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Course Outline
Data Acquisition and Transformation
Introduction to Power BI
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Power BI Ecosystem
- Power BI Desktop – the primary authoring tool for creating reports and data models
- Power BI Service – cloud platform for publishing, sharing, and collaboration
- Power BI Mobile – accessing reports and dashboards on iOS and Android devices
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Power BI Desktop Interface
- Report, Data, and Model views – understanding each workspace
- Navigation panes, ribbon, and canvas organization
Getting Data
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Importing data from various sources:
- Microsoft Excel workbooks and ranges
- CSV and text files with custom delimiters
- SQL Server and other relational databases
- Web sources and APIs
- SharePoint lists and document libraries
- Importing multiple files from a folder – automating data consolidation
- Parameters and dynamic data sources – building flexible, reusable queries
Power Query - Basic Transformations
- Filtering rows and sorting data to focus on relevant information
- Removing unnecessary columns and adding new calculated columns
- Changing data types and handling regional format differences
- Splitting columns by delimiter and merging columns into combined fields
- Handling errors and null values – strategies for clean data
Power Query - Advanced Transformations
- Unpivot and Pivot operations – reshaping data for analysis
- Grouping and aggregations – summarizing data at different levels
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Query operations for combining data:
- Merge queries – joining tables based on matching columns
- Append queries – stacking tables with similar structure
- Understanding join types: inner, left, right, full outer, anti
- Fuzzy matching – joining data with approximate matches
- Conditional columns – applying business logic to create new fields
- Custom columns using M language expressions
- Working with numeric, date/time, and text data transformations
Data Modeling and DAX
Data Modeling
- Star Schema – theory and practical implementation for optimal performance
- Fact tables vs. dimension tables – understanding their roles in the model
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Creating and managing relationships between tables:
- One-to-many and many-to-many relationship types
- Cross-filter direction – single vs. bidirectional filtering
- Cardinality settings and their impact on calculations
- Active and inactive relationships – handling multiple relationships between tables
DAX - Fundamentals
- Calculated columns vs. measures – when to use each approach
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Aggregation functions for summarizing data:
- SUM, AVERAGE, COUNT – basic aggregations
- MIN, MAX, DISTINCTCOUNT – finding extremes and unique values
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Logical functions for conditional calculations:
- IF and SWITCH – branching logic in formulas
- AND, OR – combining multiple conditions
- Text functions – manipulating and formatting text values
- Date functions – extracting and calculating with date components
DAX - Context and CALCULATE
- Row Context vs. Filter Context – the foundation of DAX evaluation
- CALCULATE – the most powerful function for modifying filter context
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Filter functions for advanced analysis:
- FILTER – creating custom row-level filters
- ALL, ALLEXCEPT – removing filters for calculations like percentages
- REMOVEFILTERS – selectively clearing filters
- Iterator functions (SUMX, AVERAGEX, COUNTX) – row-by-row calculations
DAX - Time Intelligence
- Date table – creating a proper calendar table and marking it for time intelligence
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Time analysis functions for comparing periods:
- YTD, QTD, MTD – year, quarter, and month-to-date calculations
- SAMEPERIODLASTYEAR – comparing to the same period in the previous year
- DATEADD – shifting dates by a specified interval
- PARALLELPERIOD – referencing parallel time periods
- Period comparisons – calculating growth rates and dynamic time-based KPIs
Visualization, Analytics, and Deployment
Visualizations and Report Design
- Choosing the right chart type – matching visuals to data and message
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Core visuals configuration and best practices:
- Bar and column charts for comparisons
- Line charts for trends over time
- Pie and donut charts for part-to-whole relationships
- Scatter plots for correlation analysis
- Tables, matrices, and cards – displaying detailed and summary data
- Geographic visualizations – maps and filled maps for location-based insights
- Conditional formatting – using colors, icons, and data bars to highlight patterns
- Interactions between visuals – configuring cross-filtering and cross-highlighting
- Tooltips and Drillthrough – providing additional context and detailed views
- Bookmarks and buttons – creating interactive navigation and storytelling
- Report accessibility – ensuring reports are usable by all audiences
Analytics Features
- Analytics pane – adding trend lines, forecast lines, and reference lines
- Decomposition tree – breaking down metrics to identify root causes
- Key influencers visual – using AI to discover factors affecting outcomes
- Smart narratives – generating automated text summaries of data
- Anomaly detection – automatically identifying unusual patterns in time series
Custom Visuals
- Exploring the AppSource marketplace – finding specialized visualizations
- Importing and configuring custom visuals for specific use cases
Power BI Service
- Publishing reports from Desktop to the cloud
- Workspaces – organizing content for teams and projects
- Dashboards vs. Reports – understanding when to use each
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Data refresh configuration:
- Scheduled refresh – automating data updates
- Gateway setup – connecting to on-premises data sources
- Sharing and permissions – controlling access to reports and data
- Row-Level Security (RLS) – restricting data access based on user roles
Hands-on Project
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Building a complete analytics solution from provided business data
- Connecting to multiple data sources and transforming raw data
- Creating an optimized data model with proper relationships
- Developing an interactive dashboard with key performance indicators
- Implementing analytics features for deeper insights
- Publishing to Power BI Service and configuring security
Benefits for Participants
Upon completion, participants will be able to:
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Connect to various data sources and transform raw data into analysis-ready formats
- Build efficient data models using star schema principles
- Write DAX measures for complex calculations and time intelligence
- Design interactive, visually appealing reports and dashboards
- Leverage AI-powered analytics features for deeper insights
- Publish, share, and secure reports in the Power BI Service
Benefits for Organizations
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Improved data-driven decision making across teams
- Reduced dependency on IT for routine reporting needs
- Standardized, professional data visualization practices
- Better utilization of existing data assets
- Enhanced collaboration through shared analytics platforms
Requirements
- Working knowledge of Microsoft Excel (tables, charts, basic formulas)
- Understanding of basic data concepts (tables, rows, columns)
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No prior Power BI experience required
This training is designed for: - Business professionals who need to analyze and present data effectively
- Excel users looking to transition to more powerful analytics tools
- Report creators who want to build interactive dashboards
- Analysts responsible for data-driven decision making in their organization
21 Hours
Testimonials (2)
The subject was taught in an engaging way, making me want to learn more.
kgotla Moncho - Martin Engineering Africa
Course - PL-300T00: Microsoft Power BI Data Analyst
The way it was conducted.
Natalia Kaleta-Pajewska - Solutions30 Telecom Sp. z o.o.
Course - Advanced Power BI
Machine Translated
