across order, ship, and invoice dates.

Learn to create a matrix visual in Power BI that shows sales by product category and subcategory across customer segments, with drill down and formatting options.

Apply conditional formatting in Power BI tables to highlight sales by subcategory using background color, font color, data bars, and icons, and use web URL hyperlinks for interactive insight.

Learn to use funnel charts in Power BI to display items in a single sequential order using one sales variable, with conditional formatting and profit-aware tooltips.

Learn to compute sales as a percentage, create a monthly line chart with a date dimension, and apply visual filters for percentages against grand total, row total, or column total.

Power BI treemap chart visualizes regional and country sales in a hierarchical layout, using regions as groups and sales as values, with color by profit forming a heat map.

Learn how to use the ribbon chart in Power BI to compare two dimensions with a single measure, visualizing region performance over years using sales data.

Demonstrates how to create a waterfall chart in Power BI to display the running total of profit by time, using a date table, year filters, and exact date.

Explore how slicers filter a Power BI report and visuals using date slicers with relative month and year controls, plus multi-select dropdowns for category and region.

Explore symbol map and field map charts in Power BI to visualize country-level sales with symbol size and color, using tooltips, regional slicers, and lat/long for specific locations.

Learn to show specific locations in a Power BI map visual by using latitude and longitude in a table, with Times Square and Statue of Liberty as examples.

Explore how DAX calculations differentiate calculated columns and measures in Power BI, explaining row-level calculations versus aggregate totals with examples like 1 percent incentive.

Explore the difference between implicit and explicit measures in Power BI, and why explicit measures are reusable compared with implicit ones and calculated columns for scalable reports.

Master the if logical function in Power BI to return values based on logical tests, including nested ifs with and, or, and switch for calculated columns and incentives.

Explore the switch and iferror functions in Power BI's DAX, showing switch as an alternative to nested if and how iferror handles errors, with date table examples.

Explore text functions in Power BI, including left, right, mid, len, trim, and format, using DAX and M code to extract first and last names from a customer name.

Explore the DAX format function in Power BI to customize date and numeric values, create custom date formats, convert to text, and apply conditional thousand or million formats.

Master the DAX calculate function to evaluate expressions under custom filter contexts, compute total sales by region or category, and create percentage shares using explicit measures.

Explore the related function in Power BI, an alternative to look up, use with table relationships, and pull product name from products table into the transaction table for accurate calculations.

Explain how the DAX filter function returns a subset of a table, using filter for measure-based criteria, and compare with calculate using column filters, with incentive and total sales examples.

Explore the use relationship function to filter a transaction table by a secondary date dimension, enabling sales analysis by ship date alongside order date using active and inactive relationships.

Learn how the all function ignores filters to return all rows, enabling percentage of grand total and regional totals in Power BI visuals.

Explore iterator functions in Power BI, including SumX, CountX, MaxX, MinX, and AverageX, which evaluate an expression per row and aggregate into measures to compute incentives (1 percent of sales).

Explore Power BI time intelligence functions such as DATESMTD, DATESQTD, and DATESYTD to compute month-to-date, quarter-to-date, and year-to-date totals, with practical measures like total sales MTD and YTD.

Learn to apply DAX time intelligence functions—previous day, month, quarter, and year—to compare sales and compute growth percent in Power BI.

Explore same period last year and previous month DAX calculations in Power BI to compare sales, build dynamic slicer-driven analyses, and derive growth from period comparisons.

Compute the moving average of the last 20 days in Power BI using dates in period and averageX, with a dynamic interval parameter to adjust to 30 or 90 days.

Use the DAX dates between function to compute a running total of sales from 1 January 2015 to 31 December 2018 with calculate and last date in Power BI.

Explore using a scatter plot in Power BI to show sales versus profit, read correlation from minus one to plus one, and apply trend lines and clustering.

Learn to build a histogram in Power BI that displays the frequency distribution of delivery days, using bins and bin sizes, with a categorical x axis and data labels.

Apply forecasting in power bi using exponential smoothing on time-series data to predict future sales, with 95 percent confidence intervals and automatic seasonality detection.

Explore Power BI's ai visual: ask questions in natural language to generate visuals like top sales by country, maps, column charts, bars, tables, decomposition tree, and key influencers.

Explore how the key influencers visual in Power BI identifies factors influencing wine quality, using the WinQuality data set to analyze density, alcohol, sulfates, and citric acid.