This module covers the full round trip between a spreadsheet and Power BI. You will connect Power BI Desktop to an Excel workbook, preview the contents, and load a chosen table as a working dataset, then export data from a report back out to Excel when someone needs it in that form. Along the way you will learn what actually happens when a connection is made — the difference between importing a copy of the data and querying it live, and how that single choice affects speed, file size and how current the figures are. You will also learn to inspect a raw workbook for the structural problems that quietly break an import, so that you spot them before they become puzzling errors later.

This module is about getting raw data into a shape you can actually report on, and doing it in a way you never have to repeat. Working in the Power Query Editor, you will promote headers, remove unwanted rows and columns, set data types, trim stray values, and combine tables by merging them side by side or appending one beneath another. Because every change is recorded as an instruction rather than an outcome, the same cleanup runs automatically against next month's data — and you will refresh a report and watch the steps rerun against updated figures. You will also learn why the order of those steps matters, since a sequence that works today can fail on the next file if it was assembled carelessly.

This module covers the two things that turn a collection of tables into a model that answers questions correctly. First, structure: you will learn to tell the table holding the events you count from the tables that merely describe them, connect them properly in the Model view, and understand why linking everything to everything produces totals nobody can explain. Second, calculation: you will write DAX formulas for figures that exist nowhere in your columns, such as a margin, a running total, or a share of the whole. You will also learn the distinction that causes more confusion than any other in DAX — when a calculation belongs in a stored column and when it belongs in a measure that recalculates as the reader explores.

This module is about the visual that answers a reader's first question before they have properly focused on the screen: are we where we should be? You will format numeric fields so figures are readable and comparable, then build a KPI visual that shows a current value against a target and indicates which way it is moving. Just as importantly, you will learn to choose what the number should be — turning a broad ambition such as improving performance into two or three indicators that someone can actually act on, each with a target you can defend rather than one that was simply invented. You will also learn to recognise the measures that look impressive and change nothing.

This module covers building a report page that carries more than one message and lets a reader move between views. You will assemble a page, build a combo chart that shows two measures together so their relationship is visible, and save specific arrangements of filters and selections as bookmarks that anyone can return to in a single click. You will also look at how much the choice of chart shapes the conclusion a reader draws — the same figures can be arranged to emphasise a total, a comparison between categories, or a single standout series. That leads into the particular care a dual-axis chart deserves, since the way each axis is scaled can suggest a relationship that is not really there.

This module covers making a report look deliberate rather than assembled, and doing it in one place instead of visual by visual. You will lay out and decorate a report page, then apply a custom theme so that colours, fonts and default formatting stay consistent across every page automatically. You will learn what a theme file governs and how to adjust one, along with the practical colour rules that keep a report readable: how many colours a chart can carry before a legend stops working, why red and green fail for a substantial number of readers, and how to check that text survives its background. Underlying all of it is a simple point — inconsistent formatting costs the reader effort that should have gone into understanding the numbers.

This module covers what to do in the few minutes between data arriving and the first chart being built — a stage that is easy to skip and expensive to skip. You will use natural-language questions to interrogate a dataset without designing anything, examine distinct values, ranges, gaps and outliers, and apply analytics lines and automated insights to see what stands out. You will also learn to judge whether something that stands out actually means anything, since most movement in any measure is ordinary variation. Seasonality, small denominators and periods that have not finished yet explain a great many findings that would otherwise be reported as news.

This module shows how a project timeline is produced in Power BI without any specialised chart — a matrix visual with tasks down the side and months across the top, coloured so that each task's bar spans the period it occupies. You will see how the pieces fit together: a separate date table so that every month appears whether or not anything happens in it, a status calculation that answers what is happening for each task in each month, and conditional formatting that turns that answer into colour. You will also learn what a task table must contain before any of this is possible, since most difficulties with timelines come from the data rather than the visual — missing end dates, progress recorded as words, or a single task spread across several rows.