This hands-on, online instructor-led course extends the foundational knowledge from our introductory-level courses, including PL-300: Microsoft Power BI Data Analyst and Excel BI Tools: Power BI for Excel Users. Participants will explore common intermediate-level tasks and discover some of Power BI's most valuable advanced features for data analysis and visualization.
Prerequisites:
Students should have the general knowledge equivalent to what is covered in PL-300: Microsoft Power BI Data Analyst or Excel BI Tools: Power BI for Excel Users before attending this course.
Course Objectives
Upon completion, students will be able to:
- Import data from diverse sources, including PDFs, web page regions, and collections of files
- Characterize and profile data to identify data quality issues and patterns
- Merge mismatched datasets using fuzzy matching techniques
- Generate and customize columns in Power Query for enhanced data transformation
- Perform advanced data modeling operations and establish complex relationships
- Apply Power BI time intelligence functions for temporal analysis
- Integrate custom scripts written in R and Python for advanced analytics
- Create KPIs and scorecards for business performance monitoring
- Implement advanced report design techniques for professional presentation
- Develop advanced dashboard design strategies for interactive analysis
- Conduct basic statistical analysis directly within Power BI
Course Outline
Module 1: Intermediate Power Query
- Importing data from PDFs and extracting structured content
- Finding and extracting data from web pages
- Retrieving tabular data from various sources
- Using "Get Data by Example" for intuitive data loading
- Importing the complete contents of folders for batch processing
- Using fuzzy matching algorithms to combine disparate datasets
- Creating custom columns for derived calculations in Power Query
- Common mathematical and string operations for data manipulation
- Writing and editing M language scripts for advanced transformations
- Leveraging columns by example for quick column generation
Module 2: Intermediate Data Modeling
- Adding What-If parameters for scenario analysis and planning
- Grouping and binning data to create categorical variables
- Using time intelligence functions for date-based analysis
- Generating DAX formulas efficiently with Quick Measures
Module 3: Script Visuals
- Creating R script visuals for statistical graphics and analysis
- Installing and configuring an R environment for Power BI integration
- Creating Python script visuals for custom visualizations
- Installing and configuring a Python environment for Power BI
Module 4: Advanced Report Design
- Applying and customizing report themes for consistent branding
- Creating custom themes to match organizational standards
- Conditional formatting in tables and matrices for visual emphasis
- Implementing drill-through functionality for detailed exploration
- Adding data-driven images that change based on context
Module 5: Advanced Dashboard Design
- Applying dashboard themes for unified visual experience
- Using the KPI visual for key metric tracking
- Implementing the Multi KPI visual for comparative analysis
- Adding KPIs and trend analysis using DAX calculations
- Strategies for incorporating KPIs effectively in tables and matrices
- Importing and integrating Excel data models into Power BI
- Conditional formatting for visual data exploration
- Using the DAX UNICHAR function for special symbols and indicators
- Embedding images directly within dashboards
Module 6: Analytics
- Characterizing datasets to understand underlying distributions and patterns
- Revisiting the data profiler for quality assessment and anomaly detection
- Leveraging custom visuals for specialized analytical needs
- Calculating moving averages for trend identification
- Applying ARIMA models for time series forecasting
- Performing linear regression analysis using R scripts
- Conducting time series forecasting with Python for predictive analytics
