Efficient data import - You will learn how to pull data from Excel, CSV, JSON, XML, folders, and online sources, so you can build a reliable input layer for your reports on your own.
Better data preparation - You will master data cleaning: removing unnecessary rows, fixing headers, setting correct data types, and handling duplicates and blanks to deliver consistent datasets for analysis.
Practical transformations - You will learn to split and merge columns, use pivot and unpivot, create calculated columns, and apply conditional rules to turn raw inputs into tables ready for reporting.
Combining multiple sources - You will learn how to join and append tables correctly, handle unmatched records, and prepare source structures so your data combinations work predictably and cleanly.
Reporting automation - You will build queries ready for recurring refreshes, use parameters and dynamic folders, and reduce manual fixes whenever new files or updated data arrive.
Faster, more stable queries - You will understand what affects Power Query performance, how to arrange steps in the right order, and how to diagnose issues that slow down refreshes or break data loads.
Confident use of M - You will understand let/in syntax and how steps are written in M, so you can edit Power Query code with confidence and handle transformations that the interface alone does not cover.
Report-ready query design - You will learn to design queries for further analysis in Excel or Power BI, so your datasets stay clear, consistent, and ready for trustworthy reporting and reuse.
As an ETL layer, Power Query organizes how data is acquired, shaped, and prepared for reporting. The training starts with its role in Excel and Power BI, the query editor interface, Applied Steps and preview mechanisms, and the principles of designing queries for later analytical use.
Power Query connects to files, folders, databases, and online sources, but each source type brings different import considerations. This section addresses loading Excel, CSV, TXT, JSON, and XML data, consolidating files from folders, and managing connection settings, credentials, and privacy levels.
Raw datasets often contain technical rows, inconsistent headers, empty records, and mixed data types that block further analysis. Here the focus is on removing unnecessary rows and columns, standardizing dates, numbers, and text, and cleaning duplicates, blanks, names, and dictionary values.
Column-level operations form the basis of most analytical preparation work in Power Query. Participants practice splitting and merging fields, extracting text, pivoting and unpivoting structures, grouping and aggregating data, and using filters, calculated columns, logical rules, and value mapping.
When data comes from multiple tables, the result depends on both the join logic and the source structure. The section covers Merge with left, right, inner, and full joins, Append for combining datasets, and ways to address missing matches or conflicting values before consolidation.
Building queries for repeated use requires more than a one-time refresh. This part focuses on manual and automatic updates, parameterized queries such as date ranges or file paths, and working with dynamic folders and changing file structures in cyclical reporting processes.
Slow refreshes usually result from step order, excessive source volumes, or poorly designed query flow. Participants work on limiting data as early as possible, diagnosing bottlenecks, and handling the most common refresh issues that affect stability in everyday reporting.
M language is presented as the logic behind query steps rather than a separate programming topic. The section explains let/in syntax, step naming, and code readability, then shows where generated M appears in Power Query and how to edit it safely for uncommon transformations.
Basic Excel skills - You should be comfortable working in Excel: opening files, moving across worksheets, filtering data, and understanding table structure, as many exercises will rely on that.
Working with data tables - You should understand columns, rows, headers, and common data quality issues so you can move smoothly through exercises focused on cleaning and organizing datasets.
Basic data analysis - You should have some basic experience with data analysis, such as sorting, filtering, and simple grouping, to understand the purpose behind the transformations more easily.
Initial Power Query exposure - It is worth having basic familiarity with Power Query or at least having seen the query editor, because this course focuses on intermediate work rather than a full introduction.
