Faster analysis in Excel - You will organize the way you work in Excel and build reliable spreadsheets more efficiently. With named ranges, references and better formulas, you will reduce errors in daily analysis.
More reliable pivot reporting - You will learn how to create and modify pivot tables and pivot charts to summarize large datasets faster, filter results with confidence and present findings in a clear, useful format.
Combining multiple data sources - After the course, you will be able to connect data from Excel files, CSV, TXT and databases on your own. This will help you merge scattered information into one consistent source for analysis.
Data modeling with Power Pivot - You will build a relationship-based data model and work with larger datasets than a regular worksheet can handle. This will let you create analyses that are more robust and easier to expand.
Practical DAX skills - You will master calculated columns, measures and DAX functions, including CALCULATE and time intelligence. This will help you build KPI logic and period comparisons without manual workarounds.
Clearer reports in Power BI - You will learn how to choose the right visuals, design interactions and publish reports in Power BI. As a result, you will present data in a form that stakeholders understand and use faster.
Stronger SQL Server querying - You will write SQL queries on your own using SELECT, WHERE, JOIN, GROUP BY and HAVING. This gives you more independence when retrieving data and verifying information quality at the source.
A broader BI perspective - You will understand how to connect Excel, Power Query, Power Pivot, Power BI and SQL Server into one analytical workflow. This will help you move from raw data to final reporting more smoothly.
Excel is treated as an analytical environment that combines formulas, structured references, worksheet cooperation, and database-style operations on data. The section covers function optimization, nested formulas, sorting, filtering, consolidation, external data work, and pivot tables for summarizing results.
Multidimensional analysis is introduced through the Excel ecosystem and the role of PowerPivot in extending standard spreadsheet reporting. The section connects the benefits of model-based analysis with related tools such as Power View, Power Query, and Power BI, including KPI-oriented views.
Data models begin with controlled import from workbooks, text files, CSV files, and databases into a consistent analytical structure. The section concentrates on how source data is brought into the model so later calculations, reporting, and multidimensional analysis can rely on stable foundations.
With Power Query, the emphasis is on connecting to varied data sources and shaping imported datasets before they enter the model. Participants become familiar with the interface, key terminology, transformation steps, and the practical implications of where processed data is stored.
PowerPivot serves here as the central modeling layer for loading data, refreshing connections, and defining analytical logic inside Excel. The work includes calculated columns, measures, DAX expressions, CALCULATE examples, time intelligence, and the use of hierarchies or hidden columns in model design.
DAX models extend analytical structures with calculated columns, explicit and implicit fields, and measures tailored to reporting needs. The section also addresses model corrections as well as the role of dimensions and hierarchies in organizing calculations and navigating aggregated data.
Reports based on PowerPivot models use pivot tables and pivot charts to analyze structured data from multiple perspectives. Attention is given to the differences between standard pivots and PowerPivot-based reports, along with filters, slicers, and timeline controls that refine analytical navigation.
Exporting tables from the PowerPivot model addresses situations where modeled data must be reused outside the analytical layer. The work concerns moving selected model content into tabular form so it can support further operations, sharing, or downstream reporting tasks in Excel-based workflows.
Key Performance Indicators organize measures around targets, thresholds, and status interpretation so results can be assessed consistently. This section focuses on building KPI views that connect calculated values with business evaluation logic and make deviations easier to identify in reporting.
Creating reports in Power View requires a model prepared for visual consumption, not only for storage or calculation. Participants learn how to optimize data models and then build tables, matrices, cards, filters, and charts that support clear navigation and structured report reading.
Business intelligence is framed here as a way to turn operational data into usable analytical views for decision-making. The section introduces core BI concepts, principles of effective data visualization, and basic analysis using maps and tabular views suited to different information contexts.
Working with data in Query Editor focuses on connecting Power BI to Excel files, database sources, and the Power BI service. The section shows how data is brought into the reporting environment and prepared for further modeling and analysis across different source types.
Choosing the right Power BI visual depends on the analytical question, the data structure, and the intended user interaction. Participants work with chart interactions, custom visuals, dashboards, report publishing, and the practical aspects of managing Power BI solutions after deployment.
MS SQL Server is presented from both the platform and analyst perspective, including server architecture, database design patterns, and the logic of relational models. The section also covers SQL querying, joins, aggregation, subqueries, stored procedures, and core DDL and DML operations in Management Studio.
Basic Excel skills - You should move around worksheets comfortably, edit cells, use simple formulas and save files, so you can focus on analysis instead of learning the basics of the application.
Working with tables and data - You should be able to read tables, recognize rows and columns, and perform simple data operations, because the course develops these skills at a more advanced level.
Basic analytical logic - You should understand relationships between data, filtering and grouping results, so it is easier for you to move into data models, measures, KPI logic and reporting.
Readiness for SQL work - You should be ready to work with technical query syntax and data structures, even if you do not know SQL yet, because the course includes hands-on database operations.
