Confident Power BI work - You will learn to navigate Power BI Desktop with confidence, load data, and build reports from scratch, so you can prepare useful analyses faster in your everyday work.
Better database understanding - You will organize key concepts related to databases, tables, and views, making it easier for you to connect Power BI to SQL and work more confidently with structured data.
Well-built data models - You will learn how to load source data, create measures, and validate a data model, so your reports stay consistent, readable, and ready for further business analysis.
Practical DAX skills - You will master creating calculated fields and columns in DAX, which will let you build your own indicators, define measures, and extend the logic behind your reports.
Effective data shaping - You will learn to filter, sort, group, aggregate, split, and merge data in Power Query, allowing you to prepare analysis-ready datasets without manual spreadsheet work.
Clear reports and sharing - You will learn how to choose charts, tables, KPIs, and slicers, and how to export to PDF, publish to the cloud, and embed reports in SharePoint for easy sharing.
Independent SQL querying - You will practice SELECT statements, operators, text and numeric functions, date operations, and sorting, so you can prepare better data before loading it into Power BI.
Table joins and aggregation - You will understand joins, GROUP BY, and aggregate functions, so you can combine data from multiple tables, create summaries, and improve how your queries perform.
Power BI Desktop is introduced through its core interface, typical use cases, and analytical capabilities. Alongside the application basics, the section establishes essential database terminology so participants can place later SQL and modeling tasks in the right technical context.
A sound model starts with loading source data and checking whether its structure supports the intended analysis. Participants review how calculations and measures fit into the model, then verify the setup so later reporting and DAX work rest on consistent foundations.
Calculated fields and calculated columns serve different purposes inside a Power BI data model. This part shows how DAX is applied to create both elements, with emphasis on selecting the appropriate calculation method for model logic and reusable analytical outputs.
Working on rows and columns in Power BI involves both structural changes and analytical preparation. The section includes changing data types, sorting, grouping, filtering, aggregation, and splitting or joining data so that source content is fit for later modeling and visuals.
Choosing the right visual form determines how clearly data can be interpreted by end users. The material compares tables and charts, works with line, pie, column, and bar visuals, and adds KPI elements plus slicers to support focused, interactive report consumption.
Reports are only useful when they can be delivered in a form suited to the audience and platform. This section addresses exporting to PDF, publishing reports in the cloud, and embedding content on a SharePoint site while preserving access to the prepared analytical output.
After data is loaded, it must be shaped in a way that supports reliable analysis inside Power BI. This part connects transformation, modeling, and calculation steps into one processing flow, showing how raw input becomes a structured dataset ready for reporting work.
SQL databases provide the source structure that Power BI connects to and analyzes. The section outlines the main divisions of SQL, the role of the code editor, key server objects such as tables and views, and the practical setup of a database connection from Power BI.
In Power Query, preparation happens before modeling and visualization begin. Participants sort and filter records, manage data types, create calculated columns, and split or merge fields so that imported datasets are consistent, usable, and aligned with reporting requirements.
DAX extends the analytical layer of Power BI beyond source-level transformations. This section covers syntax, measures, functions, AutoSum behavior, and calculated columns, with attention to how formulas are written and used to return business metrics inside the model.
Writing direct SQL queries begins with data types and the structure of the SELECT statement. The material connects comparison operators, IN, BETWEEN AND, LIKE, date handling, calculated columns, and result sorting to build precise extracts for analysis in Power BI.
SQL functions are used here to reshape text and numeric values before they enter the model. The section works with LEFT, RIGHT, REPLACE, UPPER, LOWER, CONCAT, and ROUND to standardize fields, combine content, and prepare values for further analysis and presentation.
Business reporting often starts with reducing detailed records to meaningful summaries. Here the focus is on GROUP BY with COUNT, MIN, MAX, SUM, and AVG, showing how aggregate functions support comparison, consolidation, and interpretation of data sets at query level.
Combining data from multiple tables requires choosing the right join for the analytical task. The section explains how INNER JOIN, LEFT, RIGHT, FULL OUTER, and CROSS JOIN affect row matching, completeness of results, and the structure of datasets used in reporting.
When a result depends on intermediate logic, nested queries help separate filtering, comparison, and derived selections. Participants work with subqueries in practical SQL scenarios and see how layered query design supports clearer retrieval logic before data reaches Power BI.
Query performance becomes critical when Power BI relies on database results at scale. This section focuses on shaping efficient SQL, limiting unnecessary reads, and interpreting how query structure affects execution time, server load, and responsiveness of downstream reports.
Computer literacy - You should be comfortable working in Windows, launching applications, saving files, and navigating folders so you can focus on data analysis instead of basic computer tasks.
Basic data handling - You should understand what rows, columns, and tables are and be able to read a simple dataset, so transforming and analyzing data during the course will be easier for you.
Excel basics - You should know basic Excel tasks such as filtering, sorting, and using simple formulas, because this will help you understand reporting logic and day-to-day data work.
Analytical basics - You should be able to interpret simple metrics, sums, and averages, so you can create measures more smoothly, read SQL query results, and build meaningful visualizations.
