Power BI and Power Query for Excel Users

Load ar data as a fact table by inspecting the r data excel file. Each record represents one invoice with date, customer, invoice number, due date, amount, and client code.

Load dClientCode as a dimension table to map client codes to names, then copy it into the main Excel model alongside the R data file.

Add a client name field to the main table with color-coding. Use xlookup to match the client code in the dimension table and return the value, blank if no match.

Create a calculated column, days overdue, to compute aging buckets by subtracting the invoice due date from the report date, highlight it, and verify with a 16-day overdue example.

Create the first pivot table report from the AR data set, sum invoice amounts by location in currency, sort high to low, and chart total amount due by location.

Create the average days overdue report by swapping in days overdue, set the pivot to average with one decimal, and build a bar chart, then tidy the legend and title.

Create a pivot table report showing total due by aging buckets, grouping days overdue into bins such as 0-30 and 31-61, with invoice amounts formatted as currency.

Create a slicer in the 3rd pivot table report to let end users filter by client type and see the amount due by aging bucket.

Identify where Excel falls short consolidating multiple location files and see how Power Query in Power BI automates stacking aging detail data across locations.

Explore the file set for the project, including the client dimension table and location-based R data, to locate invoices, due dates, amounts, and customer details.

Use Power Query to transform and stack ten location files into a single table, then load the result into the data model via PowerPivot for analysis.

Load the Dhclient code dimension table into data model by connecting to the Excel file via Power Query and adding it to the model for review in PowerPivot diagram view.

Create relationships between a fact table and a dimension table using a common client code to form a relational data model, enabling cross-table pivot reports with one-to-many notation.

Create aging bins by adding a custom column in Power Pivot to compute days outstanding from report date and due date, then classify into 0–30 and over 30 bins.

Model two related tables as a single entity, create a pivot table report for total amount by business location, format currency, sort high to low, and insert a bar chart.

Create the second pivot table report showing average days outstanding by location, copy the first pivot, switch to the average days outstanding with one decimal, and add a bar chart.

Create a final pivot report of invoice amounts by aging bucket using the shared data model, and add a client name slicer for interactive filtering.

Review a multi-location R data folder with invoices and a fact table; map client codes to names with a dimension (definitional) table, while fact data grows and dimensions stay static.

Load AR data into a power bi model by importing all files from a folder, stacking them into one table with power query, then closing and applying to data model.

Load a dClientCode dimension table from an Excel workbook in Power BI, using get data, transform in the Power Query editor, and close and apply to verify the loaded data.

Examine the model and create relationships between two tables using the common client code column, forming a one-to-many relationship and establishing filter direction for use as a single entity.

Create a calculated column days outstanding in Power BI by subtracting the invoice due date from report date, set to decimal, and add it to the model for aging analysis.

Create your first Power BI report by building a matrix to show total amount outstanding by business location, then add a bar chart of the same data with currency formatting.

Create the second report to show average days outstanding by business location using a matrix, swapping the default sum to average, and visualize with a bar chart.

Create a final Power BI report showing outstanding invoice amounts by aging bins. Convert to a bar chart and add a client slicer with a select all option.

Create a monthly wage variance dashboard in Power BI, comparing actual versus budget, with location and department slicers, and preview the Excel and Power Query versions.

Build a Power BI and Power Query workflow by using a budget table as the base, creating calculated fields, modeling location and department, and delivering final reports with slicers.

Download the section's file set, then load, cleanse, and model data in Power BI and Power Query for reporting; focus on solid data foundations rather than flashy visuals.

Understand two fact tables: actual wages daily and budget wages monthly, and two dimension tables for department and location, highlighting narrow and tall transactional data and lookup relationships.

Create a master Excel live workbook and copy wages daily, budget monthly, D department, and D location into it, yielding a four-tab model and removing sheet one.

Create calculated columns from the budget monthly basis to show budget, actual daily wages, and variance by month, location, and department, using xlookup for names and sumifs for wages.

Create the calculated column period m to normalize january dates, pull actual wages with a sumifs by location, department, and period, and compute budget-to-actual variance for final reporting.

Create pivot table from budget data, group by months and years, and compare actual wages to budgeted amount with a line chart using location name and department name slicers.

See why Excel falls short when budget data is spread across location files, and how Power Query provides a programmatic, scalable consolidation approach for future Power BI and Excel projects.

Review the file set for this project in Power BI and Power Query workflows, including seven location budgets, the daily wages fact table, and the department and location lookup tables.

Load files from a folder into Power Query, stack and combine seven files, and load only as a connection to the data model.

Link fact and dimension tables in PowerPivot by creating one-to-many relationships, then add a calendar table to unify time across data for a single pivot table.

Create a DAX measure to compute the variance between budgeted wages and actual wages, enabling month-by-month analysis in Power BI and Power Query for Excel users.

Create a pivot table from the Power BI data model to compare budgeted and actual wages, slice by location and department, and refresh automatically as new data arrives.

Explore the project files for a Power BI and Power Query workflow, reviewing budget and actual wages across seven locations using fact and dimension tables with lookups.

Load the data into the Power BI model by creating a pbix file, using get data from a folder, and transforming to combine seven budget files into one stacked table.

Delete auto-created relationships, then link the location and department dimensions to their respective fact tables, creating one-to-many relationships and a calendar table using DAX calendar auto.

Create a DAX variance measure to show a monthly time series of budgeted wages minus actual wages, with department and location filters, recomputing in each report context.

Create a Power BI report with a matrix showing year and month, budget vs actual wages, and the variance measure, plus slicers for location and department from common dimension table.

Analyze monthly employee and terminations data for a rental apartment holding company, and visualize turnover by area, community, and position using Power BI and Power Query.

Develop and model data in Power BI and Power Query using employee counts, terminations by period, and a community dimension; build calculated fields and turnover rate measures with DAX.

Set expectations to load and cleanse data, build the model, and connect the report to it in Power BI, following along with the provided files; visuals are not the focus.

Explore a data model with one dimension table and two fact tables, linking communities to areas via lookup, and analyze an employee count and terminations by position, community, and period.

Load data into a new blank Excel workbook named model live by copying the community, employee accounts, and terms sheets into a master model workbook with three tabs.

Create calculated columns by adding area and term counts to the main employee accounts table using Xlookup for lookups and Sumifs for conditional sums across the terms table.

Relate monthly employee counts to terminations by building a pivot table report with slicers for community, area, and position, then visualize trends using a combo chart with separate axes.

Deploy a turnover percent measure in Power Query and Power BI to overcome Excel limitations, enabling dynamic visuals and slicing across positions and communities.